C-QSAR
A General Approach to the Organization of
Quantitative Structure-Activity Relationships
in Chemistry and Biology
Winter, 2000

BioByte Corp.
201 West 4th St, Suite 204
Claremont, CA 91711
Phone number: (909) 624-5992
FAX Number: (909) 624-1398
E-Mail: clogp@biobyte.com

Preface to the 4 th Edition of C-QSAR

The C-QSAR database is growing nicely. Soon we will have three full-time researchers
entering data instead of two and the pace will increase to the point where we should be able to keep
up with the current rate of publication. We now have over 14,000 QSAR. The biggest change inthis edition is in section VI: Searching For New Leads.

CH

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Table of Contents
I.

Introduction...............................................................................................5II.

Organization of QSAR Database.....................................................................15

III. Using the Databases...................................................................................18
A. Help ...............................................................................................18
B. String Searching...................................................................................19
C. Physical Database..................................................................................20
1. Main Menu......................................................................................20
2. Searching and Show...........................................................................20
3. Browsing........................................................................................29
4. Statistics.........................................................................................29
5. Omitted Data Points............................................................................30
6. SMILES.........................................................................................31
7. Loading from 'Database Search' to 'Workspace'. .........................................32
D. Biological Database................................................................................33
1. Browsing........................................................................................33
2. Searching........................................................................................34
3. Comparing New QSAR .......................................................................36
4. Log PO (Optimal Log P).......................................................................38
IV. Searching the Parameter (THOR-Sigma) Database................................................40
A. Substituent Constants.............................................................................40
B. Log P and MR .....................................................................................41
V.

Regression Analysis: Example 1.....................................................................42
A. Title Information...................................................................................43
B. Naming Parameters................................................................................43
C. Naming and Entering Substituents ..............................................................43
D. Entering Structures via SMILES.................................................................443

E.Auto-Loading of Parameters.......................................................................45
F.Permuting............................................................................................47
G. Jackknifing.........................................................................................50
H. Cross Validation...................................................................................53
I. Editing ..............................................................................................54
J. Regression Analysis: Example 2.................................................................54
K. Substituent Selection in Molecular Design......................................................58
VI.

Searching for New Leads............................................................................60

VII. SMILES Tutorial......................................................................................66
VIII. Substructure Searching...............................................................................70
IX. Searching Combined Databases.....................................................................72
X.

Caveats ...............................................................................................76

XI. Appendix: Parameter Definition.....................................................................81
XII. Notes on the Use of Substituent Parameters.......................................................85
XIII.QSAR and Combinatorial Synthesis................................................................87
XII. References .............................................................................................894

I. Introduction
The reader is cautioned that the subject embraced by this manual is not something to leaf
through on a rainy Sunday afternoon. Over 60 years ago, one of the authors was told by his
chemistry professor that organic chemistry was too complex for the human mind. It has become
vastly more complex since then! Of course, it is quite simple compared to biology. We are taking
some beginning steps to mechanistically merge the two subjects. Anyone who licenses the system
will be given several days free instruction in Claremont plus assistance later via telephone.
Structure-activity relationships have been at the heart of chemistry since the work of
Mendeleyev in the 19th century. However, it was the work of Hammett in the mid 1930's that
opened the way for QSAR in organic and eventually in biological chemistry. Since that time anenormous amount of work has been done. Our current system contains over 14,000 sets of data
and the corresponding QSAR. Of these 6,300 are biological QSAR and 7,700 are from physicalorganic chemistry. These come from the many, many different sub-disciplines of physical organicand biological chemistries. An important purpose of this system is to facilitate comparative QSAR
from as many points of view as possible.1-10
The purpose of our dynamic, integrated electronic system is much more than a compilation of
QSAR from all areas of chemistry and chemical biology. We view it as laying the foundation for ascience of chemical-biological interactions; that is, a science which undertakes to understand how
any (or all) chemicals affect all living systems or parts thereof (see Table 1). Obviously, such ascience will be decades in maturing, but we have reached the point where the subject can be taken
seriously. In so far as possible we are relating the QSAR of classes in Table 2 to those of Table 3.
That is, by science we mean mathematical relationships that can be compared with each other inmany ways. Obviously qualitative knowledge has been accumulating for some time.
The field of QSAR has become so large that it is no longer possible to write a review of say,
14,000 equations plus the attendant chemical structures and data. To make matters worse, theresults of quantitative chemical and biological studies appear in an incredible number of journals.

5These studies are very poorly indexed by Chemical Abstracts. Without a continually updated
electronic system, it is impossible to profit from past experience.
A truly major problem in advancing the science of QSAR is the split between those hoping toformulate QSAR (by whatever method) and those synthesizing variations in a lead compound.
Unless good variation in the major properties (steric, electronic, hydrophobic) is achieved, it is ofcourse impossible to establish the importance (or lack of it) for any given property. It is clear fromthe perusal of the Journal of Medicinal Chemistry, for example, that most papers give little or nothought to this important problem. Along with this is the matter of collinearity. If, in a set ofderivatives, two parameters are almost parallel no decision can be made as to which is mostimportant. The above problems need to be considered early on in a synthetic program. An
important feature that has been recently added to our system is that of substituent selection (section
V-K). Here it is shown how to select a set of substituents for the early phases of lead modificationthat will have all of the common parameters for all members of the set with the widest choice of
substituent properties. Also at the start of the study collinearity can be minimized.
We started to seriously collect data about 1965. In 1970, David Elkins wrote our first programfor searching the data, but it was cumbersome to use since structures were encoded in the
Wisswesser Line Notation and the need to use IBM cards made it slow and inconvenient. Thus thecurrent C-QSAR program is the outgrowth of over 35 years of research. It was designed andwritten by David Hoekman, and it employs the widely-used SMILES notation (invented by David
Weininger) for structure entry. It also uses his Merlin searching program. Table 1 outlines thegeneral organization of the system and tables 2 and 3 show the areas from which the QSAR come.
As of the present, QSAR development is booming with so many new ideas, often based oncomplex calculations, that it is virtually impossible for anyone to make critical evaluations of theirrelative merits. An important aspect of our data is that it provides many examples from all sorts ofbiological and mechanistic organic chemistry that can be employed for comparative purposes.
Moreover, the QSAR are formed from traditional parameters whose intended meaning is easily
understood.

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C-QSAR does have merit for lead generation (section VI). The majority of Bio QSAR in ourcurrent system are from articles where the authors made no attempt to derive a QSAR of any sort.
No doubt in many instances activities could be increased by means of QSAR. Possibly the best
example of this approach is Koga's demonstration that the rather mediocre drug, nalidixic acid,could be transformed with the help of QSAR to what has become the fabulous quinolone
carboxylates.11 Other examples of QSAR success in the development of products of industrial
importance, have been discussed by Fujita.12, 13 Another way of searching for leads is to look forsets with large log 1/C values. C is the molar concentration producing a standard biological effect.
At the moment, we have 704 sets that contain compounds with log 1/C > 8 (i.e., 10 -8 molar) and
1,327 with log 1/C > 7.
At present there is nothing comparable to our large and growing database to guide thedevelopment of the science of chemical-biological interactions. Single isolated QSAR standing
alone must be accepted with caution. It is only as they can be laterally related in a variety of ways
to other QSAR that we can begin to place some confidence in their informational content. The
subject will have to develop the way organic chemistry did. Someone discovers a new reaction andthen many chemists working over the years define the limits of the reaction.

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Table 1
Organization of Sets
Field
input data
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.

Title

Description

SYSTEMCLASSCOMPOUNDACTIONREFERENCE
SOURCE
CHECK
NOTE
DATE
PARAMETERS
SUBSTITUENTS
SMILESDATA
PRM MAX/MIN

biological or physical system
Pomona classification of system (tables 2 and 3)
parent compound (if any)measured action or activity
journal reference or other source of data set
person who entered data set
person who checked data set
additional information about data set
date on which set was saved into databaselist of parameters*labels of substituents
topological description of compounds
table of parameter values
maximum and minimum of each parameter

Output data (equation:)
15.
TERMS IN EQN
16.
EQUATION
17.
IDEAL
18.
STATISTICS
19.
RESIDUALS
20.
PREDICTED

parameters in regression equation
regression coefficients for each parameter
ideal (or optimal) log P, and confidence limitsn, df, r, s, etc.
deviations between y-predicted and observed
predicted values of dependent parameter* examined, even if not used in final equation.

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Table 2Class Codes--Biological Database{Number of sets in parentheses}*BO

Unknown

B1

Nonenzymatic Macromolecules(DNA, Fibrin, Hemoglobin, Soil,
Albumin, etc.) (220)

B2
B2A
B2B
B2C
B2D
B2E
B2F
B2G

Enzymes
Oxidoreductases (639)Transferases (155)
Hydrolases (588)
Lyases (36)Isomerases (11)
Ligases (3)Receptors (884)

B3
B3A
B3B
B3C
B3M
B3R
B3S

Organelles
Mitochondria (87)
Microsomes (97)
Chloroplasts (82)Membranes (92)
Ribosomes (0)Synaptosomes (22)

B4
B4A
B4B
B4C
B4E
B4F
B4P
B4V
B4Y

Single-Celled Organisms
Algae (37)Bacteria (668)
Cells in culture (568)
Erythrocytes (80)Fungi, Molds (240)Protozoa (93)Viruses (145)
Yeasts (47)

B5
B5C
B5G
B5H
B5I
B5N
B5S
B5L

Organs/Tissues
Cancer (90)
Gastro-intestinal tract (67)Heart (83)Internal/soft organs (62)Nerves, Brain, Muscles (326)Skin (50)Liver (17)

B6
B6A
B6B
B6F
B6H
B6I
B6P

Multi-Cellular Organisms
Animal (vertebrates) (646)Insects (168)Fish (150)
Human (37)
Invertebrates (non-insect) (101)Plants (118)* These numbers are constantly changing as new data are added daily.

9Table 3Class Codes--Physical Database{Number of sets in parentheses}*
PT

Theoretical (28)PO

Unknown

P1
P1P
P1X

Ionization (1502)
Ionization Potential (29)
Proton Exchange (67)

P2

Hydrolysis (773)P3

Solvolysis (613)

P4
P4I
P4E
P4M
P4N
P4R
P4U

SpectraIonization Spectra (61)ESR Spectra (2)Mass Spectra (12)NMR Spectra (175)
IR Spectra (9)UV Spectra (23)

P5

Miscellaneous Reactions (418)

P6
P6E
P6N

Substitution
Electrophilic Substitution (238)
Nucleophilic Substitution (1047)

P7
P7D
P7E
P7N
P7P

Addition
Dimerization (10)Electrophilic Addition (149)
Nucleophilic Addition (215)
Polymerization (12)

P8
P9
P10
P12
P13

Elimination (147)
Rearrangement (182)
Oxidation (420)
Radical Reactions (556)Complex Formation (100)P14 Partitioning (131)
P14C Chromatography (21)
P15
P16
P17
P18
P19
P20
P21
P22
P23

Pyrolysis (88)H-Bonding (26)Electrochemical (226)Br Ønsted (119)
Esterification (232)
Photochemical (39)
Hydrogenation (16)
Isokinetic (3)Reduction (81)* These numbers are constantly changing as new data are added daily.

10In compiling the physical database from mechanistic physical organic chemistry we haveconcentrated on chemical reactions in solution. Although there are some examples (275) based on
spectra and gas phase reactions, no attempt was made to be complete in these areas. The same
applies to the Brønsted reaction (108 examples).
Many papers report results from runs at a variety of temperatures. Generally we have reported
only 1 example at the temperature nearest to 25°. In cases where a reaction has been run in various
mixtures of solvents (e.g., ethanol and water) we have reported representative examples. For lackof time, we have not attempted to standardize the dependent variables as we have in biologicalreactions. That is, intercepts cannot be compared. Publication of Hammett type equations has
occurred at such a rapid rate and in such diverse areas that it was impossible to organize the results
before modern interactive computing. Finally, after considerable effort, we feel that we haveacquired a large percentage of the data and devised the means to view it from many perspectives.
Biological QSAR has been in an even more confused state. The major areas--biochemistry,
medicinal and pesticide chemistry and the various toxicologies--all have a large number ofsubspecialties e.g., enzymology, anesthesiology, cancer, mutagenesis, metabolism, cardiology,
pyschobiology, bacteriology, plant physiology, urology, etc. It is apparent from Table 2 that,beyond the few key words listed, we have not as yet attempted to include them in a systematicway. Yet they do provide significant help to the researcher. A further complicating factor is thatreports on these studies, that are appearing at an ever increasing rate, are published in hundreds ofextremely diverse and sometimes obscure journals and hence are difficult to find. Our databaseshows that partition coefficients (at the moment we have over 20,000 experimentally measured
octanol/water log P values of which over 10,000 are unique and considered to be reliable), fromwhich hydrophobic parameters are derived, have appeared in over 500 different journals. This
does not include the many reports on various types of chromatography carried out for the same
purpose. We believe the time has come to integrate these results. Since a variety of approaches are
currently being studied for the formulation of QSAR, one might question whether this is the time topush this project. However, the experimental data reported and organized will be of value for11decades to come regardless of how the methodologies evolve. In fact our system will provide thetesting ground for the various new approaches stemming from quantum chemistry and molecular
dynamics.
This applies to many of the QSAR in our current system. Many sets have been poorly
designed. The QSAR for these have low r2 values and outliers and sometimes have two few
datapoints/variable. Nevertheless we have found such first attempts to be helpful in supporting
each other and suggesting the next steps. When one attempts to rationalize in numerical terms theresults from treating even something as simple as a cell culture (let alone mice) with say 30 or 40'congeners' the problems are awesome. Nevertheless, the enormous drug industry constantly faces
these problems. The DNA codes for 50 to 100 thousand proteins that account for the many
enzymes and components of the variety of cellular membranes and those of the organelles. Allsorts of biochemical processes are open to perturbation. Thus, it is not yet clear what quality (interms of r2 ) one can expect with the complex Bio systems. However, a rational and statistically
based analysis is far better than intuition.
Our current premise is that the major interaction forces to consider in a set of congeners acting
on a biological system are electronic, steric and hydrophobic. Less important factors are hydrogenbonding and dipole moments. Hydrogen bonding can be important. However, as yet there is no
general way to deal with it, in the way that one can use Pi (π), for example, to account for thehydrophobicity of a substituent. The orientation and distance between an OH on the substrate or
inhibitor and the bonding site on the receptor is so critical that a general method forparameterization appears impossible. In this case, indicator variables can be helpful.
Before considering the details of how the total system operates, it is instructive to point out afew practical uses. For example, under P1 (table 3) there are 1,502 examples of QSAR based on
the ionization of all sorts of acids and bases. Of these, 1,301 are in terms of pKa. By moving to
the show mode (see below) one can by calling for system (1) and compound (3), scroll through all
1,300 sets for the ionization of acids and bases in less than one hour noting those set numbers ofinterest. These can then be loaded for detailed study. Of course, a similar approach can be used for12any of the much smaller classes in tables 2 and 3. One could load all of the summaries forionization including the QSAR and page through them in a couple of hours. This approachprovides mini reviews of massive amounts of research. By isolating all of those containing a termin either σ, σ -, or σ + we obtain 6,471 equations restricted to aromatic systems. Eliminating those
based on σ - or σ + we obtain 3,867 based on σ. There are now 1,113 different substituents thathave measured values of both σp and σm (i.e., 2,226 σ values). (There are 1,999 σp values,
including calculated values). Thus we can calculate ionization constants for 2,226 × 3,867(8,607,942) monosubstituted acids or bases assuming that there are two positions free for
substitution. This figure is high since in some instances there is more than one QSAR for a givenacid (e.g., benzoic) in different solvents. Fujita13 has shown how the effect of ortho substituentson ionization can be taken into account so that these too can be considered as well as multiple
substitution. The possibilities can be extended to acids of the type X-Ar-Y-Q where Q is an acidic
or basic group. A method is now available3 for calculating the attenuating effect on Q of many
examples of the linking group, Y (e.g., -OCH2-, -CH=CH-CH=CH-, -SO2CH=CH-, etc.).
The ionization of aromatic amines and phenols generally depends on σ -. There are 375
substituents with measured σp- values14 which along with the corresponding σm provide many
possibilities for calculating these ionization constants (it is generally found that σm = σm- = σm+ ).
There are 879 Taft-type2 σ* for use with aliphatic reactions where the field/inductive effect isimportant. Thus for the 110 QSAR of aliphatic acids and amines many new ionization constants
can be calculated.
The sterimol steric parameters exist for 1,078 different substituents.2 With the huge variety of
substituent parameters, and the ability of our current regression program to automatically load theseand over 14,000 QSAR for study, we believe that comparative QSAR has been sufficiently
organized so that it can do much to extend our understanding of chemical and biological reaction
mechanisms. As will be discussed later, all sorts of evaluations of substituents and parameters canbe made. For instance, we are finding the sterimol parameters B115 to often be more effective inthe correlation of intramolecular steric effects than Taft's classic Es parameter. At present in the13physical database there are 296 examples where B1 appears in QSAR and only 224 for Es or E SC.
We have not yet checked many of the early entries when only Es was available. We suspect thatsterimol parameters will do as well or better in many instances. Since sterimol parameters arecalculated, rather than measured like Es, many more can be readily obtained.
All outliers in the database are labeled. Searching the physical base we find 2,806 QSAR haveone or more outliers. This figure is somewhat unreal. In entering the early work, ortho substituentswere often entered, but not used in deriving the equation. Thanks to the work of Fujita andNishioka13 we can now cope with such steric problems. We can study the 2,806 outliers to see
which substituents have caused problems.
The bank can be searched to find examples where a particular substituent has been used. For
example in the physical base, CF3 appears in 921 equations, OCF3 in 31, SCF 3 in 14 and SF5 in
only 9. In Table 2 the numbers in parenthesis add up to more than the total for the database. The
reason for this is that in some instances more than one label is attached to a single equation. A
reaction might be labeled as P10 and P12 (radical and oxidation) or it might be tagged as B4B andB6A for drugs attacking E. coli in mice.
The major difference between the biological and physical database equations is the importanceof hydrophobic terms, logP, and π, in the former. Out of 6,300 Bio-QSAR 4,367 contain such aterm (69%). Considering the five major categories the following disposition is found:
Table 4

EnzymesOrganelles
Cells
OrgansWhole organisms
Receptors

QSAR with Hydrophobic terms
1291
300
1386
527
1014
379

Total QSAR
in class
2400
395
1829
741
1218
881

%
54
76
76
71
83
43

As might be expected hydrophobic terms show up least frequently in enzymes and receptors
and of course, show up most often in QSAR for whole organisms.

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There is much still to be learned about the role of hydrophobicity in the design of greater
selectivity in bioactive compounds and less toxicity in industrial chemicals. The principle ofminimal hydrophobicity in drug design is obviously important for bio availability,16,17 and one
must always be alert for the presence or absence of hydrophobic interactions. Sometimes
compounds participating in electrophilic reactions with cells or whole organisms do not display ahydrophobic effect,18 but the reason for this is not always clear at present.
The environmental toxicity of chemicals is constantly under review. Three widely studied test
organisms: fathead minnow, tetrahymena and daphnia, are represented by 40, 53 and 47 sets
respectively. Results with these toxicity studies can be compared with those from other organisms,
cells, etc. For instance, pentachlorophenol occurs in 140 sets, atrazine in 10, and methoxychlor in
21.
The present report outlines the current state of our efforts to organize the QSAR literature to
make it readily accessible for a virtually unlimited variety of comparative analyses. This will not be
the last such effort and we hope that our ideas will be of value to others considering the next steps
in comparative structure-activity analysis. Biological QSAR has become big business and large
amounts of money are being invested in its development. We are only in the early stages of a fieldthat computers have made intensely exciting.

II. Organization of QSAR Database
The database has been organized in two major areas of chemistry: Biological (table 2) andphysical-organic (table 3). Table 1 shows that the datasets in each of these areas is subdivided into
input information and equation information (output). Class Codes in Tables 2 and 3 simplify
searching. Normally the user is interested in either the physical or biological section. Splitting themsaves searching time, but more important, it makes the evaluation of the results of the searcheasier. A major objective of our system is to obtain the desired data in a precise, quickly-scanable
form.

15

Originally the 'systems' under study were entered and are accessible only by name. For
example, there might be a QSAR study on mice with acute LD50 as the reported activity. But if'mice' were the system entry used in searching, one might miss comparisons where the QSAR wasentered under the system 'mouse' or 'murine'. When there is ambivalence about names, enter
both. Searching under mouse find 311 while searching with both mouse and mice finds 412. One
might also want to expand the comparison to include certain vertebrates (such as rats or guineapigs) but exclude others (such as fish). To make both entry and access more specific, yet keeping it
simple, we have added a classification code as the second input data item in Table 1. It is apparent
from table 2 that the code numbers increase with increasing complexity of the biological system. In
all cases the 'class' is the broadest category and is narrowed by the 'system name'. Searching with
2 B6A isolates all vertebrates (646), then string searching with rat finds 197 sets.
A major problem in searching either database for parameters of a desired type is knowing whatlabel to use. As indicated below, we have developed standard labels for the most often usedparameters, but any parameter can be used if it is defined in the note section. Of course finding setscontaining such unusual parameters is difficult unless one knows the symbol. The following
symbols define the major parameters. One can use the browsing mode to look for unusual labels.
Electronic Terms. S, S-, S+, S., SI, and S' are the English alphabetical representation of theHammett type constants: σ, σ -, σ + , σ •, σI and σ*(see ref. 1 for definition and many examples of
their use) Note that for the radical parameter (sigma dot) a period is used, and the prime symbol isused instead of the asterisk to denote Taft's sigma star, since the asterisk has another programmingrole. S' is not sigma prime (σ').
At present the database contains a few QSAR based on molecular orbital calculations. The
labels employed are: HOMO, LUMO and Qi . In some instances LUMO-HOMO has been used todefine the HOMO-LUMO gap. E 12

is the potentiometric oxidation or reduction value, and U

represents dipole moment. BDE stands for bond dissociation energy.
Hydrophobic Terms. Log P, Mlog P and Clog P parameters are based on octanol/waterpartition coefficients for the neutral form of the compound. Mlog P (M= measured) values can be16

automatically loaded from our preferred list of over 10,000 experimental values while Clog P
values are calculated from structure by a fragmental method19 and are now calculated andautomatically loaded for regression analysis. Recently, Leo has modified the method of calculating
Clog P so that one no longer faces the missing fragment problem. In early work log P values were
loaded by hand and may contain values calculated manually. Log P' represents something otherthan the standard octanol/water value for a neutral compound. It may come from another system(e.g., chloroform/water) or it may be from buffered octanol/water when ionizable compounds areunder consideration. It should be defined in the notes. Hydrophobic parameters have sometimes
been obtained by chromatography, and these are termed RM. PI represents π, the hydrophobic
constant for aromatic substituents. For automatic loading the PI value for the normal benzene
system is chosen--not the value as seen on nitrobenzene, pyridine or aniline.
Steric Terms. ES is the classical Taft parameter20 and ESC is the form of E s corrected by
Hancock for hyperconjugation.20 B1, B5 and L are the sterimol parameters proposed by Verloop,
Hoogenstraaten and Tipker15. MR represents the molar refractivity of a substituent while CMR is
the molar refractivity which is automatically calculated for the whole molecule. Both are scaled by
0.1 and can be automatically loaded . MV represents molar volume that may have been obtained bya variety of means. MGVOL is the McGowan21 molar volume for the whole molecule that can be
automatically calculated and loaded for regression analysis. Values for most of these can be foundin the Parameter database (THOR Sigma).
Very often substituent effects (and hence parameters) are position dependent. This is indicatedby a suffix following a comma: S,M or PI,4 or B1-3; where M(eta),4 and 3 represent the ring
positions where the substituent is attached. Multiple positions on a parent structure given in Field 3
in the input data are indicated by, for example, PI,X,Y or PI-SUM.
Ad Hoc Parameters. All sorts of special parameters have been used by different authors.
Usually these are defined in the notes, but sometimes the original paper must be consulted. Most
prominent are the indicator variables (1 or 0) used for the definition of many different kinds ofstructural variations. When they are not defined in the notes, their meaning is easily deduced from17

an inspection of the columns of parameters. Recently solvatochromic parameters, α and β, havebeen defined as measures of H-bond donating and accepting capability respectively. These arelabeled 'alpha' or 'beta'.
Dependent Variables. In the physical database the dependent variable is most often a rate orequilibrium constant represented by K or KREL. The latter symbol generally means relative to H.
In the biological database the most common dependent variable is log 1/C where C is the molar
concentration of chemical (moles/liter or moles/kg) used to produce a standard response. When thetime is fixed this can be shown to be a rate constant. Sometimes RBR is used where RBR
represents a relative biological response. When log 1/C is used intercepts of the QSAR can often becompared with others. Of course this is not true for RBR. At present log 1/C is used in 3,947 sets.
For enzymic reactions KM, KCAT and VMAX represent Km, k cat, k cat/Km and Vmax.
Integrated with the QSAR database is a model-building regression program (Section IV ) into
which any particular existing data set can be loaded and re-studied with new or additionalparameters and structures.
Several other databases and calculation algorithms complete the suite of software in the CQSAR™ 'package'. The MASTERFILE database contains over 45,000 log P values measured inover 300 solvent systems, with octanol/water dominating. It also lists pKa's and activity types forabout 11,000 structures.

III.

Using the Databases

Note: In the following sections the commands to be typed in are in boldface and underlined, and a
break in the underlining indicates a space is required, but case is not important.
A. Help: The user is encouraged to use the 'Help' function at just about any point in thisexercise. Entering ? ( or help ) delivers a brief help message, and choosing any of the functions
listed preceded by ?? will give detailed help. For example, while in the regression mode, ? will
return a list of functions , and if one were interested in 'eigenvalues', entering ?? eigenvalue will18

tell how to calculate them for a set of parameters. ?? Pred shows the various way results from a
calculation can be displayed.
B . String Searching:

Grammar plays a very important role in this system, and the'grammar' involved, while simple, must always be kept in mind or else spurious results will beobtained. The grammar for string searching can be illustrated with the word 'in'. It can stand alone(as a preposition) or be part of another word. The following examples show four distinct contents
in which it can appear.
E.coli in mousea word(both leading and trailing blanks) " in "influenza

start of word(leading blank, but no trailing blank) " inbrain

end of word(trailing blank, but no leading blank) in "pyridine, guinea

inside a word

(neither leading nor trailing blanks) inTo match only strings occurring at the start or end of a word the string must be 'extended' toinclude a leading or trailing blank. This is done by starting/ending the query with a quote andblank. A quote and blank before the set of searching letters restricts matches to the beginning of theletter string, while a blank and quote after the string of letters restricts matches to the end of words.
To match only whole words, leading and trailing blanks must be present. Some further examples
may make this clearer.
"HEM" or HEM matches HEMOGLOBIN, but not CHEMOTHERAPY.
"ASE " matches LYASE , but not L. C ASE I.
If you 'quote' a string, but do not include either a leading or trailing blank, the query is nodifferent than if you had not included the quotes at all. It is not required that quotes be matched up
before and after a word. The two examples above could be stated:" HEM matches HEMOGLOBIN, but not CHEMOTHERAPY
ASE " matches LYASE, but not L.CASEI
Any character search can be negated by prefacing it with NOT. This causes the result to be thereverse (logical complement) of what it would otherwise be.
NOT CAT does not match CAT, CATCH, CATTAIL

19

NOT ASE " does not match LYASE, but does match L.CASEI
In combining 'strings' it is important to note that a space denotes 'or' and expands the search,while a comma denotes 'and' and restricts it. Examples will follow which will demonstrate thepower of string searching both databases. While learning to use the system, one should inspect theresults from searches to be sure that one has found only the desired information.
C.
Physical Database:
After logging on, enter QSAR at the system prompt (usually the $ sign), and follow it by dataphys ical. A prompt then asks for a password. For read only access press RETURN and the main
menu is displayed (table 5).
Table 5Main Menu
1
Summary
DIR
2
Show
HELP
3
SearchPRINT
4Browse
QUIT
5
MedChem
READ
6Manager
REG
7
Save Database
VMS
WRITE
1 . Main Menu. Entering 1 gives a summary of the number of sets, compounds and
SMILES (see section VI). This table applies to both databases. The biological database has
113,750 data points (compounds). The physical base contains 76,111. Calling for 'help' will
instruct you how to exit from any mode and call another as well as reinforce the explanations inthis Manual.
2 . Searching and Show is accomplished in two steps: first one enters the sea rch mode
from the main menu (3), then one enters the command and presses return to start the search. Afterthe search is completed, use the sh ow mode to display the hits. The Search Menu is shown in table
6.

20

Table 6
Search Menu
SEARCH0 Equations

8 Note15 Terms in eq. DIRBACKUP

1 System

9 Date16 Coefs. in eq. HELP

BLANK

2 Class

10 Parameters

17 Ideal/logB

PRINT

NOT

3 Compound

11 Substituents

18 Statistics

QUIT

LIST

4 Action

12 SMILES

19 Residuals

READ

5 Reference

13 MERLIN

20 Predicted

REG

6 Source

14 Prmmax/min

7 CheckVMS
WRITE

The Show Menu differs from the Search Menu only in the left hand column which illustrates
the sorting option.
As noted above, the grammar of string searching must be carefully followed or else one can get
more than the expected result. For instance, one might search the physical base with the command
2 P1 where 2 refers to the 'class' as listed in table 1 and 1 is thought to refer to the code for'Ionization' as listed in table 3. However with this entry one would recover 3,414 sets, which is
far too many for ionization alone. As entered, the command locates all sets with classes P1, P10,
P12...etc. and finds 'oxidation', radical reactions' etc. The correct entry has leading and trailingspaces 2 " P1 " and finds only 1,493. Entering SH OW takes one to the show mode where theresults can be inspected.
The value of string searching can be illustrated with a search of the action field while in the'Search Menu'. Entering 4 bromin returns the Search Menu with a status check showing thesearch will be performed on the entire physical database of 7,700 sets and the search requested is'Action....BROMIN'. To perform the search, enter sea rch, and 167 hits are found. To peruse the'catch', enter sh and then 4 and one finds bromination, brominolysis, photobromination,
dehydrobromination, etc. If you were interested only in equations where bromine is involved in an
addition reaction, which is classed as P7 in table 3, enter sea rch to return to that Menu, and enter 221

P7 and then press return, which shows the status check indicating that the search will be made on
the 155 hits made previously. Enter sea rch, which finds 54 sets of bromine addition reactions. If
you want to see bromine in radical reactions, you need to return to the original 155 hits. This canbe done by blanking out the last search with the entry bl 2 . Then enter 2 P12 and after approving
the status check, enter sea rch. This returns the Menu showing 54 hits. One might want to view
these sets showing 'system', 'action' and 'terms', and would enter sh 1 4 15 . To return from any
Menu to the Main Menu, enter q. To illustrate another way to search the bio database from thesearch mode enter 1 HIV to find 128 QSAR associated with the AIDS virus.
Important Note: In combined commands used in the 'show ' mode, the grammar differs
somewhat from that used in string searching. A space specifies 'and' (not 'or'), and a comma
specifies 'through'; e.g., 1 4 means 'one and four' but 1 , 4 means 'one through four'. This will
become more apparent in further exercises.
More complex Searching Examples: (The novice can skip to section III to become
familiar with other basic operations before returning to this section, if desired.)Category 1 in Table 1 is SYSTEM, and for the physical database it refers to the solvent inwhich the reaction was run. In searching this field the NOT command is very helpful. Often mixed
solvents were used as the reaction medium and the % sign is always used in their identification.
The importance of this feature can be illustrated if we wish to search for reactions run in aqueous
solution. ('aqueous' is always used, not 'water'). Searching with 1 aqueous would make 3,441
hits. Adding to the search 1 not % eliminates mixed solvents and would reduce this to 1,398.
Searching with 2 " P1 " finds 533 QSAR for ionization in water.
Remember that results from all previous searches can be removed by entering bl ank.
Alternatively, one or the other can be removed by bl 1 or bl 2 . To find which of the 7,700 sets are
based on mixtures of ethanol and water enter, 1 aqueous, " ethanol ". Note that several
commands can be entered on one line by the use of commas which denote 'and', but the status
report shows them on two lines. This search finds 698 sets based on ethanol-water mixtures. To
see if certain steric parameters are significant in the QSAR of these sets, enter 15 MR ES B1
22B5 . There are 49 hits. Entering sh ow and then 15 displays the variables used in each set , withthe dependent variable first (15 refers to terms in the equation, Table 1).
Any number of codes can be used simultaneously. Entering 2 P3 P2 " in the search mode,collects all examples of hydrolysis and solvolysis (1,383). The quotes on P2 are required by string
searching grammar so that P20, P21, etc. would not be included.
Collecting studies based on the various Hammett-Taft sigma constants requires some thought.
Besides S, S+ , S - and S' (computer-compatible letters for the Greek symbols σ, σ + , σ - and σ*)various positional suffixes have been attached to sigma. Most common are S,X and S,Y where thesigma parameter may be of a different type at the two positions; e.g., S,X and S + ,Y. Also, it must
be remembered that in string searching one must use a leading blank as in " S or sets with ES will
be included. If you wish to find all occurrences of 'ordinary' sigma (S), including those at
specified positions but excluding the 'special' sigmas (S+, S-, etc.), the following steps should be
followed. Entering 15 " S finds 7,111 sets including both positional and 'special' sigmas. To
remove the latter, enter 15 not S- S + SI S ' S . and 3,859 remain. Notice that the 'not' commandis not necessary in searching for the 'special' sigmas. For example, 15 " S- finds all QSAR basedon sigma-minus parameters (980), including those at specified positions.
In the above examples we have focused on S alone, but the QSAR collected might containother terms. These could be eliminated as follows:
1.
2.
3.
4.
5.
6.

15
15
15
15
15
15

"S "
not
MR
not
B1
not
ES
not
logP
not
**2

3,618 hits
3,612 hits
B53,490 hits
3,435 hitsPI3,423 hitsbilin 3,385 hits

The symbol **2 represents squared terms such as S2 or PI2 and bilin represents bilinear
equations. Note that the NOTS must be listed as a separate entry.
Moving to sh ow and entering 15 we see that except for a very few examples, all QSAR arebased on the single term S. The search could be changed by the command bl ank 2 4 whichremoves these two commands leaving:

23

1.
2.
3.
4.

15
15
15
15

"S "
not
B1
not
logP
not
**2

B5
PI
bilin 3,445 hits

Non linear QSAR. Over the years nonlinear QSAR have been found, and a variety of
approaches have been devised to deal with them. Most of these involve using more than one
variable. Because of the lack of general agreement on how to correlate such data we have normallyused a single parameter and either a parabolic equation (aσ + bσ2) or a bilinear equation. These canbe searched for as follows:
15
15

S+**2
bilin

finds 114 eq. parabolic in σ+
locates 31 sets bilinear in any parameter

Range Searching. As noted above, searching the physical database with 15 " S " finds 3,618
sets. It does not include electronic parameters such as S, X, etc. Normally, this is too large a list to
scroll through. If one wanted to see only those equations with a modest electronic dependence, one
could enter 16 - . 8 < " S " <1 . This locates those equations where the coefficient with sigma (ρ)
lies between -0.8 and +1. (1,139 examples). In the Search Menu, 16 is the code for the coefficientwhich, in the present case, is that for S. We might now check these sets to see how many
examples of radical reactions have a term in S, with such slopes, by entering 2 P12 (The Search
Menu shows '2' as representing 'class', and table 3 shows 'P12' representing Radical Reactions.).
We find 73. This more modest list of equations can now be viewed, logically ordered byincreasing sigma coefficient. First enter sh ow. The Show Menu lists all the items that can bedisplayed with each equation. At first we may only want to see the essentials, and so we enter/sort = 16 1 3 4 15 , 18 . (As noted previously, in the grammar in the Show Mode a spacemeans 'and' and a comma means 'through'.) The program then asks which coefficient to order the
display on, and one enters " S " . The information specified by 1 3 4 15 , 18 of the Show Menu
is displayed and the sets are ordered in terms of increasing values of the coefficient, ρ, from 0.798 to + 0.982. Since 16 delivers the equations with coefficients and terms, it might seem
redundant to specify 15 also, but it is so often useful to spot the significant parameters at a glance.
24Compound. Searching for specific compounds by their 'names' cannot be made effective. In
Section 3 of Table 1, entries such as phenols, X-C6H4-COOH or benzodiazepines have been used.
Compounds can more effectively be found by using the SMILES notation or by substructure
searching using MERLIN as shown below. However, searching category (3) for generalized
structures can be instructive, if preceded by a search by Class. For example, one might want to see
what types of chemicals have been studied in radical reactions. In the Search mode enter 2 P12
followed by sea rch. This locates 556 sets. For a quick overview of the compounds studied, entersh ow and then 3 . Scrolling through these examples takes less than ten minutes (imagine how longit would take to locate these in the library) and set numbers for those of interest can be noted.
These can be examined in detail by switching to the regression mode (reg ) which will be coveredin Section III-7. Not surprisingly, it is seen that toluenes are a favorite subject for radical study.
Returning to sea rch and entering 3 C6H4CH3 C6H4Me Toluene (note string searching isbeing called for) finds 73 cases where various derivatives of toluene have been the subject ofinvestigation. This gives a quick, but probably incomplete, answer. A more systematic method
which yields 111 hits is discussed later.
Action. (4 in Search Menu) Since uniform nomenclature for entry of this type of data has notbeen developed as yet , one can not be sure that a search will find all that one is interested in. Since
searching is so rapid, a viable strategy is to start with a broad search and narrow it as you see what
it finds. For example, enter 4 chemical which sequesters 183 examples where this word was
employed. Perusing the hits in the sh ow mode (4), we find words such as photochemical,
electrochemical and chemical shift. Repeating the search entering 4 chemical, shift uncovers
141 NMR studies. Searching with 2 P4N locates 175 NMR studies which includes those basedon coupling and splitting constants in addition to chemical shifts. We have made very little effort toinclude QSAR on spectra or the BrØnsted reaction.
References. (5 in Search Menu) Searching the reference category can often be of interest. One
can often recall the name of a person who made a certain study, but cannot remember where or
when. For instance, searching 5 Bordwell locates 103 sets. These can be narrowed by checking
25certain years; e.g., 5 (1997) (1998) . Eight QSAR came from recent papers published in these
two years. Entering bl 2 to begin again with 103 hits and entering 5 Cheng isolates 19 QSAR by
Bordwell and Cheng.
It might be of interest to see the trend in publications of QSAR in physical organic chemistry.
The following searches could be made: 5 (1950) (1951) (1952) ; 149 hits. 5 (1970) (1971)(1972) ; 898 hits. 5 (1990) (1991) (1992) ; 352 hits. It is necessary to put parentheses aroundthe year to distinguish it from page numbers. There are 114 QSAR from 1998. Remember thatthese are equations not individual papers. Several QSAR may come from one article. Althoughremaining strong, interest in the Hammett type equations in the 90's seems to be subsiding.
Publications in particular journals can be checked. 5 J.Am.Chem.Soc. finds 1,619. Note thatone should not leave spaces between the words in the title, but case is not important. From anotherpoint of view we can see where most publications occur by entering the following three searches insequence:
55
5

not J.Chem.Soc.
not J.Org.Chem.
not J.Am.Chem.Soc.

The hits decline as: 6279, 5268, and 3652. Thus out of 7,700 sets only about half have been
published outside of these three journals. Entering sh ow followed by 5 one can scan the list of theless 'popular' journals where publications have appeared. Note that no space has been left between
the abbreviations.
Searching for Similar QSAR. One of the most important uses of the C-QSAR program comes
after a new QSAR has been derived. The database can then be searched for comparable equations
that may help validate the new one. For example, suppose that you have formulated a new QSAR
for an aromatic nucleophilic substitution using the (S-) parameter and the resulting coefficient ρ - =2.7 Table 3 gives the Class for this type of reaction as P6N, and so you can search for similar
equations by entering:
2 P6N which delivers 1047 hits for nucleophilic substitutions, and then:
16 2 . 6 < S- < 2 . 9 7 which narrows the sets of interest to 9.
26

The first command isolates all nucleophilic substitution reactions. The second finds those with
ρ - in the range 2.6 to 2.97. The following are representative examples. Going to Sh and listingwith /sort = 16 1 3 4 15 16 18 followed by S- yields:
Reagentρ−

SystemCompound1.

20° Aqueous

X-C6H4OCO(CH2)3NMe2

Internal

2.60

2.

20° Methanol

2-p-nitrophenoxy-3-NO2 -5-S-thiophenes

piperidine

2.61

3.

20° Aqueous

X-C6H4OCO(CH2)4NMe2

Internal

2.65

4.

75° Benzene

6-X-2-NO2 -C6 H3 Cl

piperidine

2.72

5.

20° Benzene

2-phenoxy-3-NO2 -5-X-Thiophenes

C6 H5 CH2 NH 2

2.75

6.

50° Methanol

4-X-1-I-2-nitroiodobenzene

N3-

2.90

7.

20° Methanol

2-Br-3-NO2 -5-X-thiophenes

piperidine

2.96

Two examples omitted.
It is sometimes desirable in comparative QSAR to focus on equations with a specific number ofterms with a limited number of variables. This can be illustrated with a search for all QSAR
containing only 3 terms, all of which are variations of σ.
1.
18
2<terms<4196 hits
2.
15"S
186 hits
3. 15 not ES I MR D F B1 B5 **2 bilin

22 hits

The first search isolates all QSAR containing 3 terms. The second ensures that σ occurs at least
once and the third that all QSAR which contain a term other than σ or nonlinear terms are
eliminated. These generally are reactions involving two molecules in both of which substituents
have been varied. It should be noted that until one obtains experience it is good practice to inspectsearch results at each step to see if ones expectations are being met. A good example of such athree term equation is the following: (set #4405)

27

X-C6 H4 NH 2 + Y-C6 H4 CH2 CH2 OSO2 C6 H4 -Z → Y-C6 H4 (CH2 )2 NHC 6 H4 -X + Z-C6 H4 SO3 Log k2 = -1.32(±.05)σ,x – 0.13(±.01)σ+,y + 1.08(±.03)σ,z – 3.93(±.01)
n = 80,r2 = 0.992,

s = 0.042,

q2 = 0.991

Another way of looking at QSAR with a definite number of terms is the following from the Bio
QSAR database:
1.
2.
3.

18
18
18

3<T<5
n > 20r>0.90361 hits
274 hits
213 hits

This finds all QSAR that have four terms, a minimum of 5 data points/variable and a fair quality ofcorrelation.
Continuing with the bio database:
Source. This compartment contains the name of the person who actually entered the data, notthe paper from which it came. Entering 6 Gao uncovers 1,445 data sets entered by Hua Gao.
Check. The name of the person who checked the entry is contained here. If it has not beenchecked 'unknown' is entered. Entering 7 not unknown finds 2562 have at present beenchecked by someone other than the person deriving the QSAR.
Note, data and parameters. There is little or no value in searching these fields.
Substituents. This field can be of value for finding QSAR that contain a particular substituent if
it has a generally accepted form. Searching the bio data shows the following. The methyl group
might be entered as CH3, Me, or methyl. Searching 11 CH3 locates 949 sets; 11 Me, 4,590 sets
and 11 methyl , 497 sets. All of these could be sequestered by one command; 11 CH3 Me
methyl . Searching substituents can be of value in locating unusual functions. 11 SO2C6H5
makes 44 hits. The location and type of aromatic substituents can be studied as follows 11 " 2NH2 " "O-NH2 " isolates 81 examples. Note that string searching is involved so that quotespace notation is necessary. 11 " 3,4,5-Cl " " 3,4,5-Cl3 " finds 26 examples. " 2-NO2 , 4 NH2 " locates 4 examples. 11 " 2,6-Cl " " 2,6-Cl2 " uncovers 83 sets in which both ortho
positions are substituted.

28

3 . Browsing. From the Main Menu, entering 4 returns the following table.
Table 6
Browse Menu
1. System
2. Class
3. Compound
4. Action
5. Reference
6. Source

7. Check
8. Note10. Parameters
11. Substituents
12. Smiles

By entering any of these code numbers all of the examples in the system will be listed exactly
as they have been entered without (as yet) any kind of organization. It still can be useful for the
beginner to become familiar with the terms that have been used, especially in the biological
database. Ordinarily only categories 1, 3, 4 and possibly 5 are of interest to browse.
4 . Statistics. With each QSAR the following statistics are given:
N
DF
R
R2
S
SS1
SS2
D+
DOmit
Q2

Number of datapoints
Degrees of freedom
Correlation coefficient
Squared correlation coefficientStandard Deviation
Sum of squares about the mean of the dependent variable
Sum of squares from the deviations from the regression line
Number of positive deviations from the QSAR
Number of negative deviations from the QSAR
Number of datapoints omitted in deriving the QSAR
Indication of the quality of fit of the data

Any of these fields can be searched, but only a few are normally of value. They are accessedby 18 of the Search Menu (same in table 1). Checking the Phys data illustrates the useful searches.
The command 18 n>20 in the search mode finds only 447 out of 7,700 QSAR that are based on
21 or more data points. 18 n>10 hits 1,882 sets and 18 n>4 locates 6,651 based on 5 or morecompounds. 18 n<4 locates 123 based on 3 data points. Of course, three data points is notenough to properly define a QSAR, however, if it is very sharp with a good spread in the data
points it is better than nothing. There are no equations based on 2 data points. Physical organic
chemists have mostly been interested in establishing a value of ρ for a reaction with a minimum of29

effort and hence often have not studied as many derivatives as those interested in Bio QSAR do.
Normally one needs a minimum of 5 data points/variable with well spread parameter values.
The quality of the correlations can be evaluated by means of the correlation coefficient r.
Entering 18 r > . 9 0 shows that 7,552 sets have correlation coefficients greater than 0.90. 18 r
> . 9 5 yields 6,856 QSAR and 18 r > . 9 9 finds 3,209. Quality can also be checked with respect tothe standard deviation S. 18 S < . 1 0 hits 3,973 QSAR with standard deviations less than 0.1 while
18 S< . 2 0 makes 6,129 hits. Quality can also be analyzed in terms of the deviation of individual
data points (Residuals, 19). A search with 19 - .05<dev<.05 hits only 1,075 examples where
no calculated value in the set deviates by more than ±.05 from the experimental value. This is avery stringent standard. Relaxing the standard to ± . 2 finds 3,842 examples.
5 . Omitted Data Points. In developing a QSAR the question is often raised as to when, if
ever, one should withhold data points. We believe that there are several good reasons for doing so.
Outliers may be pointing to a failure in the mathematical model; or they might result from an error
in the method of calculation of the dependent variable; they can be the result of an experimental
error or they can result from a side reaction. Whatever their source, it is extremely important thatomitted points not be forgotten.
In our system, to omit a point it must be marked by an asterisk (starred) which is held with thedata point so that it will not be forgotten. Moreover it becomes possible to make generalized studiesof outliers. For example, entering from the Phys search mode 2 P12 collects all examples ofradical reactions (556). Now entering 18 omit>0 isolates all QSAR with one or more starred data
points (214). Moving to sh ow and entering 11 lists all substituents for each data set.
Using the above approach any set of data can be rapidly surveyed to obtain an overview of the
QSAR. For instance entering from the search mode 2 P5 isolates all miscellaneous reactions, atpresent 418 examples. Some of these can be re-classified. Moving to sh ow and entering 1 3 4 11
15 16 18 one can look over all that has been done without loading each individual set to examine
the results. One can scroll through the output in half an hour. Note that beside each substituent, isthe residual, that is, the difference between the observed and experimental value for the current
30

stored equation. In this manner, the poorly behaved substituents for any type of reaction could bedetermined.
Example 1 shows how exemplars for any type of parameter could be selected and example 2
illustrates how good examples of a particular type of reaction (in this case nucleophilic substitution)
can be found.
15
18
18
19

Example 1" S+
1,653 hitsn > 10335 hitsr > .98
161 hits-.1<dev<.1
17 hits

2
18
18
19
19

Example 2
P6N
1,047 hitsn>10
276 hitsr>.98
175 hits
omit <1175 hits-.1<dev<.152 hits

These sets could be used to test for calculation of electronic effects of substituents by newly
developed methods.
Of course one might want to find poorly fit data to test methods to improve it.
15 S +
1653 hits
18 n > 15
142 hits
18 r < . 9 0
2 hits
6 . SMILES. Almost all structures in the databases are entered in the SMILES notation(section VII) so that any compound can be located via its SMILES or a common name. For
example, to find QSAR which contain phenol, go to the search mode in the Phys bank (enter dataphys and then 12 ). This returns a panel where one can enter either the SMILES (clccccclO ) or
phenol . Pressing return shows the structure of phenol. If it is correct press n if not press y forediting. Pressing n returns the program to the searching mode. Entering sea finds 285 sets that
contain phenol. Normally this locates a set of phenols; however, sometimes phenol may be one ofa miscellaneous set of compounds. Going to sh ow and entering 3 one sees a variety of examples
other than all-phenol sets. For more discussion of SMILES, see section III-C, 6 and ref. 25.
To find more specific information on the phenol sets isolated now enter, for example, 2 P6E
to collect examples where phenol is involved in electrophilic substitution. Sea rching finds 12
examples. Moving to sh ow and entering 15 it is seen that 6 QSAR are based on σ + and 6 on σ.
Entering 4 (action) shows that 7 examples involve bromination, 4 iodination and 1 on nitrosation.
31

Substructure searching is another approach to isolating specific types of compounds (Section
VIII).
Obtaining the SMILES structure by entering the name has serious shortcomings. For instance,
entering p-chlorophenol yields the SMILES, but entering 4-chlorophenol fails. Using the name isvery helpful with complex drugs or natural products where the SMILES takes time to write andwhere there is a standard name. Entering Quinine to get the SMILES and then searching, one setof data is found in the physical database that contains quinine.
7 . Loading from 'Database Search' to 'Workspace'. After a data set of interest has
been found by means of the 'search' and 'show' modes, it can be examined in greater detail iftransferred via its set number to the regression mode. (Modifying an old set or preparing a new one
for actual regression analysis will be described later.) This is done by entering reg ression fromeither the Bio or Phys database, and for this and the following exercises, transfer to the biological
database with the entry data bio followed by reg . The screen prompt becomes qsar>. Enteringload /d 1890 (set number) transfers the set to the workspace. Any data set can be viewed in thefollowing ways by entering the indicated commands:
Sum mary
System:Class:
Compound:Action:Reference:
Source:Check:
Note:
Parameters:
Eq/run:Seed ata
Seeeq uation
eq /runSeep arameters
1
Ypred

Lists key items of dataset.
Canine gastric mucosa H+, K +-ATPase
B2C Hydrolases
4-X-phenyl-2-NR-Guaindinothiazoles
I50
Ojha, T.N. et.al. Ind. J. Biochem. Biophys. 30, 239 (1993)
HanschL. Zhang
I=1 for NCH 2 C6 H5 , I-2=1 for 3,4-di-OH
Y Pred Dev log 1/C I S+ I – 2
log 1/C = 0.62(±.18)I - 0.15(±.15)σ+ + 1.25(±.29)I-2 + 4.49(±.11)
n = 23, r2 = 0.901, s = 0.185, q2 = 0.863
Displays table of parameters and their values together with predictedvalues and their deviations from the QSAR.
Displays the last derived equation.
Shows stored equation for comparison with any newly derived equation.
Lists parameter labels with their numbers. For example:2
Dev3
log1/C

4
I

5
S+

6
I-2

32

'Ypred' is the predicted value from the latest stored equation. 'Dev' is the difference between thisfigure and the observed value (item 3). The dependent variable is usually entered in position 3.
To see the SMILES generated structures for the compounds of a data set enter depict 1 ,which depicts sequentially all of the compound structures as one presses return after each panel of
4 structures. The process can be stopped at any point by entering q. This can be very important indealing with a large data set, say > 100 compounds. To view any particular structure enter depict# (number of compound in set). To check all structures following compound 16 enter depict 1 6 ,To view all structures up to 16 enter depict , 1 6 . To see those between 16 and 20 enter depict
16,20.
D. Biological Database
Our database of 6,300 Bio QSAR can now be used as the starting point for developing ascience of chemical-biological interactions. We believe that as it grows, it will help to minimize
redundant research and provide new leads via the study of familiar chemicals.
To access the Bio database from the $ prompt, enter QSAR then data bio and on request for
password press return . As in case of the physical database the Main Menu is displayed.
1. Browsing. The inexperienced user can use the 'Browse' feature in the biological databaseto even better advantage than in the physical database to become acquainted with its contents. In theMain Menu, entering 4 displays the Browse Menu. As an example, in browsing the 'system' you
might find 'cat' as an item of interest. The items in 'system' have not been alphabetized, and there
might be several studies of cats that may be of interest. One can search by entering 1 " cat ",which returns the display:cat

muscle tibialis cat

aortic ring of cat

gut catcat intestine

phosphodiesterase III from cat heart

liver extract cat

muscle tibialis anterior cat

At this point it might be advisable to review the grammar used in 'string searching' as given in
Section III.B.

33

2 . Searching. String searching the 'system' field (1) in this database can be of more value
than it was for the physical database. Field 1 does not have standardized information, but use of'Class' (2) can increase its utility. For instance, entering 2 B4B yields 669 sets relating tobacteria. Moving to the sh ow mode, one can quickly page through these sets by entering 1 tosurvey just what kind of microorganisms have been studied. Or entering 3 gives the types ofcompounds that can also be quickly scanned. Such a survey of all of the work on bacteria can bemade in less than 5 minutes. It would take days in the library. Returning to sea rch and entering 1
aureus makes 107 hits all of which are for S. aureus, except 3 for M. aureus and 2 for P. aureus.
Now entering 15 not logP PI RM eliminates all QSAR with hydrophobic terms, leaving only 8
examples. These are of special interest since hydrophobicity is so often a factor in Bio QSAR.
Entering bl 2 removes the last command. Now entering 15 " S locates 35 QSAR with various
sigma terms. Going to sh ow and entering /sort=16 1 3 4 15 , 18 and " S on the prompt
displays the QSAR in order of increasing value of ρ.
Using a combination of 'system' and 'class' in searching can be effective in other ways.
Searching with 1 " rat " results in 797 hits but includes studies on, for example, 'oxidase
monamine liver rat'. If only whole animal studies are of interest, this can be narrowed by entering
2 B6A , which reduces the hits to 144. Reversing the order in this last search gives the same
result. Sometimes the purpose of a search is to look for similarities that cross 'class boundaries'
and the search is broadened rather than narrowed. For example, one might want to look forsimilarities in equations dealing with the enzyme oxidoreductase, the organelle microsome, andwhole animals. This search would combine: B2A B3B and B6A to yield 1,357. Now we could
isolate QSAR containing a σ+ term for comparative analysis by 15 S + to find 102 cases. Next
move to sh ow and enter /sort=16 1 3 4 15 16 18 and follow this with S + on the prompt.
Searching on biological activity presents problems, because there are innumerable ways inwhich biological activities have been defined. We have not yet attempted to systematically define
searchable terms. Still, searching this field can be helpful. For example, using 4 metab finds 24
examples where the authors referred to their work as metabolism. Of course, there are hundreds of34

examples of such studies that have been referred to in other ways: dealkylation, hydroxylation,hydrolysis, etc. Some examples are:
4
4
4
4
1
4
1
1
4
4

bind
uncoup
Biocon.
GlutathGlutathInflamCycloox
HIV
Mutag
LD

781 hits for binding of chemicals to various bio materials
42 hits for uncoupling of oxidative phosphoralation
21 hits for bioconcentration in various ways
17 hits in which glutathione was employed
28 hits including glutathione transferase
13 hits on anti-inflammatory agents
29 hits on cyclooxygenase 1 and 2
128 hits on human immunodeficiency virus
34 hits on mutagenesis
227 hits mostly on LD or MLD plus a few outliers

Combined searches of the 'parameter' category can be fruitful. The following set of commands
can be used to look for patterns in electronic and hydrophobic effects in various biological systems:
1.
2.
3.
4.

15
15
15
15

logP
not
logP' **2"S
not S + S- SI S ' S . ES MR

bilin

3,600 hits
2,443 hits
2,048 hits
1,623 hits

The second command removes sets based on log P from systems other than octanol or
instances where P' is the distribution coefficient for sets of partially ionized compounds. It also
removes QSAR nonlinear in log P which makes for easier first time comparisons. Now moving tosh ow, two types of comparisons can be made. These are mostly QSAR linear in log P. First,
ordering on σ (S) we find a group of antitumor agents of the aniline mustard type (XC6H4N(CH 2CH2Y)2 with ρ of about -1.5 to -2. and a small generally negative slope (h) with log
P. The crucial factor for reactivity of these agents with DNA is the electron density on N6. Set
1780 for the enzymatic acylation of X-C6H4-NH 2 which is also dependent on the electron density
on N has a ρ of -2.

At ρ ~ 0.3 to 0.5 we find examples of uncoupling of oxidative

phosphorylation in mitochondria. Other examples of uncoupling occur at ρ ~ 2. The dependence of35

biological activity on σ and comparison with examples from physical organic chemistry has been
recently reviewed.3 Ordering on log P yields a more complex picture.
Another example might be to check the bio system for possible oxidation of anilines that would
likely be associated with σ+ as follows:
1. 12
2. 15
3. 16
4. 2

anilineS+-10<S+<0
B2A

137 hits
14 hits
11 hits
7 hitsMost of the eleven examples involve oxidoreductases with, possibly, radical mechanisms.8
3 . Comparing New QSAR. One of the most important uses of the searching capabilities of
C-QSAR, one which will become of increasing value as the database grows, is that of finding
QSAR that might be similar to one from current research. Range searching is useful in saving timeinspecting the findings; however, it must be used with care since one does not normally know justhow close two coefficients or intercepts (const) must be before one might consider the underlying
mechanisms to be the same. To start with a simple example, assume we have just formulated a newequation linear in log P with no other terms except the intercept (const). If the slope of our equation
were 0.8 and the intercept 1.7 we might select a comparative group of QSAR as follows:
1.
2.
3.
4.
5.

15
15
15
16
16

logP
"_log1/C _ _"not logP' **2 bilin.7<logP<.9
1.5<const<23,600 hits
2,341 hits
573 hits
348 hits
55 hits

Command 2 assures us that all sets have the same dependent variable where C is the molar
concentration of chemical producing a standard end point. Under this condition we can compareintercepts if the slopes are essentially the same.
One might expect to find a rather uniform set of compounds and actions for the 52 QSAR. But,
moving to sh ow and perusing the catch with 1 3 4 reveals a variety of chemicals engaged in avariety of actions. We would term these nonspecific because of the low value of the intercept. An

36intercept of 2 says that when P = 1 (log P = 0) a 10-2 molar concentration of chemical produces thestandard response. For comparison isopropanol has a log P of 0.05. Returning to sea rch andbl anking command 5, then replacing it with 16 4<const<10 and sea rching finds only 37
examples with intercepts above 4. These are for rather complex chemicals (compared to alcohols)
and the activity would not be considered nonspecific. Studies with quaternary alkylammonium
salts are exceptions. These charged molecules appear to disorganize membranes by a rather
nonspecific mechanism. Their very low log P and high potency results in the high intercept.
Searching for similarities in more complex QSAR is illustrated as follows:
1.
2.
3.
4.

15
15
15
15

" log1/C "PI"S
not S + S- SI S ' S . ES MR

3,895 hits
485 hits
140 hits
58 hits

Quotes have been placed on the first example since there are instances where C is not molar andthese have been noted by log 1/C'. Quotes have not been placed on PI since the parameter is often
position dependent and one might want to consider this information. Adding the stipulation that aterm in σ must be present reduces the catch to a small number of QSAR that can easily be viewed
in sh ow, without ordering the QSAR on the slopes of PI or σ, by the entry 1 , 3 , 4 16 .
Molar refractivity has often been used as a measure of substituent bulk. It is presumed that thesteric effect of substituent bulk should reduce activity, but sometimes bulk has a positive effect.
Care must be taken in developing equations to see that MR is reasonably orthogonal with respect tohydrophobic parameters. The following search illustrates how such examples can be isolated forstudy. The whole biological database can be searched with the following commands:
1.
2.
3.
4.

16
15
15
15

.15<MR<.36
not **2 bilin"S
not S + S- S ' SI S .

95 hits
71 hits
19 hits
12 hits

Command 1 sequesters QSAR with modest positive MR terms (note that MR has been scaled
by 0.1 in C-QSAR). The second instruction eliminates the more complex QSAR and finally we37isolate those equations that also contain terms in σ. From the sh ow mode, entering 1 3 4 11 , wefind that our catch consists mostly of a variety of hydrolases operating on aromatic substrates
having substituents (mostly) in the para position that appear to be promoting reactivity by helping
to properly position the substrates on the enzymes.
4 . Log P o (Optimal Log P). Up to this point we have avoided consideration of QSAR
with nonlinear terms. These often may be of primary interest. They appear in two forms: parabolic(e.g., alog P - b(log P)2) or the bilinear model in which activity normally increases linearly to an
optimum and then begins to decrease linearly or level off. Neither set of terms is an ideal solution.
The parabola is a symmetrical relationship and it is often apparent that the relationships are not
perfectly symmetrical. The most unsatisfactory aspect of the parabola in terms of comparative
QSAR is that the slopes are not comparable with linear QSAR. In principle the bilinear form isideal in that the initial (upward) slopes can be compared with linear QSAR. Moreover, it is often
found that an increase in hydrophobicity increases activity only up to a certain point and thenbecomes flat. This is especially true for enzymes where hydrophobic space may be limited. A
serious problem with the bilinear terms is that unless there is a good spread in values of thedependent variable, the slopes have completely unrealistic values. Generally, this is easy to spot
for someone who has had considerable experience in the QSAR field. For instance, it is known
that slopes of log P and π in simple linear equations rarely exceed ± 1.2.3 Despite the unrealistic
slopes, the estimates of the optimum value are usually good when they can be compared with thatobtained via the parabolic QSAR. Searching with 15 logP**2 PI**2 finds 430 examples. Thisis a small fraction of 6,300 and shows that most researchers have not covered hydrophobic spacewith their QSAR. This overlooks logP'**2 and PI-X**2 cases. Replacing the command with
15 **2 locates 713 QSAR with any parabolic terms (including MR, σ etc.), while the command
15 bilin finds 864. Many of the QSAR entered as parabolic equations in the early stages ofbuilding the database have not yet been examined to see if they can be improved as bilinear
equations. In QSAR where the dependence on hydrophobicity is non linear (parabolic or bilinear)
the optimum value for log P or π is indicated by the subscript o (log Po). This information is very
38

important in the design or rationalization of activity of bioactive compounds, and it is instructive to
see how this parameter varies with the biological system. The hydrophobic effect is complex and it
is determined by at least 3 major factors: first the ease of penetration into simple cells or the'random walk' inside more complex organisms; second, the interaction with the final receptor; andthird, metabolism of the compound, especially by the P450 set of enzymes. For example, while
increasing log P may increase the potency of a compound acting on an isolated enzyme, it also may
experience metabolic degradation, leaving less material available to reach the active site in thewhole organism.
To search the database for compounds having log Po, use the following commands:
1.
2.
3.

15
15
17

logP
logP**2 bilin(logP) bilin(ClogP)
1.5<logP<2.5

3,600 hits
965 hits
88 hits

Command 3 narrows the catch to log Po values between 1.5 and 2.5. To inspect the results, moveto sh ow and enter 17 . For parabolic equations, log Po is displayed with its confidence limits,
when it is possible to calculate them. For the bilinear, log B is given as well as the optimal log P.
The confidence limits are found by jackknifing (Section V-G).
One of the advantages of the parabolic model is that an estimate of log Po can be obtained
without having data points on the down side of the curve which is necessary to derive the bilinear
model. Further information on these QSAR can be obtained using the usual codes. 1 3 4 17
displays system, compound, action and log Po.It is instructive to compare log Po for QSAR on
cells with that on whole animals. Entering 2 B4 finds 1,832 QSAR on all types of cells. Then 14
logP** bilin (logP) bilin(ClogP) isolates QSAR where log Po is found (271). Moving tosh ow and entering 3 17 and surveying the results we find that charged compounds (quaternaryammonium and guanidinium analogs) have distinctly lower log Po. When these and those without
confidence limits and partially ionized acids and bases are omitted, the remaining sets have an
average optimum log Po of about 4.3. Repeating the process for vertebrates by entering 2 B6A
finds 174 sets where the mean log Po value is about 2.8. This is significantly less than that for39

cells. We believe that the difference is largely due to the random walk process and metabolism.
That is, log Po is an important measure of Bio availability. However, log Po of about 2 is often,
but not always, ideal for penetration of the CNS, which may or not be desirable.16, 17
This was one of the earliest generalizations of the QSAR paradigm, stemming from thediscovery that 16 examples of CNS agents (barbituates, t-alcohols carbamates) acting on a varietyof animals had a mean log Po value of 1.98. This figure is close to the above finding of 2.8 for avariety of biological end points. That is, log P of about 2 seems to be about ideal for general Bio
availability. This figure could be shifted up or down depending on the nature of the receptor andany special metabolic liability. It does not hold for charged or partically ionized compounds.
However, it is our belief that one wants to make drugs as hydrophilic as possible commensuratewith efficacy.16, 17 Of course, ascertaining exactly what efficacy is in humans is by no means
simple. Short term use of a drug is one thing, but long term use is another. Increasedhydrophobicity allows drugs to penetrate into hydrophobic compartments and disrupt a wider
variety of processes.
The trend to do screening of potential drugs on cells, rather than animals, makes the selectionof rather few compounds for trial in animals difficult. One may be mislead to select compounds
that are too hydrophobic for ideal results in animals.

IV.

Searching the Parameter (THOR-Sigma) Database

A. Substituent Constants
The 'preferred' substituent constants (i.e., the most reliable σ, π, MR, etc.) can be
automatically loaded into regression format as a QSAR is being developed, as will be explained ina later section. However, one often wants to check parameter values of one kind or another for agiven substituent, especially to see how other methods of determination might have led to values
different from those chosen as 'preferred'.
To look up substituent parameter values one accesses the Parameter (THOR-Sigma) databasefrom the $ prompt by entering thor. Respond to the query for database name by entering sigma .

40

For read-only password, press return, and the prompt becomes THOR:sigma>. If, for example,
one wanted to scan the parameters for the trifluoroacetamido substituent, -NHCOCF3, one entersf ind smi * NC(=O)C(F)(F)F , where * indicates the attachment bond (See Section VII for
SMILES tutorial.) This returns the statement: 'found record 20 data items'. These can bedisplayed, under the headings: 'Item' 'Value' 'Label' 'Reference' 'Sel' 'Footnotes', with thecommand 'type table values' entered as: t t v . An asterisk(*) in the 'Sel' column indicates apreferred value. These are the ones that will be automatically loaded in constructing a new dataset
as described in a following section. To save space the Reference and Footnote entries aretruncated, but usually they give sufficient information for initial selection. To see the full entry on
any item use the 'type item number' command as: t i 7 , which in this example returns thefollowing:
SMILESTIMESTAMP
VALUE
LABEL
SEL
REFERENCE
FOOTNOTE
2*N(C=0)C(F)(F)F
1990 Jul 13 08:52:52
0.30
S.META*
Exner,O. & Lakomy,J., Coll.Czech.Chem.Comm., (1970) 35,1371
Apparent pKa of substituted acids (50%Aq.-EtOH or Water @ 25 Deg.)
80% Me-Cellosolve and 50% EtOH

To return to the VMS prompt $ enter quit.
B. Log P and MR: In the THOR Sigma search described in the previous section, it can beseen that the hydrophobic substituent parameter, PI, and the substituent molar refractivity
parameter, MR are listed as Item 5 and 9, respectively. Often it is preferable to have theseparameters apply to the entire molecule rather than just the substituent. The calculations and any
measurements are both conveniently available through UDRIVE.
From the regression mode, enter udrive . The panel that returns on the screen lists a number of
helpful suggestions, but the default in the command line is 'S', a prompt for a SMILES entry.
(Section VII). Pressing return puts the cursor on the SMILES entry line and if, for example,
aspirin is desired, a proper entry, if one elected to begin at the carboxyl group, would be:
OC(=O) clccccclOC(=O)C41

This delivers a panel with the 2-D structure displayed by entering DEPICT. Entry via name isoften easier, and to do this the 'help' screen directs one to enter: F followed by 2 . The upper line
then calls for "name" whereupon aspirin can be entered. Entry via 'name' is even more desirable
for very complex structures, such as strychnine. Occasionally entry via CAS number is thepreferred route, when SMILES is long and the correct nomenclature doubtful, as might be the casewith PCB analogs. In the panel's lower section is a summary of: (1) Preferred Measured log P (ifavailable); (2) Estimated Log P; (3) a statement of Error Level; and (4) the CMR (calculated molar
refractivity). In the right hand panel is a summary of MASTERFILE data that can be accessed.
Entering T (in caps) at the command line returns a prompt showing that log P data is going to beshown by default. (One could enter pka or act ivity here if desired). The table displays 25 log P
values for aspirin measured in 9 solvent systems and at a variety of temperatures and pHs.
Entering t returns the entire THOR page with all the data. Entering quit at any time or return at the'nomore' prompt returns to the main panel, which now has lost the depiction. (One gets it back byentering p). Calculation details can be viewed at moderate length by entering c , or in 'verbose'
form by entering C. Details of the Molar Refractivity calculation are seen by entering M.

V. Regression Analysis: Example 1
For several reasons it is best to begin the regression program while in the proper area, either
database bio or database phys . Searching for similar equations can then be carried out directly,
and if the developed equation(s) is worthwhile, it is much easier to save it in the proper space.
First enter reg ression followed by clear to be sure your workspace is empty. Next assign aname for storage and retrieval; e.g., name Smith-1 . Next enter the title information as in thefollowing example:

42

A. Title Information
T/system mouse embryo fibroblast cells
T/comp ound X-C6H4-NH2
T/action I50 Growth
T/ ref erence Harada,A , Hanazawa,M , Saito,J , Hashimoto,K .
Environ. Toxicol . Chem. 11,973(1992)
T/source Name of person entering data
T/class B4C
Now check by entering sum mary
B.Naming Parameters
The next step in data entry is to name the parameters which one plans to use. Automatic
loading will be demonstrated in this example, and so only the dependent variable need be entered.
Enter getp . The program asks for a label for parameter 3. (As noted previously, parameter 1 isreserved for predicted values and 2 is used for deviation). In the present instance log1/C isentered. The prompt is then for parameter 4, but since automatic loading is to be used, just enterend at this point. If parameters other than those in THOR-sigma are being used, such as M.O.
parameters or pKa, they would be entered at this point. Often data is not in logrithmic form. They
can be entered as such and then converted to log P form by using gett ran.
C. Naming and Entering Substituents
Now enter newsub . This prompts one to enter each substituent label. (In data sets of
miscellaneous structures, the whole name, such as ethanol would be entered at this point.) In thepresent example entering the first label, H , then pressing return, returns a prompt to enter a value
for parameter 3:
Label for substituent 1: H
parameter value 3:
2.73
Next it prompts for substituent label 2, etc.
Label for substituent 2: 2-NO2
parameter value 3:
3.28

43

It is important to note that there must be no spaces within the label. i.e., 2,4-di-CL not 2 4
diCl. After all of the labels and parameter 3 values are entered, enter end .

NH2
X

No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.

Substituent
H
2-NO2
3-NO2
4-NO2
2-NH 23-NH 24-NH 2
2-Me
3-Me
4-Me
2-OMe
3-OMe
4-OMe
2-Cl
3-Cl
4-Cl
2-OH
3-OH
4-OH
4-C2H5
4-C3H7log 1/C
2.73
3.28
3.44
3.49
4.85
3.92
4.68
3.83
3.72
3.70
4.13
3.52
4.55
3.68
3.41
3.89
4.82
4.08
4.70
3.64
3.46

It is advisable to save data entry frequently by entering save . To view Title information, entersum mary. To view parameter data entry, enter seed ata. If entry errors need to be corrected, one
can enter editsub or editdat. Additional details on editing are found in Section I which follows.
Often some of the variation of activity with structure is sensed as one enters the data, andhopefully, some idea of the possibly significant parameters can be obtained. In the present example
we have well-known electron releasing substituents ortho and para to the amino group which areseen to increase toxicity. Note that the two most hydrophobic analogs, 20 and 21, are notespecially potent.
D. Entering Structures via SMILES (Section VII)
If the data set is not based on a parent structure, then the SMILES for each structure must beentered one at a time and auto-loading of parameters is not possible. In that case, entering getsmi

44

provides a panel with a prompt for structure 1. After the SMILES is entered, 'return ' displays the2-D structure. If the structure needs editing, enter y , if not, enter n and the prompt for the second
SMILES appears. Since there are a large number of SMILES stored in the database together withname(s), the name can be entered at this point and the SMILES will be picked up from thedatabase. The name of the compound must be entered exactly as at least one of the storedsynonyms, but the program helps one search for 'near-misses'. Common names have been used,i.e., acetic acid not enthanoic acid; use p-chlorophenol not 4-chlorophenol. This can be a very timesaving procedure for complex structures such as strychnine. When all of the SMILES have been
added enter end or quit.
E. Auto-Loading of Parameters
With the present set of anilines, automatic loading is to be used, and the parent structure isentered via getsmi /p arent. A panel is displayed into which one enters the SMILES with an * foreach substituent position. In the present example substituents are at the 2, 3 and 4 positions, and aproper SMILES for the parent is: Nc1c(*)c(*)c(*)ccl or cl(*)c(*)c(*)ccclN.
Note that the asterisks are placed in parentheses which is how SMILES denotes branching from themain pathway. Pressing return should then display:
NH2

*

*
*

If the structure is not correct, enter y for editing. Deletions and additions can be made to take place
just left of the cursor. When the structure is correct, enter n and the prompt returns to qsar. Next
enter getsmi and the panel returns for entry of the first compound. Enter *H *H *H and'return' to see the unsubstituted aniline. If editing is not needed, then in the panel asking for thesecond structure enter * N(=O)=O *H *H for 2-nitroaniline. 3,4-dichloroaniline would beentered as *H * Cl * Cl . (Note: If a substituent contains a ring, [e.g., OCH2C6H4-4-Me =45

*OCc2ccc(C)cc2] denote it with a higher number for ring closure than any used in the parent.)
After entering all structures enter end (or quit) which returns to the qsar prompt.
It is good practice at this point to enter depict , which will display all structures for a final
check to be sure that the name assigned matches the structure displayed.
Occasionally, it is not possible to enter a SMILES. The structure may not be known for amember of the set. Entering ** is necessary to account for the presence of the compound in the set.
For automatic loading of parameters enter f etch which then shows: Nclc(*)c(*)c(*)ccl where 1,
1 2 3
2 and 3 refer to the ortho, meta and para positions of aniline.
Enter 1 to load parameters for ortho substituents. A list of labels for all possible substituents is
displayed. Since it is not known what kind of electronic effect to expect, we will begin with'normal' S, S+ and S-, accepting the approximation that, in terms of resonance effects, ortho ismuch like para. Therefore we enter 15 16 17 to load σ, σ + and σ − for ortho substituents. The
program returns a report of what it has done. Next enter f etch followed by 2 . Now enter 18 forσm for substituents in the meta position, with the good assumption that for the meta position σm =σ -m = σ +m. For para substituents use f and enter 3 and then 15 16 17 . Now view the data
matrix with seed ata. It is obvious that for each of the parameter types we really need thesummation of all the positions, ortho, meta and para, and this is done by the command
gett ransformation. With this program, parameters can be transformed in many ways as indicatedby the panel after entering gett rans. For example we might want to test a cross product term as σ •π or an exponential term such as (log P)3. Following the instructions in this panel all sorts oftransformation can be accomplished. In the present instance we want to combine positional sigma
constants. First enter the new label S and press return . A panel for the transformation appears
into which is typed from the upper left column: S-P-1 + S-M-2 + S-P-3 a space must be present
on each side of the +. Enter seed ata and the new parameters that cover all 3 positions are shown.
(Note that it will be parameter 11 and on following 'screen'.) Next the process can be repeatedusing gett for the other two types of sigmas: S-P+-1 + S-M-2 + S-P+-3 . and S-P--1 +S-M-2 + S-P--3 . Now the no longer needed precursors can be removed. Enter seed and note46

the parameter numbers of S-P-1, S-M-2, S-P-3, S-P+1 etc. The use of the delete command
solves the problem: del /para followed by the parameter numbers to be deleted (4 5 6 7 8 910). Parameters cannot be deleted if there is a stored equation. del /eq removes it and theparameters can now be deleted.
Hydrophobic parameters must be considered from two points of view: global (log P) and local
position dependent (π). There are two options for automatic loading of the global log P once the
SMILES have been entered. Entering add mlogp loads all experimentally determined values froma carefully evaluated list of over 10,000. Of course this is relatively very small compared to all
possible organic compounds, but sometimes for common chemicals (such as phenols or alcohols)
all desired values can be obtained. Generally the command add clogp is used and the values arecalculated.19 It is extremely important to remember that both add MlogP and add ClogP supply log
P for the NEUTRAL form of the compound. Also it must be remembered that PI (π) values arefrom benzene. These will not be very accurate when the substituents are near a group with a strong
electronic effect (e.g., a NO2 group or a N or O atom in a heterocycle). It can be of value to add
both MlogP and ClogP for comparison to see how well ClogP works for a particular class ofcompound.
Although ClogP is calculated for the neutral form of the solute, these values are often
satisfactory for a set of partially ionized compounds if there is not a great range in the degree ofionization. Of course even if one obtains a reasonable QSAR, the intercept would differ from that
where the apparent log P (log D) was used instead and log PO would need correction. Also using asigma constant can correct for the degree of ionization.
Now entering in order, f , then 1 , followed by 7 11 12 13 , parameters to check on possible
steric effects of ortho substituents. The data set should now be saved by entering save . It can bereloaded by entering load Smith-1 .
F . Permuting
At this point the permutation routine is valuable for uncovering the most important variables.
Enter seep to view parameters:
47

1
YPRED

2
DEV

3
LOG1/C

4
S

5
S+

6
S-

7
MLOGP

8
ES-1

9
10
11
L-STM-1 B1-STM-1 B5-STM-1

All of the steric parameters are labeled 1 when in fact, they are for ortho substituents. Theseshould be edited to ES,2, L-2, B1-2 and B5-2. One must use the dash and not a comma for thesterimol parameters (not B1,2) a comma is used with ES; otherwise string searching finds S-. To
edit enter editp ara 8 . The label is displayed and the 1 is deleted and replaced by 2 . The sameprocess can be used for parameters 9, 10 and 11.
Entering 3 perm 4 5 6 7 8 9 10 derives equations for all possible linear combinations of theeight parameters and then displays the results in tabular form . One could also explore the parabolicand bilinear forms of log P by using P7 or B7 (case is important).

1 TERM REGRESSIONS
S.D.
1 .344
2 .417
3 .447
4 .4595 .571
6 .578
7 .5798 .580

MLOGP

S+
-.73

S

S-

ES-1

-.95
-.44
-.65

0.7

L-STM-1 B1-STM-1 B5-STM-1 CONST
3.73
3.88
3.91
4.37
.17
3.65
0.9
3.66
3.90
.11
3.76

2 TERM REGRESSIONS
S.D.
1 .2982 .316
3 .331
4 .352
5 .352
6 .353
7 .353
8 .394
9 .411
10 .412

MLOGP

-.18

-.24
-.28

S+
-1.83
-1.03
-.61
-.73
-.74
-.73
-.73

S
1.72

S-

ES-1

.74

-.06

-.71
-2.12

.48
.95

L-STM-1 B1-STM-1 B5-STM-1 CONST
3.51
3.58
3.95
.14
3.57
3.71
.04
3.68
.03
3.67
4.14
4.12
3.83

48

3 TERM REGRESSIONS
S.D.
1 .271
2 .3003 .3004 .301
5 .305
6 .306
7 .306
8 .321
9 .322
10 .322

MLOGP
-.200
-.175

S+
-1.753-1.174-1.979-1.978-1.877-1.869-1.857-1.397-1.377-1.376

S
1.803

S-

ES-1

L-2

B1-2

B5-2

CONST
3.751
3.803
3.730
3.609
3.518
3.594
3.513
3.672
3.756
3.587

.731
1.930
1.916
1.795
1.772
1.853

-.102
-.092
.042
-.076
-.084
.847
.831
.855

-.084
-.080
.077

The clue to the relative value of the various parameters is the standard deviation (S.D.). From this
it is seen from the 1 term regressions, that σ + is the single most important parameter. The
coefficient with this term is -0.73 and the constant for the equation is 3.73. Now considering the 2term regression, S is the next most important parameter. However, note that the coefficient with σ+ has changed greatly (σ and σ + are not independent of each other). The most solid parameter is σ
+ which occurs in 7 of the 10 equations. The other parameters show no consistent pattern.

Considering the 3 term regressions, MlogP is the next most important parameter. All of thecoefficients with the steric parameters are very small and there is no pattern in their entry into theequations. Looking at the set of 4 term regressions (not shown) we see that there has been no
reduction in the size of the standard deviation, hence no improvement in the correlation.
We can now check for collinearity of the parameters by entering corr 4 5 6 7 8 9 10 11 toobtain the correlation matrix values of r between the various parameters.
MLOGP
MLOGP
S+
S
SES-1
L-2
B1-2
B5-2

21
21
21
21
21
21
21

S+
.540
21
21
21
21
21
21

S
.537
.967
21
21
21
21
21

S.490
.921
.975
21
21
21
21

ES-1
-.218
-.168
-.243
-.337
21
21
21

L-2
.164
-.085
.013
.068
-.758
21
21

B1-2
.200
.019
.096
.151
-.884
.885

B5-2
.078
-.177
-.075
-.001
-.751
.956
.807

21

49

The upper portion of the matrix gives the correlation coefficient r for the correlation between
each of the various parameters, while the lower portion indicates how many data points thecomparisons are based on. Note that the correlation between the three σ parameters is very high.
To avoid this problem care must be taken in the design of the congener set before starting the
project (Section V-K) or at least to correct it as the project proceeds. At this point it would be a
mistake to use two σ terms. Using σ + and the next best parameter MlogP, enter 3 reg 4 5 toobtain:log 1/C = -0.18 (±.24) MlogP - 0.61 (±.30) σ + + 3.95 (±.33)
n = 21,

r2 = 0.693,

q2 = 0.615s = 0.331

This is not a very good equation; it accounts for only 69.3% of the variance in the data (100×r).
G . Jackknifing
At this point it would be good to know if there are any outliers among the data points. Thesemight be due to the inadequacy of the model, an error in entering data (if it was done manually) oran experimental error in determining the activity of one of the analogs. Another major cause ofoutliers that is very difficult to understand are side reactions yielding a different kind of biologicalresponse. In some instances, it has been possible to resolve those problems.9, 23 To carry out'jackknifing' one enters: 3 j 4 5 . In this operation 21 regression equations are derived dropping ineach instance a different data point. The best equation is the one with the highest r2 after the
dropping of one point. From this operation we obtain:
Omittednone
1H
16 4-Cl
18 3-OH
6 3-NH 2
4 4-NO2

R2
0.693
0.849
0.713
0.702
0.701
0.699

S
0.331
0.211
0.329
0.334
0.336
0.337

Under omitted, none means all 21 data points were included and with the two variable equation, r2= 0.693 as shown above is found. Dropping the parent compound, aniline, yields a QSAR with r2= 0.849 and S.D. = 0.211.
50

To delete data point 1 use the command star /a dd 1 . This places an asterisk on 1 and it is not
used in deriving equations. Any number of points can be withheld in this fashion. star /a 1 5 , 1 0
18 would withhold points 1, 5 to 10 and 18. star /d elete can be used to remove all asterisks in thereverse manner. Or star /d 5 could restore data point 5 (remember that case is not important inentering commands).
Starring 1 yields the following QSAR:
log 1/C = -0.22 (±.15) MlogP - 0.55 (±.19) σ + + 4.07 (±.22)
n = 20,r2 = 0.849,

q2 = 0.783,

s = 0.211

We can explore the possibility that a three variable equation might be worthwhile. Enter 3 reg
4 5 7 which yields:
log 1/C = -0.23 (±.12) MlogP - 1.36 (±.51) σ + + 1.26 (±.77) σ + 3.91 (±.20)
n = 20,r2 = 0.914,

q2 = 0.868,

s = 0.211

Although this equation has a significantly better r2, we would not recommend accepting it until
some kind of support for such a model can be found from physical organic chemistry. The objectof the game is not to get the highest r2, but to get understanding.
One might suspect from the opposite signs of the two σ terms that log 1/C depends
parabolically on the electronic effect of substituents. This can be checked by performing theregression 3 reg 4 p5 which yields a correlation with r2 = 0.886 or 3 reg 4 p6 which gives r2 =0.781. Placing a P before any parameter number derives an equation with this term in parabolic
form while placing a b (e.g., b5) yields a result with the parameter in bilinear form (ref 1, page
195).
Further insight can be gained by plotting the data. Any two parameters can be plotted against
each other by the command a graph b where a and b are the parameter numbers. Entering 3graph 5 it is seen that the 2-, 3- and 4- nitro points fall off the straight line. Deleting these points
by star /a 2 3 4 and then comparing the results shows that 3 reg 4 5 gives just as good an r2 as
3 reg 4 P5 . More work is needed with strong electron withdrawing substituents, such as CN, to
see if the problem is electronic or if the NO2 might be undergoing reduction in the cells. It is51

readily reduced to the toxic nitrenium group. We have also found in possible radical reactions ofphenols that strong electron withdrawing substituents block the reaction.22
At this point we need to look for support from other QSAR. Enter database Bio or Phys andthen sea to get the search program. To find other comparable Bio QSAR enter 15 S + and sea .
Nar rowin g the sea rch to equ ation s hav ing coe ffici ents nea r our s by sea rchin g on 16 - . 7 5 < S +< . 4 5 reduces the list to 59 having values of ρ (slope) bracketing that found in our 2-variable
equation. Enter sh ow and 15 to inspect the results. So doing we find a number of QSAR having
parabolic or bilinear terms in S+. These can be deleted by returning to sea rch and entering not
S+**2 bilin (S + ) leaving 54 with linear S+ terms. Moving to sh ow and entering /sort=16 1 3
4 15 16 18 and then S+ on the prompt gives a list ordered on increasing values of ρ.
Equations of interest are found for developmental effects of phenols to rat embryos in culture,phenol toxicity to P. aeruginosa, phenol toxicity to P. posphoreum, phenol complexing with NAD
and phenols inhibiting Listeria. These QSAR contain only a term in σ + for which the ρ values are:-0.56, -0.58 and -0.65. Thus we find the developmental (toxic) effect of the phenols to depend on
σ + in a parallel manner to the toxicity of the anilines to mouse embryo cells. A fourth QSAR is
found for phenols, the endpoint measured as a 10g weight loss in rats. Weight loss in pregnant ratsshows parabolic dependence on logP and contains a σ+ term with ρ = -0.61. Adding log P +(logP)2 to the rat embryo equations also makes a slight improvement, but with only 11 data points,
one hesitates to rely on such an equation. More data (20) were available for the weight loss
correlation. Of course, the above results do not prove that anilines and phenols operate by similar
mechanisms, but they are suggestive.
We can now turn to the physical database for further insight. Enter reg to go to qsar and thendata phys when asked for the password press return . This returns the main menu. Enter sea forthe search mode. We now repeat the routine used for the Bio search. Entering 15 S + locates
1,656 sets. 15 not S+ **2 bilin (S+) removes the non linear equations, leaving 1,579
examples. By searching on 16 - . 7 5 < S + < - . 4 5 this is reduced to 136 sets. These can be perusedin the sh mode by the entry /sort=16 1 3 4 1 5 , 18 . However, since we suspect a radical52

reaction the 136 hits can be further reduced by 2 P12 which yields 50 cases. Viewing these wefind one of special interest: radical abstraction of •H from phenols (X-C6H4OH + (CH3)3CO• →X-C6H4O• + (CH 3)3(OH) with a term of -0.71σ + . In fact, there are many examples of H
abstraction by radicals which are correlated by σ + of which the following are representative for CH bonds.
Compound
X-C6H4CH2CH=CH 2
X-C6H4CH3
X-C6H4CH2C(CH3)3
X-C6H4CH2C6H5Reagent
Br•
Cl•
Br•
Br•

ρ+

-0.75
-0.73
-0.72
-0.62

H . Cross Validation
The value of cross validation in avoiding poor quality or meaningless QSAR has been
discussed by Cramer et al. 24 We have included two ways of viewing the problem. The programautomatically calculates a cross validated r2 (Q2) and prints this out with each equation. One wants
to see the normal r2 and Q2 in 'reasonable' agreement, but exactly what amounts to reasonable is amatter of judgment. If they are not close (say r2 = .90, Q2 = .7) then jackknifing to remove one ormore points may be worthwhile.
Q2 is basically a kind of warning that the QSAR may not be as good as implied by r2. In somefashion the data are not well fit. If this is due to an outlier or two, jackknifing can be used to findthe deviant point(s). More complex problems, such as nonlinearities in the model may be hard tofind.
In another approach one can set one's own standard. For example, in the above case of theanilines using the command 3 cross 4 5 randomly drops 10% of the data points in 10 trials anddisplays the QSAR. In these examples r2 ranges from 0.807 to 0.896.
One can also set an arbitrary percentage drop: 3 cross /omit=30% 4 5 which yields 6 QSAR
with r2 from 0.803 to 0.896. In this example, 30% of the data points were randomly dropped. Any
percentage can be selected by inserting the proper number along with %.

53

In the above examples it is found that the coefficients with MlogP and σ + vary but little fromour first QSAR with only one point omitted.
I. Editing
There is always the need to change data values or correct errors, and the most general way to
do this is by entering editset . This displays all the data and puts one into a general edit mode
allowing: (1) movement of the cursor by the arrow keys, (2) the use of the delete key to erase thecharacter to the left of the cursor, and (3) the insertion of new characters at that spot. Of course acompletely new variable could also be added by first assigning it a symbol and then entering thevalues. When finished, one must exit this edit mode, using control Z. When making minorchanges a quicker mode is available. Entering editdata prompts for the substituent number.
Entering that number then prompts for the parameter number, whence the current value isdisplayed. When this is edited, a box for the new parameter value is displayed. After editing iscompete, the command seed ata enables one to check the results. Other editing choices are shown
after entry of edit .
After experimenting with a wide variety of parameters including squared and bilinear ones
which have been added to the data matrix, it is convenient to use the delete command to clean up
the set before saving it. However parameters cannot be deleted if an equation has been saved. To
check for saved equations, enter eq /run . All equations can be deleted by del /eq # . Now
entering seep arameter the parameter numbers are displayed. The command del /para 4 8 16
would remove these parameters. Sometimes it may be necessary to delete a data point and theinformation associated with it including the SMILES. The command del /sub # removes thesubstituent and its SMILES with that number.
J. Regression Analysis: Example 2
We now consider a data set (Bio #1557) based on a study of benzanilides (XC6H4CONHC6H4-R) inhibiting mitochondria by G. A. White. The set can be loaded for study
when in the Bio mode by entering load /d 1557 . Entering sum mary gives a view of the set
including the parameters that were studied in the derivation of the equation. Entering eq /run54

displays the stored equation. Entering pre dict shows the parameters used to derive the equation,
substituents and calculated values. In the summary under compound it is indicated that substitution
has been made on both rings; one labeled X and the other R. Dep ict , pictures the 2-D structures
which can be paged through by pressing return . A particular structure or set of structures can beviewed by depict # or depict 1 3 , 1 0 ; for example.
Describing how to go about formulating a QSAR is rather like giving directions for solving a
jigsaw puzzle. There are many ways to go about it and they depend on clues one gets from
inspecting the scene. In the case of the QSAR one does not usually have all of the pieces tounambiguously picture the roles of steric, electronic and hydrophobic properties of all positions on
a parent molecule.
A good first move is to check for a role for hydrophobic effects since 50 to 80% of the Bio
QSAR depend on log P or on π at some position on the parent. In the present case entering 3graph 4 one sees not only a strong nonlinear relationship, but evidence for a bilinear effect. By
starring various points one can check up on the behavior of subsets of the data. For example wemight compare each of the two rings independently. First enter star /d to free all data points. Thenentering 3 reg B4 yields the bilinear relationship:log 1/C = 0.99 (±.34) ClogP - 1.73 (±.74) bilin (ClogP) + 1.41 (± 1.2)
n = 34,r2 = 0.542,

s = 0.682

optimum = 5.62, β = -5.49

Activity initially increases with increase in log P with slope (h) of 0.99 and then falls off linearlywith slope of -0.74 (0.99 - 1.73). The optimum (log Po) listed above is actually rapidly calculated
from the parabolic equation. To get the exact value enter 3 j B4 . The optimum shown here is
5.19.
The above bilinear term actually represents: -1.73(±.74)log(β • 10ClogP + 1) where β = -5.49.
Generally researchers use the form log (β • P + 1). Since many of our parameters are on a log
basis (σ, π, E s, MlogP) or are calculated as such ClogP, it is simpler to use log (β • 10 x + 1)
where X is in logarithmic form.

55

Note that the Dev + and Dev - which represent the number of points below and above the regression line are not badly out of balance. Also the 95% confidence limits on the coefficients are
not unreasonable. Although the correlation is not high in terms of r2, it seems promising. Next weneed to see the effect of adding the steric and electronic terms. Inspecting the substituents we see
only one example (#15) that contains a strong electron withdrawing via resonance substituent.
Hence it is not surprising to see the QSAR does not contain a σ - term. It is of interest to see that
there is no electronic term for X-substituents. It is quite possible that the person entering this set or
any other of the sets in the database may have overlooked something. The best way to consider thispoint is to study X substituents alone. Enter star / a 8 , which stars all substituents above 7. Now
enter 3 perm 4 5 6 7 14 15 16 . The most important variable is Es - X2 followed by Clog P. A
very small lowering of the standard deviation occurs on the addition of B1-X, but a 3 parameterequation is not justified by 7 data points. The two variable equation is:log 1/C = 0.73 (±.47) ClogP - 1.24(±.32) Es-X2 + 0.97 (±1.4)n =7,

r2 = 0.973,

s = 0.113

q2 = 0.923

At this point recall that all substituent values of Es are negative, that is, the Es term implies thatbulky substituents are good! Note also that all of the log P values for this subset are much below
log Po of 5, so that linear dependence on ClogP is expected. Deriving single parameter equations in
ClogP and ES-X2 one finds that the latter is more important. The coefficients are in reasonableagreement with the stored QSAR.
Since there is significant variation in the properties of X, the fact that useful electronic terms are
not found might be due to a collinearity problem. Enter corr 4 5 6 7 14 15 16 which provides
the correlation matrix of r values between the various substituents. The correlation (r) between ESX2 and the electronic parameters is not high. Entering 3 reg 4 14 shows that, indeed, electronic
effects do not appear to be significant. The substituents studied in this position are rather small.
Our results suggest that using larger hydrophobic substituents would yield more potent compounds
as long as we do not exceed log Po of 5 for the whole molecule. It would be interesting to study
the isopropyl and t-butyl groups.
56

Caution must always be used when working with the bilinear terms. Unless there is a goodrange in the values of the dependent variable unreasonable coefficients may be found for theseterms. Sometimes the numbers are so large the problem is obvious. In the case of log P or π alarge amount of experience teaches that the coefficient (h) is rarely outside of the range of ±1.2.3
Large negative slopes (more negative than -1.2) are sometimes due to steric effects of large
substituents when log P and steric terms are collinear. The big advantage of the bilinear model isthat one can compare the slopes, especially the initial slope, with other QSAR which are only linear
in log P, for example.
Now to consider the other ring enter star /d followed by star /a 1 , 8 3 2 , this provides a set
in which all substituents on the X-ring are constant having only a 2-methyl group. Entering 3
perm B4 8 13 17 18 finds only the parameters of the stored equation to be significant andentering 3 reg B4 5 8 13 yields a very similar equation to that stored, but with a lower r2.
Destarring all data points and then removing two by the jackknifing procedure produces the storedequation.
Considering this equation what should the next move be? Obviously the negative sign with B1R2 shows that ortho substituents in this ring are bad. Surprisingly no para substituents were
tested. The negative coefficient with σ (S,R) shows that electron releasing substituents are
beneficial. One of the best would be 4-NH2 (σ = -0.66) or 4-OH(σ= -0.37).
If further testing showed that the 2-isopropyl or 2-t-butyl groups on the X-ring increased
potency, then one of these could be combined with the 4-NH2 in the R ring. In addition, it would
be necessary to add alkyl or alkoxy groups in the 3-position of the R ring to obtain a log P value
near 5 for maximum potency.
To gain experience, the beginner can enter any dataset by load /d # where # is the set number.
Unfortunately parent SMILES have only been provided for the more recent entries. We plan tocorrect this deficiency (above #3500 in physical databank and above #3000 in the Bio databank).
Once the set has been loaded one can explore it as we have done with the above example.

57

K . Substituent Selection in Molecular Design
A critical problem in undertaking a program to modify a parent compound in a structure-activity
study is the selection of suitable substituents.1 Not only does one need convenient access to thewidest possible variety of substituents, but it is time saving to be sure if a particular substituent isselected that it has a range of known substituent constants (σP, σ P+ , σP−, π, MR, etc.). At the startof such a process it is generally not possible to anticipate which parameters will be significant in
the final QSAR. To utilize this feature of the C-QSAR program there are two commands that can beentered from the regression mode: parameter, parameter / nolimit . Entering parameter produces
a table of labels (same as the f etch command). Any group can be selected. For example, entering
15 16 17 (σ, σ + , σ−) the prompt asks for the minimum and maximum to set a range for eachparameter. We might enter 0 1 0 1

0 1 to define the range for each of the three parameters.

After the data have been collected, enter seed and we find that only 21 substituents fall in thisrange.
In selecting a set of substituents for combinatorial synthesis it can be very important to limit therange of π or possibly other parameters. Thus for π, one might set limits such as -1 to 3 for π andlet values for the others fall where they may by simply pressing return to pass the options by. If
one had pressed return at each prompt so that no limit had been set and then entered seed , 79
substituents are found. Notice that when entering more than one parameter, the data are ordered on
the first parameter called for. One can stop the listing at any point by entering q.
Entering parameter / nolimit asks for no limits. This saves time in specifying limits whenmany parameters are requested. Entering seed shows the same 79 substituents. One can stop thelisting at any point by entering q.
Entering parameter / nolimit and then selecting 1 2 12 15 18 followed by seed produces aset of 256 substituents ordered on increasing values of π (first parameter selected) all of whichhave π, MR, B1, σp and σm values. Since this operation is in the regression mode thesesubstituents are loaded for regression. Enter seep and we find the parameter labels. Entering 3

58

reg 4 we find r2 for the correlation between π and MR (r2 = 0.127). Entering corr 3 4 5 6 7 we
obtain the following correlation matrix for the set of 5 parameters in terms of r.
P1
P1
MR
L
S-P

MR
.357

B1
.161
.292

S-P
-.225
-.052
.343

S-M
-.275
-.106
.250
.918

The above approach may be sufficient, but especially in Combinatorial Synthesis one wants tobe sure that data space is well explored and collinearity minimized. To deal with this problem enterparameter /E /P (E denotes euclidian space and P stands for pick). From the displayed tableselect parameters of interest and all substituents having all of these parameters will be sequestered.
In the present instance enter 1 11 12 13 15 18 for π, B1, B5, L, σP. These are entered inregression mode. Enter seep this shows euclid P1, B1, B5, L, Sp with their numbers. Entering
Corr 4 5 6 7 8 yields the correlation matrix. Entering seed lists the substituents in order ofincreasing distance from H. The collinearity between any two substituents can be checked. seep
provides the parameter numbers and 4 reg 8 shows that the collinearity between π and σP isalmost 0 for 294 substituents. Next, enter star /A which stars all substituents. Now use seed toperuse substituents picking the numbers of those of interest in terms of their ease of synthesis anddistance in euclidean space. Then enter star 1d followed by the number of the selected
substituents. Now enter corr 4 5 6 7 8 or any combination of these to explore the collinearity
among the selected substituents.
Another way to select the most effective substituents to break collinearity enter parameter/ nolimit . Next, select 15 and 16 from the table of parameters. Since you are now in regressionmode, 3 reg 4 yields the correlation equation for two terms and also informs one as to the numberof substituents having both σ and σ+ (199). Next, use gett and enter S + - S for the name of the
new variable. This then is defined on the prompt by entering S-P+ S-P . This becomes newparameter 5. The command sort /null / abs / des and then 5 on the prompt orders the59substituents in decreasing size of difference. Of course one could treat any number of parameters inthis fashion.
The default approach can be had by the command parameter / nolimit /E . This selects
automatically π, MR, L, B1, B5, σP, σP+, σP-. seed lists only 61 substituents having all of theseparameters. The reason for this is that there are relatively few values for σ+ and σ -. However,these electronic parameters are very valuable in providing mechanistic insight via QSAR3, 8, 9, 47
and should be studied separately. There is a high degree of collinearity among them. In a way thisis helpful as it means that σP can detect the presence of electronic effects that later can be exploredwith the other two parameters.

VI.

Searching for New Leads

The most difficult and important aspect of medicinal chemistry is finding new lead compounds
for drug development. Strange to say, they are all around us, and yet almost impossible torecognize. A good example is the conversion of nalidixic acid, a mediocre antibiotic, into aquinolone carboxylate. QSAR played an important role in this process.11 The old estrogenic
sedative thalidomide now looks promising for treatment of leprosy and multiple myeloma.
Improved "me too" drugs are always coming into the market. The great excitement at present iscombinatorial synthesis. Of course with our present C-QSAR system, one can select any subject
and study it for possible clues to increase potency or decrease toxicity. Since the vast majority ofthe bio QSAR in our system were developed by us from data published by others in which noattempt to formulate a QSAR was made, it is highly unlikely that the most active congeners werediscovered. A selective approach is to find QSAR that cover highly active compounds. The
dependent variable log 1/C has been entered in molar terms whenever possible, hence log 1/C = 9means activity at 10-9 molar concentration.
A selective approach is to consider QSAR that cover highly active compounds. Two searchmodes are possible:60

1. 14 log1/C>9 makes 2 hits. In these instances, all members of the set have log 1/C values> 9.
2. 14 log1/C@max>9 finds 243 data sets. In this case, any set having a congener with log
1/C greater than 9 is selected. Lowering the standard to 8, we find 17 and 708 sets
respectively for the two searching modes. At log 1/C of 7, we find 118 and 1,334 sets.
Searching the entire bank with 15 " log1/C " 3,906 sets are characterized by molar log
1/C terms.
These approaches can be generalized in the following ways:
3. 14 log1/C<2866 hits

4. 14 2<log1/C<4

212 hits

5. 14 logP>6

24 hits

6. 14 logP@max>6986 hits

7. 15 not logP**2

5,940 hits

15 not bilin (logP)

5,760 hits

15 not bilin (ClogP) 5,414 hits
15 not logP'

5,232 hits

8. 14 logP@max>6986 hits

14 6<logP<8

1 hit

The above table lists the number of different examples and the range of some of the morecommon classes of substituents that provide all sorts of opportunities for modification and testingof a known QSAR.
As the database increases, it becomes more arduous to find exactly what one wants to study.
There are several ways to make searching more selective. Besides setting a high level of activity
one can limit the search to biological areas of interest. For instance, we might search on organelles
and cells by entering 2 B3 B4 . This makes 2,232 hits, about 1/3 of the database. Next, selecting
the cutoff at log 1/C = 7 by 14 log1/C@max>7 reduces the catch to 584 sets. Going to sh ow,
we see that these are listed in order of increasing size of set number. To peruse recent work, search61with 5 (1990) (1991) (1992) (1993) (1994) (1995). This reduces the number to 211. A quick
check of the catch finds many sets for HIV. To eliminate these enter 1 not HIV . This cuts thenumber to 158. Once a compound of interest has been found, the MERLIN search can be
employed. Going to sea and entering 13 and then on the prompt entering the SMILES forN
N
H

and then searching, locates 172 sets that contain one or more compounds having this moiety.
Moving to sh and entering 1 3 4 the main features of activity can be scanned in a few minutes.
Some sample hits are as follows:
C6 H5

N

X

O

XC6 -H4NCOO(CH2) 3

HN

R

HN

N

N

N

N
H

O

X

N

I50 C- RAF
Protein Kinase

Antagonistic activity
vs. synaptosomes
Rat cerebral cortex

Displacement of
cyclohexyladenosine
from rat cortex receptor
X

N

N
SCH2
N
H

Mic vs. Helicobacteria PyloriO
XHN
X C6H 4NN
H

I50 Thymidine Kinase

CH2

HN
C
S

N

N Y
CH2 HC

CH3

I50 to protect MT-4 cells
from HIV

Searching on the following structures finds the indicated number of hits for sets that containone or more compounds having the indicated feature.

62

O

CH3 CNH2

1,450

H2NCONH2524

H2NCSNH 2

108

N

N

HN

N

OH

N
S

OH

22

771

821

550

N

196

519

17

S

181
N
H

N
H

356

H2NC(=NH)NH 2

190

Searching can be made much more specific as illustrated in the following examples. Aftersequestering 821 pyridines, we can narrow the search to more specific points of interest. Using 2
B2 finds 356 QSAR on enzymes and receptors. Finally, 15 S + narrows this to 6 that contain σ+
terms.
Table 7
range

number of substituentsπ-5.96 to 10.3

1088

0.1MR
σp

0.09 to 12.3

1536

-1.58 to 2.42

1999

σm

-0.53 to 2.18

1234

σp

-7.2 to 1.88

397

σm+

-1.15 to 3.20

75

σp

-

-0.82 to 5.40

375

m

σ

-0.33 to 2.25

92

σ*-5.40 to 7.6

887

σI

-0.96 to 1.53

998

σp

+

•

-0.08 to 0.90

26

•
m

σ

-0.12 to 0.03

13ES

-8.68 to 0

249

B1

1 to 4.65

1078

B5

1 to 12.17

1077

L

2.06 to 17.2

1083

63

Because of the relatively few examples of σm+ and σm- we have normally made the assumptionthat σm = σm+ = σm- . We can now obtain some idea of how valid this is. Using parameter/ nolimit and entering 18 19 via seed , finds 45 instances where values for both σm and σm+substituents are known. Enter seep to get parameter number. Next 3 reg 4 shows that r2 =0.796. Using 3 j 4 and starring the bad points we can eliminate three outliers: O-, N + (Me)3, 3thienyl. Correlation of the remaining 42 finds r2 = 0.915; s = 0.113. Note that by using theregression 'ploy' one can quickly ascertain the number of substituents that have values for any pairof parameters (in this case n = 45). If one had used seed and retrieved several hundred, it would
require scrolling through to find matching pairs.
Repeating the process for σm and σm- it is found that 76 substituents have both parameters andthe correlation between them has r2 = 0.930. OmittingNO

and NH3 + the remaining 75 examples

correlate with r2 =0.962, s = 0.084.
At present there are 366 substituents for which both σ* and σI are known. Removing the fourmost poorly correlated substituents (-(CH2)3P(=O)(OEt)2, SO2CH=CH 2, 9-carbazolyl andB(OH)2 we find r2 = 0.911, s = 0.073.
The most useful σ• or ER values for radical reactions are σ• for Creary and ER, simply because
more of these are available. Values are also given for σ• determined by Arnold and by Jiang.
Sterimol parameters (B1, B5 and L) are known for a number of substituents that have cis andtrans forms. At present, for technical reasons, we can not enter values for both, hence we have
only given values for the trans forms. The cis values can be found in ref. 2.
We have greatly enlarged the number of σp and σm values in our system by means of thefollowing correlation equations obtained from experimental values for -C6 H4 -4-X and -C6 H4 -3-X.
σ(-C6 H4 -4-X) = 0.310(Ò0.023)σp + 0.026(Ò0.009)
n = 35,r2 = 0.956,

s = 0.026

σ(-C6 H4 -3-X) = 0.238(Ò0.020)σm + 0.012(Ò0.006)
n = 23,r2 = 0.968,

s = 0.012

64

Inserting any σp or σm value into these expressions yields a value for the substituted phenyl
moiety. To study the development of any QSAR in the system that has the parent SMILES entry
one loads it to reg by set number. If the set does not have parent SMILES (check by enteringf etch) then all SMILES must be deleted and the parent entered. Next, check for the relative
importance of the various parameters. Naturally if the parameters are nonlinear then one has themost to gain by maximizing them. The equation should be printed out along with the numberedparameters (seep ). Delete the current parameters after deleting the stored equation (del /eq ).
Now enter newsub and receive the prompt for the first new substituent. Enter the symbol and on
the prompt for the parameter value, enter a period (. ) (this is to be calculated). After all new
substituents of interest have been entered, enter end . Now the SMILES for the new derivatives
must be added via getsmi . This provides the prompt for adding the first new substituent to theparent SMILES. When all the new substituents have been added, enter end or quit. At this point,
it is good practice to enter depict , to check all entries to see that no errors in entry have occurred.
Now one must enter a new set of parameters for those that were deleted via the f etch command.
(Section V-E).
Of course, one is searching for the limits of known substituent constants to maximize activity.
At the same time, care must be taken to minimize collinearity. The approach in Section V-K. can bemost helpful. For illustration, consider the example of a QSAR with the following independent
variables: log P, log P2 , σp , σm, B1. Of course, log P values can be calculated from the SMILES,
but in selecting substituents, the need is to select those that have π values which, when added toparent log P, do not yield a log P much beyond log PO or at least the upper confidence limit on log
PO. For parameter selection assume σ is most important to consider (we already know the limit onlog P). We need to consider both σp and σm so that the two can be summed to get the overall σ.
After entering parameter we see the list to select from. Selection will be ordered on the firstparameter entered that would be σp . Select 15 18 1 12 . On the prompt for limits assume we havetested σp up to 0.5. Hence, enter .5 and 3. Any large number can be selected for the upper limit
simply to insure that all substituents with large values will be covered. We can use the same limits
65

for σm. For π the decision will have to be made after one calculates what the ideal value of π should
be. For the present, we might set -.4 and 3 and then 1 and 3 for B1. One must set some kind ofreal limit for each parameter. For example, setting 0.5 and 4 for B1 the search fails, as the smallest
value for B1 is 1 for H. Entering seed we find a catch of 19 substituents. The most interestingmight be SO2 CF3 with σp of 0.96. That is, if B1 has a positive coefficient in the QSAR and its π of
0.55 is not too high. Of course, two substituents e.g., 3,5-di-SO2 CF3 might be used if thesynthetic chemists did not object too strongly!
To check for collinearity, enter corr 3 4 5 6. From the correlation matrix, it is apparent that
only σm and σp are highly collinear. In the present case, this makes little difference since ourinterest is in getting the highest sum of σp and σm possible. This could be very important if one
were trying to establish the importance of σ - or σ+ for mechanistic perspective.
To further be sure that we are not facing a collinearity problem at a lower level in selecting
SO2 CF3 we can use the regression program since parameter has placed us in that mode. The
following regressions can be run: 3 reg 5 ; 3 reg 6 ; 5 reg 6 . In each case, using pred allows
us to see the correlations between individual substituents. For more complicated problems, pred/ des / abs can be used to find the most noncollinear examples from an inspection of thedeviations.

VII.

SMILES Tutorial

Inventing the SMILES language for linear entry of complex structures of organic chemicals
into computers was a major achievement by David Weininger.25a,b S implified Molecular Input Line
Entry S ystem has four basic rules that cover 98% of all structure notation. These can be learned ina few minutes, but of course it will take some practice to become proficient. One can practice byentering udrive from either regression mode. It is a bit more convenient to enter from the sea rch
mode by entering 12 . In the displayed panel one can either enter the common name ascorbic
acid or the SMILES to see the 2-D structure. If the common name was used entering n goes to the66search mode and pictures the SMILES. Entering sea completes the search. The following
examples illustrate the four basic rules.
1. Use ordinary atomic symbols.
a. B, C, N, O, P, S, F, Cl, Br, I.
b. Suppress hydrogen except on pyrrole nitrogen and derivatives such as indole carbazole etc.
In these instances [nH] is used. The small n represents aromatic nitrogen.
c. Other atoms and any charges are placed in brackets; e.g., [Si]; [N+ ]. Also use H to fill thevalences of non-ordinaries; e.g., dichlorosilane: Cl[SiH2]Cl.
d. Use upper case letters for aliphatic atoms and lower for aromatic, but the second letter ofany symbol is always lower case; e.g., [Sn] for aliphatic tin; [se] for aromatic selenium.
e. For non-linear structures, branches are enclosed in parentheses.
Examples:a. CH3CH2OH

CCO

b. CH3CH2NHCH 3

CCNC

c.

n1ccccc1 1 is to connect n and c to form aromatic ring

N

d.
e.

C1CCCCC1NH

c1c[nH]cc1

f. Pb[Cl]4[Pb](Cl)(Cl)(Cl)Clg. DCCl3 (deutero chloroform)[2H]C(Cl)(Cl)Cl (Isotopic mass number precedes atom)

2. Bonds need not be specified except:
a. Equal sign (=) for double bond.
b. Pound sign (#) for triple bond.
c. A period separates disconnected structures; e.g., ions, complexes, or reactions between
two compounds. An asterisk represents the bond of a substituent to its parent.
Examples:CH3 C ≡ N

CC#N

CH3CH2COO- Na+

CCC(=O)[O-].[Na+]HC ≡ CNO2

C#CN(=O)=O-SO2NH 2 substituent*S(=O)(=O)N67

Azido substituent*N=[N+]=[N-]Ammonium substituent*[N+H3]
Reaction between 4X-Benzoyl Chloride + 4X-Aniline

Cl

O
C

NH 2

clcc(*)ccclC(=O)Cl.clcc(*)ccclN (see p. 27).

**
3. A branched group is placed in parentheses.
a. Branches can be nested if desired.
b. Can follow any path desired; will be made unique for storage.
Examples:

CF3CH2C(=O)CH3

FC(F)(F)CC(=O)Cor C(F)(F)(F)CC(C)=O

H 3C

CH3
O
O
CH3

O2N

C6H5N+ ≡NBF 4-

CC(N(=O)=O)CC(C)C(=O)OCclcccccl[N+]#N. [B-](F)(F)(F)F

4. To make a ring pathway linear one bond must be 'broken' for each ring.
a. One numbers the atoms on each side of the break with the same number.
b. Any number can be reused after that ring is closed.
Examples:Benzeneclcccccl

2-amine, 4-nitrotoluene

CH3
NH2

Cc1C(N)cc(N(=O)=O)cc1(start with one of
the substituents)

NO2

Note that N is either trivalent or pentavalent (NO2 is written as N(=O)=O); (CH3 )3 N as
CN(C)C; (CH3 )3 N-O
2

C=N

CH3
O as C=N(=O)C.

1
1

c1ccc2ccccc12 or clc2ccccc2ccc1
2

CH3
Na+ O

CHOH

2

1

CC(O)clcccc2c(C(=O)N)ccc(cl2)[O-]. [Na+]
O C
NH2

68

In some routines (e.g., getsmi ) the SMILES for many complex structures can be retrieved byname, if present in MASTERFILE. For example, it is much faster to recover a SMILES forstrychnine by name, strychnine , than by entering SMILES atom by atom. However, the lattertask is not too difficult if one chooses a path which first follows the periphery of the structure andthen leads into the center. Beginning arbitrarily at the top of the benzene ring as shown, the lower
case 'c' is followed by the number '1' showing that it will later be joined to the carbon fused to thepyrrolide ring (that 'c' will also be followed by '1').
N

6
start

1
7
end

2

5

4
N

3
O

O

SMILES entry for strychnine
As the path follows the periphery, bonds to the interior are 'broken' and numbered insuccession, until the path enters the interior at the quaternary carbon and the seventh and last 'ring
break' is made. (Note that the highest number should equal the number of rings present.) The
SMILES for this path is: clcccc2N3C(=O)CC4OCC=C5CN6CCC7(c12) C6CC5C4C37. Before
entering the string, it is a good practice to check to see that each numeral appears as a pair. Notethat before the path enters the central region, it branches to make connection to the beginningcarbon (at c12). Note also that these numbers refer to 'one' and 'two' and not 'twelve'.
Numbering the bond breaks is sufficient to "keep score", and it is more legible in small diagrams
than numbering both sides of the break as was done in the previous example. It takes a few hoursof practice to become comfortable in entering complex structures via SMILES, but if one canhandle the strychnine example, there are few in all of chemistry that will be more formidable.

68When entering structures in a QSAR using the getsmi /parent routine, and both the parent
and substituent contain a ring, remember to begin the substituent ring with a number higher thanused in the parent. The following example with the adamantyl as a substituent on, for example,
benzene will illustrate this:getsmi / parent

c1c(*)cccc1

For the 1-adamantyl substituent the getsmi entry would be: * C23CC4CC(C2)CC(C3)C4
For the 2-adamantyl substituent it would be: * C2C3CC4CC2CC(C3)C4
*
2

2
3
4

4

1-Adamantyl

32-Adamantyl

As Shown by DEPICT:
CH2

CH

H2
C

H3
C

CH

C

H2
C
CH

CH

C
H2

CH

CH
CH

CH2

CH2

CH2
C
H

CH2

CH2
C
H

VIII. Substructure Searching
A more general method of searching for compounds of interest is that of substructure
searching. When a desired substructure is provided, the program finds all compounds in the
database where a hydrogen atom of that substructure can be replaced with another group.
Merlin is entered from the Bio (or Phys) regression mode. A panel is presented in which the
SMILES can be entered or, in many cases, the compound name. If the structure is correct, choosen and the program sequesters all derivatives in which a hydrogen has been replaced. Entering

68depict , displays the 2-D structures. Entering depict 1 , 1 0 would show the first 10, etc. or the
depiction process can be stopped at any point by entering q.
Carrying out this process for aziridine in the bio database (entered as C1NC1 or by name)finds 260 examples of which the following are representative:
O

O
R2

N

N

R1

CH2 OCONH2

R

N

OCH3

N

O

NH

NO2

C(O)NHR

NO2

O

N

O

O=P-NHCOOR
N

N
S=P
N

R
N
R

Note that even when azridine is fused into a second ring system, it is also captured. Carryingout the same operation from the Phys mode finds 48 examples and of course searching from data
double 308 hits on aziridines are made.
One of the shortcomings of the present system of simplified substructure searching is that it
often finds too many examples for consideration. For example, in the Phys reg mode enter merlinand c1ccccc1C#N to find derivatives of benzonitrile. Choosing n starts the search and, on theprompt asking whether or not to continue, entering y , sequesters 600 derivatives of benzonitrile.
Enter depict , displays them 4 at a time. It would be rather laborious to look at all 600 structures,
but not impossible. If we limit the search by asking for derivatives of 4-chlorobenzonitrile(SMILES N#Cc1ccc(Cl)cc1), only 4 examples are found: 4-chlorobenzonitrile and 3 derivatives
which contain 1 other substituent in addition to 4-Cl.
Searching for derivatives of chloramphenicol in the bio mode using merlin is illustrative. One
can enter the SMILES for the analog without the nitro group, but a simpler method is to enter

68

chloramphenicol by name, choose y for editing, and then delete the N(=O)=O at the end of the
SMILES string.
OH

OH

CHCHCH2 OH .

CHCHCH2 OH .

NHC(=O)CHCl2

NHC(=O)CHCl2
NO2

Chloramphenicol

In the resulting search we find 20 examples with substitution on phenyl ring or the C of
CHCl2. If we wish instead to study only the variations on the methyl of the acetamido group, we
can enter the SMILES for :OH
CHCHCH2OH
NHC(=O)CH3
O2N

Clcc(N(=O)=O)cccl C(O)CO)N(=O)CNow 3 examples are found and are viewed by entering depict 1 , All are 4-NO2- phenyl
analogs with variations in the side chain. Searching on chloramphenicol makes only 2 hits:
chloramphenicol and the trichloroacetamido analog.
We can search the double database (see Section VIII) from the search mode by entering [ Te] .
So doing finds 55 examples of compounds containing Te.
After some practice one learns how to phrase questions to limit the number of hits. However,this is difficult with aliphatic compounds. Searching with CCO (ethanol) makes more hits than canbe handled. In such cases one must resort to guessing which simple structures will be contained in
the dataset for QSAR of interest and resort to the simple SMILES (Section VIII).

IX.

Searching Combined Databases

There are countless ways to search the combined Phys and Bio databases. By the properchoices the searching can be limited to either half, but the primary reason for combining the two issearch the Phys database for mechanistic support for Bio QSAR.

68

To obtain the combination enter from either regression mode data double then press returnand enter sea . Care must be taken in the use of set numbers in this mode. Set numbers for Bio data
are the same in data double and data Bio and hence can be used to retrieve sets for study in either
mode. However, Phys data sets have been assigned new larger numbers that must be used in data
double to extract sets for study. To see the normal Phys dataset number enter 6 in show. The
number shown here is the normal Phys label which is used in the Phys mode to retrieve sets.
A large number of instructive comparisons of the electronic role of substituents have previously
been published6, and thus steric factors will be used for illustration here. Taft's steric parameter,Es, was defined using the rates of acid hydrolysis of the esters, RCOOC2H5. Hence, for thisreaction, the coefficient of Es is defined as 1.0. The values of Es for all substituents larger thanhydrogen are negative, and so a positive coefficient for Es indicates the reaction is hindered bylarger substituents.
Entering 15 ES makes 508 hits on the combined database. Since we have not placed quotes on
ES, it is good practice to see what has been found. Going to sh ow and perusing under 15 we seethat a variety of labels other than Es are found; i.e., KES, ESC, PRES, EST, ESTD. These can beremoved by the not command (15 not KES ESC etc.). Doing so leaves 478 examples. To
narrow the 'catch' enter 16 .7<ES< . 9 and sea which reduces the number to 40. Going to sh ow
and entering /sort=16 1 3 4 16 18 . The following representative examples illustratecomparative analysis.
1. Benzene
Benzoic acids
Ionization 25 deglog 1/K = 0.71(±.22)Es-2 + 1.82(±.19)σ - 1.70(±.70)F-2 + 5.37(±.07)
n = 29,r2 = .956,
s = 0.160
2. Sea Urchin Eggs
X-C6H4CH2OC(=S)SCH 2C6H5
LD50

68

log 1/C = 0.72(±.18)Es-B + 1.23(±.59)logP – 2.24(±.10)log(β•10logP + 1) + 2.88(±.25)
n = 28,r2 = 0.846,

s = 0.354

3. Aqueous 50% Ethanol
Trans C6 H5 CB = CACOOH
Ionization 25 deg
pKa = 0.73(±.35)Es-2 – 5.61(±1.2)σ - A + 4.61(±.30)
n = 9,
r2 = 0.960,
4. Isopropanol
X-COCH3

s = 0.235

Reduction with sodium borohydride 50 deg
log K = 0.75(±.17)Es + 2.19(±1.0)σ* + 0.99(±.34)
n = 7,
r2 = 0.982,
s = 0.139
5. Muscle Rectus Abdominis Frog
X-CH 2COOCH2CH2N(CH 3)3+
Contractionlog 1/C = 0.76(±.17)Es + 1.19(±.37)π + 4.65(±.28)
n = 6,
r2 = 0.984,
s = 0.076
6. Acetonitrile
X-pyridines
Reaction with CH3 I 25 deg
log K2 = 0.79(±.24)Es-2 - 2.21(±1.9)σ + 0.08(±.30)
n = 10,r = 0.921,

s = 0.178

7. Aqueous 50% Dioxane
2-X-C6 H4 CONH2
Acid hydrolysis 90 deg
log k = 0.80(±.09)Es–2 + 0.08(±.12)
n = 13,r2 = 0.906
s = 0.086
8. S. aureus
BrCH2 CONHR
I100
log 1/C = 0.83(±.30)Es + 1.59(±.30)log P – 0.26(±.05)(log P)2 + 3.20(±.40)
n = 15,r2 = 0.953,
s = 0.208

68

9. Mouse

CH3x
3-X-C6 H4 CH2 CHNH 2 (amphetamines)
Stimulation of locomotor activity
log k = 0.84(±.12)Es + 0.35(±.12)π + 2.50(±.30)
n = 9,

r2 = 0.891,

s = 0.181

10. Aqueous
RCH2 CONH2
Acid hydrolysis 75 deglog k = 0.84(±.12)Es + 1.62(±.20)
n = 9,
r2 = 0.974,
s = 0.088
11. Chloroplast, Pea
X-C6 H4 NHCON(Me)CONHMe
I50 of photosystem II
log 1/C = 0.88(±.29)Es–2 + 5.69(±.16)log P – 5.63(±1.7) log(β • 10logP + 1) –0.55σ(±.34) + 2.62(±.39)F-2 – 2.51(±2.3)
n = 37,r2 = 0.937,
s = 0.203
M. Tuberculosis
2-X-4CONHNH2 pyridines
MIC
log 1/C = 0.89(±.22)Es–2 – 3.70(±1.1)F + 5.78(±.50)
n = 17,r2 = 0.835
s = 0.368
12. Aqueous 60% Dioxane
RCOOMeAlkaline hydrolysis
log k2 = 0.89(±.15)Es+ 1.90(±.29)
n = 7,
r2 = 0.978,s = 0.085
The 13 equations are for widely different reactions. Seven represent simple chemical reactions
and six are from biological systems. The parameter Es (with slope of 1) is based on the acidhydrolysis of RCOOC2 H5 (CH3 ) that have a slope close to eq. 10 and 13. Several correlate
substituents ortho to the functional group (1, 6, 7, 12) which brings out a similar steric effect. The
examples with biological systems may involve intra- and/or intermolecular steric effects. Bear in68mind that values for Es are all negative except H so that a positive coefficient indicates a deleterious
effect and vice versa. Equations 5 and 9 are closely related to the system defining Es whichsuggests a possible reaction involving nucleophilic attack on the carbonyl group. In the example ofeq. 9 meta substituents cannot affect the side chain, hence the effect must be intermolecular. Theseequations illustrate one way to search for lateral support for a new QSAR. Es and B1 are similar,
but we find that B1 is often the better for modeling steric effects.
Although we are only in the very early stages of Comparative QSAR, the use of a common setof classical parameters from physical organic chemistry is promising. Of course, until much morework has been done, one cannot always be sure exactly what such comparisons are telling us.
Nevertheless, these results can be used as points of comparison by those researchers working withthe more ambitious approaches to QSAR, such as CoMFA, neural networks and genetic
algorithms.

X . Caveats
It must not be forgotten that the present databanks were created over a period of about 30 years
by many individuals. The biological database was developed by the chance discovery of data setsthat could be correlated with the tools available at that time. As time went by, improvements in
QSAR formulation were made, and more complex datasets could be entered. Most of the QSAR
have not been published. Values of the dependent variables have been checked, but the values of
the independent variables may have been improved (especially log P) since the original entry, but
as of the present have not been checked.
To check a QSAR for possible errors the best way to start is to use the stored QSAR (seeeq )with the command pred / abs / des . This lists the results with the most poorly fit points first inincreasing order of goodness of fit. The data can also be examined by the commands pred / des/ abs / unstar or pred / des / abs /star. This is handy for viewing large sets that may contain anumber of starred datapoints. The parameters values can be checked and one can consider otherways of parameterizing the data. It is easy to try other parameters with automatic loading now
68available. About 55% of the physical QSAR have been entered using the parent SMILES
procedure, but only about 45% of the biological have been so entered. We plan to remedy thisdeficiency so that one can rapidly check new ideas with the latest parameters.
Recently we have made a concerted effort to obtain a more complete set of QSAR from the areaof physical organic chemistry. This was done by going through the indices of a number of the
major journals cited in Chemical Abstracts. In this way we obtained about 4,000 QSAR. By
checking references to other work in these papers, we found over 3,000 more equations. Still wehave neglected certain areas such as spectra, dipole moments and Brönsted type correlations. Also,
we have no doubt missed many examples not published in English or German. We have noticed
that those publishing in the "standard journals" (i.e., J. Am. Chem. Soc., J. Chem. Soc., and J.
Org. Chem.) often do not reference papers published in "foreign" language journals. We hope torectify these omissions in time, if we can find collaborators in other countries to help us with this'language problem'.
We have retained sets that are obviously not very good correlations for various reasons. Many
authors have not studied more than a few compounds. This generally means that variation in thesubstituent properties is poor. In other cases, a number of data points have had to be omitted andsometimes correlation coefficients are low. We have felt that such examples (especially the
biological data) might be of help to others studying similar reactions, a poor example being better
than none at all. This is where comparative QSAR plays a most helpful role. When a QSAR for asimilar set of chemicals acting on a similar system can be found, a weak QSAR can be supported
or refuted. Such information is more helpful than conventional statistics.
In developing QSAR we have tended to under parameterize rather than over parameterize
equations. There are a variety of ways in which dual parameter equations have been developed inphysical organic chemistry (σI + σR , σ + σ •, F + R, etc.). We have rarely resorted to suchrefinements, since a major goal has been to develop QSAR for comparative purposes. We feel thatthe under parameterized equations may be more easily compared.

68The task of delineating the role of hydrophobic effects in QSAR is difficult. In wholeorganisms there is the problem of the random walk of the chemical from the site of entry to the siteof action. There is good reason to expect this to be related nonlinearly to log P. At the receptor, one
would expect more specific hydrophobic interactions of certain parts of the ligands and thesewould not necessarily parallel those of the random walk process. Hydrophobic effects at thereceptor, would best be modeled by π of certain substituents. In rare examples 26 it is possible tofind clear roles for both π and log P in the same equation, but usually one accepts some kind of"average" hydrophobic term. To really understand the problem studies would have to be made at
the isolated receptor, on cell culture and in the whole organism. Only a very few such studies havebeen made.6
Another factor confounding the hydrophobic effect in whole organisms is that of P450
metabolism. In general hydrophobic compounds are more rapidly metabolized. Thus metabolism
can have much to do with setting Log Po.
In building a database of QSAR for comparative analysis, having the means for the calculationof log P is of paramount importance. One simply cannot begin to measure log P for all of the SAR
studies being published and very few authors, give the problem much attention. At present, thecalculation of Clog P has reached a fairly reliable status as shown by the following equation:
Mlog P = 0.96Clog P + 0.08
n = 10,000,

r2 = 0.979,

s = 0.278

The ten thousand measured values have been carefully selected over a period of many years.
A short coming in all of the current methods of calculating log P is that while the relative values
may be reasonable, the absolute values may be off the mark. QSAR obtained with these will havefalse log Po and intercepts. The only way to be sure of avoiding this uncertainty is to measure atleast one log P for the set (ideally the parent compound) and use this to adjust the calculated values.
When using the automatic method for obtaining log P (add ClogP ) it is a good idea to also use
add mlogP to see if any measured values are available for comparison. Even if only one to two

68values exist this does help in getting a better estimate of log Po. Log Po is of paramount importancein understanding bioavailability in whole organism research.
Parameterizing for local hydrophobic effects, can be difficult. It is not unusual to find examples
where meta substituents show a hydrophobic effect and para substituents do not and this problem
can be complicated by ring flipping.27, 28 Ideally the substituent parameter, π, can be used to checkfor these possibilities. However, our current automatic loading uses only π values measured for XC6H5 or values calculated from simple benzene derivatives. If there is a strong electron
withdrawing group nearby, it will affect the π value of the substituent being parameterized,
especially if lone pair electrons are on the attachment atom of the substituent of interest. For
example, the following are measured log Ps: benzene = 2.13; aniline = 0.90; nitrobenzene = 1.85;
and 4-nitroaniline = 1.39. With benzene as the parent solute, π values for the amino and nitro
substituent are: π -NH2 = 0.90 - 2.13 = -1.23; and π -NO2 = 1.85 - 2.13 = -0.28. When these two
substituents are on the same phenyl ring, electronic interaction raises their apparent π values as
seen in these calculations:(1) For nitrobenzene as the parent solute system: π -NH2 = 1.39 - 1.85 = -0.46(2) For aniline as the parent solute system: π-NO2 = 1.39 - 0.90 = +0.49
Each π value has been raised by +0.77 compared to the value using benzene as a parent.
This increase in π values must be considered in heteroaromatic rings if the heteroatom iselectronegative; e.g., for aminopyridines. One can get around this problem for relatively simple
systems by taking π as the difference in the CLOGP values for the substituted compound and theparent compound. The ClogP program takes into account electronic interactions between
substituents.
Even though π from the benzene system is not ideal for more complex problems it generally isgood enough to spot local hydrophobic effects. Depending on the importance of the problem it
might then be worthwhile to measure log P for a variety of substituents to assess the seriousness ofthe problem.

68One of the most difficult problems is that of outliers. These are generally found taking the'best' QSAR that one can obtain in terms of r2 and then jackknifing to remove aberrant data points.
Of course, the danger in this procedure is that one is forcing the data to fit the 'best' model. The
procedure of marking outliers is important in that it helps one to get clues about the cause behindthe problems. There are four major reasons for outliers: 1. The mathematical model may beincorrect. 2. Shortcomings in the parameter values–especially for steric parameters. 3. Experimental errors.

4. Finally and probably, most serious is that of side reactions. There are

innumerable possibilities for members of a set of 'congeners' to react with the components of evena 'simple' cell that might affect the measured activity. Of course, this can be minimized by using
relatively unreactive substituents. As we gain background from Comparative QSAR, we expectthat it will be possible in some instances to identify very similar chemicals operating by different
mechanisms. Two examples illustrate this point.22, 23
To our knowledge ours is the first attempt to develop a computerized database for storing andcomparing QSAR for all kinds of chemical and biological reactions. As such, it no doubt has many
shortcomings. However, it can be relatively easily modified in many ways and we welcome anysuggestions users might care to offer. At sometime in the future we may want to use more complex
equations and include 3-D graphics.
Finally, we hope that the C-QSAR program will induce as well as assist others in the next
phase of QSAR, that of developing an organized science of structure-activity relationships forchemical-biological interactions such as that which has evolved for organic chemistry in the past
125 years.

68

XI.

Appendix: Parameter Definition

When in either the Bio or Phys regression mode loading a set and then using the f etchcommand displays the following table:
1

PI

pi

23

S-INDUC

sigma inductive2

MR-SUB

substituent refractivity

24

S-AN-RSsigma resonance, anilines

3F

field effect (from S-L)

25

S-RES.+

sigma resonance plus

4

R

resonance effect (from S-L)

26

S-'

sigma prime

5

R+

resonance plus

27

S-PARNOsigma para normalized

6

R-

resonance minus

28

S-ORTH+sigma ortho plus

7ES

E(s) from Taft

29

S-PHOSPsigma phosphoric acid8ES-

E(s) from hydroboronation

30

S-L

sigma localized [Charton]

HYBO

9

ES-V

E(s) from Charton

31

S-OTWSTsigma orthogonal twist

10

ES-A

E(s) from Austel

32

S-STAR

sigma star from Taft's

11

L-STM

length sterimol

33

S-IND.P

sigma inductive [phosphorus]

12

B1-STMwidth sterimol

34

S-RES.P

sigma resonance[phosphorus]

13

B5-STMwidth sterimol

35

ER-P

electronic radical, para

14

O-STER

ortho quats with MeI

36

ER-M

electronic radical, meta

15

S-P

sigma para

37

S.DOT-Psigma dot, para

16

S-P+

sigma para plus

38

S.DOT-Msigma dot, meta

17

S-P-

sigma para minus

39

S.-DOT-P

sigma dot, para (JJ)

18

S-M

sigma meta

40

S.-DOT-M

sigma dot, meta (JJ)

19

S-M+

sigma meta plus

41

S.P-C

sigma para (C)

20

S-M-

sigma meta minus

42

S.M-C

sigma meta (C)

21

S-O

sigma ortho

22

S-O-

sigma ortho minus

Any of the 42 different parameters can be automatically loaded for regression analysis, perusedfor drug design or compared with each other for theoretical analysis. The more commonly used (1,2, 3, 4, 5, 6, 7 11, 12, 13, 15, 16, 17, 18, 23, 32) are discussed and their use illustrated in ref. 4.

68

A brief definition and references follows.
1.

PI. Hydrophobic parameter (π) for substituents defined by partitioning of X-C6 H5 between
octanol and water. (P)1, 2
π x = logP X-C 6 H5 - logPC6 H62.

MR-SUB. Molar refractivity of a substituent defined analogously to π.
2MW
MR = ( n −1 2
)
n +2
d

where n = refractive index, MW = molecular weight and d = density.
MR values are scaled by 0.1. MR is highly collinear with substituent volume.1, 2
3.

F. Swain-Lupton inductive/field effect parameter for aromatic systems.14

4.

R. Corresponding Swain-Lupton resonance parameter.14

5.

R+. Taft resonance parameter for substituent delocalization of a + charge.14

6.

R-. Taft resonance parameter for substituent delocalization of a - charge.14

7.

ES. Classic steric parameter for substituents defined by Taft from the hydrolysis of XCH2 COOCH3 (C2 H5 ). 20

8.

ES-HYBO. An Es type parameter obtained from the hydroboration of substituted
ethylenes.

9.

ES-V. Charton's steric parameter.29 Using the command parameter / nolimit and entering
7 and 9 it is found that there are 117 for which both Es parameters are available. Entering 3
reg 4 yields a correlation between Es and Es-V with r2 = 0.942. This can be improved tor2 = 0.964 by jackknifing to remove three badly correlated points: C(CH2 CH3 )3 , CHBr 2 ,CH(Me)CH2 CMe3 .

10.

Austel's 30 version of Es. A calculated value available for 1738 substituents. There are 198
substituents with both Es-A and ES, between which the correlation is weak: r2 = 0.793.

11.

L-STM. Verloop sterimol parameter for substituent length.1, 2, 15

12.

B1-STM. Sterimol parameter for the width of the first atom of the substituent.1, 2

68

13.

B5-STM. An estimate of the overall width of the substituent.1, 2

14.

O-STER. There are 30 substituents having this parameter for the effect of adjacentsubstituents inhibiting the reaction of pyridines with CH3 I. There is little correlation
between O-STER and Es or B1.31

15.

S-P. Normal Hammett σ constant for para substituents. It is based on the ionization
constants of benzoic acids.1, 2

16.

S-P+. Brown σ + parameter where substituents delocalize a + charge or radical via
resonance.1, 2

17.

S-P-. Hammett constant (σ -) where substituents delocalize a negative charge via resonance.
It is derived from the ionization constants of phenols.1, 2

18.

S-M. Hammett σ constant for meta substituents (non conjugated substituents).1, 2

19.

S-M+. Brown σ + parameter for meta substituents.4 There is little difference between σmand σ +m . 1, 2

20.

S-M-. Hammett σ - for meta substituents (non conjugated substituents).1, 2

21.

S-O-. Hammett σ for ortho substituents. It correlates poorly with σp , for 51 substituents r2= 0.303.32

22.

S-O. The σ - parameter for ortho substituents.33

23.

S-INDUC. σI for the field/inductive effect.1, 2 Originally defined from 4-X-bicyclo [2.2.2]
octane-1-carboxylic acids.

24.

S-AN-RS.34 Resonance parameter (σ - ) obtained from anilines. There are 25 substituentsfor which both σ - and S-AN. The correlation between the two is poor: r2 = 0.718.

25.

S-RES+. Resonance parameter for delocalization of + charge.35

68

26.

S-'. Field/inductive parameters from bicyclo [2.2.2] oct-ene-1-carboxylic acids, 4-Xdibenzobicyclo [2.2.2] octa-2, 4-diene-1-carboxylic acids and cubanedicarboxylic acids.36

27.

S-PARNO. A set of normalized σp values.37

28.

S-ORTH+. σ + for ortho substituents.38

29.

S-PHOSP. σ for substituents attached to phosphorus.39

30.

S-L. σL for field/inductive effect.40

31.

S-TWST. Effect on σ resonance by twisting substituent 90o out of plane.41

32.

S-STAR. Classic σ* defined by Taft.1, 2

33.

S-IND.P. Field/inductive parameter for substituents attached to phosphorus.42

34.

S-RES.P. Resonance parameter for substituents attached to phosphorus.42

35,36. ER-P, ER-M. Radical parameters defined by Yamamoto and Otsu.1, 2, 43
37,38. S.DOT-P, S. DOT-M. Radical parameters (σ•) defined by Dust and Arnold.44
39,40. S.-DOT-P, S.-DOT-M. Radical parameters (σ•) defined by Jiang.45
41,42. S.P-C, S.M-C. Radical parameters (σ•) defined by Creary.46

68

XII.

Notes on the Use of Substituent Parameters

The electronic parameters for aromatic substituents, σ, σ+, σ- have been developed andextensively tested over the last 40 years. The 'normal' electronic parameter σ is used when there isnot significant through resonance (i.e., direct resonance between a substituent conjugated with thereaction center.) The parameter σ- holds when direct resonance is involved in the delocalization ofa negative charge: i.e.,ONO

ation of a positive charge is involved:

OH

X

O
N
O

OH

O H

X
H

.While σ+ is used when delocalizO H. Surprisingly, σ+

serves very well to correlate radical reactions. 9 In fact, it is more generally useful to detect andcorrelate radical reactions than parameters formulated expressly for this purpose (σ• and ER)9 .
Those inexperienced in physical organic chemistry should test all three. In fact experienced
researchers are sometimes surprised by unexpected results that shed light on the underlying
reaction mechanism. A serious problem is the high collinearity among these parameters that mostbe minimized by careful substituent selection. For example, there are 199 substituents for σp andσp + for which r2 = 0.878. However, this collinearity can be greatly reduced by the proper selectionof substituents. In the case of σp and σp - where 196 values are available for both types ofsubstituents correlation is also high r2 = 0.829. Of course, meta substituents are not conjugatedwith a reaction center so that σm ≅ σm+ ≅ σm- .
For reactions with aliphatic systems where resonance is not involved two so-called
field/inductive parameters are available σ* and σI. Sometimes one gives a somewhat better answer
than the other. σ* may contain a small component associated with steric effects. The correlation
between these parameters is also high r2 = 0.856 for 366 substituents for which both values are
known. F is a field/inductive parameter for aromatic systems. It is most commonly tested in68combination with one of the aromatic parameters and a steric parameter for substituents ortho to areaction center.
The commonly used steric parameters are Es, B1, B5, L and MR. Es is the classic stericparameter defined from ester hydrolysis by Taft. It is somewhat comparable to B1, however for
147 substituents common to both series r2 is only 0.3. But the low collinearity is the result of
including large bulky substituents in the comparison. The collinearity is much higher with thesmaller commonly used groups. B1, B5 and L are calculated parameters. B1 is used for stericeffects around the first atom in the substituents, B5 is bulk parameter and L is for the overall
length. MR (molar refractivity) is essentially a measure of the overall bulk of a substituent. It ishighly collinear with molar volume. Unless care is taken it may also be highly collinear with log P.
Of course, there are two major steric problems: intra- and inter- molecular and generally inbiological systems one has no idea whether one or the other or both are involved. One of the great
advantages of our system is that parameters can be very rapidly load for regression analysis andthen using the perm utation command one can quickly sort the significant variables. All of theabove parameters have been discussed in more detail and there uses illustrated.4 A parameter thatcannot be automatically loaded is the indicator variable. These are often of considerable help. They
are added by using the edit set command. Then one must take care to add the name of the newvariable. Then a 1 or 0 is added for the presence or absence of the feature under consideration. In
adding valves for substituents that cannot be autoloaded care must be taken not to overrun thespace allowed for each line. When this is done, a marker is shown on the far right of the screen.
This must be removed by moving the cursor away from the last value and using delete until themarker disappears.

68XIII. QSAR and Combinatorial Synthesis
The almost infinite possible variation in the structure of organic chemicals presents the drug
designer or the toxicologist with overwhelming possibilities. For example, we noted in 1979 6 that
if one were to substitute all seven positions on the quinoline ring, with (at that time) the number ofwell characterized substituents (166), there would be approximately 3.5 X 1015 possible
derivatives. Combinatorial synthesis has greatly magnified the problem as Brown and Martin haveshown. They note that if one were to start with 360 commercially available precursors for R1 and259 for R2 and if one were to select 100 R1 's and 100 R2 's for the production of 10,000
compounds by combination of the R1 and R2 precursors the number of possibilities would be 2.5
X 10 164 . To provide perspective for the size of such a number, they note that the age of theuniverse in seconds is about 1017 and its size is 10108 è 3 .
Combinatorial synthesis is booming in tandem with the stock market! Although the approach
outlined in this manual is not designed to expedite the random search for new leads it does havesome features that may be of help. There are hundreds of QSAR that are based on trivial numbersof data by combinatorial standards. These QSAR provide points of departure for combinatorialstudies as well as old fashioned synthesis as mentioned in VI.
In undertaking the expense of designing, synthesis and testing of a large number ofcompounds it is very important to avoid property redundancy, the collinearity problem and to be
sure that electronic, steric and hydrophobic data space is well covered. For example, electronic
terms are found in 1,509 out of 6,300 bio QSAR and hydrophobic terms are found in 4,097. In
what we would call a conservative approach to multicongener synthesis, starting with a highly
active parent molecule (active at 10-6 to 10-9 M) the interest would be in substituents whose
properties are known. The only way to do so at present is via groups with known substiuent
constants. In section V. K, we have discussed this problem. In addition to substituent constants
one could use calculated log P values as well. However we believe, despite their shortcomings, π
for substituents can often provide better insight. We have encountered hundreds of instances where68only the hydrophobicity of one part of the molecule is important in the QSAR. One must calculate
or measure log P for the parent and then limit the selection of π values so that the log P range forthe compounds to be prepared is reasonable. Rather few drugs have log P outside the range of -1
to 5. 48

68

XII.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.

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68