Fall 2002

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

Table of Contents
Preface ............................................................................................................................................ 4
I.

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

Structure of the System........................................................................................................ 6

III. Using the Databases............................................................................................................12
A. Help................................................................................................................................ 12
B. String Searching ........................................................................................................... 12
C. Physical Database:........................................................................................................ 13
1. Main Menu ................................................................................................................................ 14
2. Searching and Show .................................................................................................................. 14
3. Browsing.................................................................................................................................... 22
4. Statistics..................................................................................................................................... 23
5. Omitted Data Points................................................................................................................... 24
6. SMILES ..................................................................................................................................... 25
7. MERLIN and SMARTS ............................................................................................................ 27
8. Loading from 'Database Search' to 'Workspace'........................................................................ 28

D. Biological Database ...................................................................................................... 29
1. Browsing ................................................................................................................................... 30
2. Searching ................................................................................................................................... 30
3. Comparing New QSAR ............................................................................................................. 35

IV. Searching for New Lead Compounds .................................................................................. 39
A. Locating QSAR that are Based on Highly Active Compounds................................ 39
B. MERLIN Searching ..................................................................................................... 41
V.

Searching Combined Databases........................................................................................ 43

VI. SMILES Tutorial................................................................................................................ 47
VII. Parameter Definition.......................................................................................................... 51
VIII. Regression Analysis: Example 1.......................................................................................... 56
A. Title Information (set B633) ....................................................................................... 57
B. Naming Parameters...................................................................................................... 57
C. Naming and Entering Substituents............................................................................. 57
D. Entering Structures via SMILES................................................................................ 59
E. Auto-Loading of Parameters....................................................................................... 59
2

F. Permuting...................................................................................................................... 60
G. Checking for Parameter Collinearity ......................................................................... 62
H. Jackknifing.................................................................................................................... 62
I.Plotting Data .................................................................................................................... 63
J. Cross Validation ........................................................................................................... 63
K. Editing ........................................................................................................................... 64
L. Regression Analysis: Example 2 ................................................................................. 65
M. Substituent Selection in Molecular Design................................................................. 68
VIII. UDRIVE and Masterfile ................................................................................................... 70
IX

System Crashes ................................................................................................................... 70

X.

Caveats ................................................................................................................................ 70

XI

References ........................................................................................................................... 753

Preface
It has now been 40 years since the first QSAR were developed at Pomona College. Our firstattempt to cover the subject from the chemical as well as the biological point of view was
published in 1996. Although it is still by far the most extensive publication of equations, much
has changed since that time. Then our database of biological QSAR was only 6,000 and QSAR
from mechanistic organic chemistry was much less. We now have a much more sophisticated,
searchable database of over 17,700 equations of which 8,900 are for biological reactions. In
1995, the calculated octanol/water partition coefficient Clog P was based on only 7,500
measured values with r2 = 0.956 and standard deviation of 0.336. At present, the accuracy of thismost important parameter is much improved: n = 12,600, r2 = 0.973, s = 0.299. The Hammett
type parameters are still the most extensively tested electronic parameters. Where they have been
compared with quantum chemical parameters (about 130 examples), about half the time one issignificantly more effective than the other.
What remains most surprising to us is that, in the vast majority of studies on the interactionof chemicals with biological entities, no attempt has been made to make any kind of QSAR.
Worse, structural changes in a parent compound are obviously being made with no thought given
to including substituents that have significant variation in hydrophobic, electronic and stericproperties! Although the need for interaction between synthetic and mechanistic organic
chemists is becoming ever greater, as is the close interaction between chemists and biologists,
progress is very slow.
Two important features of our program are: it can be used to see what has been done inthousands of examples and it can be used to formulate new QSAR. The latter use is greatly
facilitated by the autoloading of parameters. This includes the automatic calculation of log P, π,
CMR (substituent polarizability) and MgVol (molecular volume). We believe that the majorityof researchers will want to begin by reviewing what has been done in their area of interest. This
often provides important possibilities for comparative QSAR. In this way one can obtain lateralsupport for any newly defined QSAR.

4

I. Introduction
Understanding how chemicals react with themselves and how they interact with thechemicals that compose living systems or parts thereof (DNA, proteins, enzymes, organelles,
cells, membranes) has become a major concern of many phases of science. It is central to drug
development, toxicology, environmental science, biochemistry and molecular biology. The
information explosion, which is occurring at a constantly increasing rate, is an enormous
challenge to cope with, despite the various online approaches. These methods are excellent if one
knows the specific questions one needs answers to. They are rather helpless when it comes tosearching for structure-activity relationships. For instance, simple Hammett equations that were
initiated by Hammett in the mid-1930s are sometimes listed in Chemical Abstracts as LFER
(linear free energy relationships), occasionally under Hammett, and these days sometimes under
QSAR. However, searching under these terms would yield only a small fraction of the publisheddata because many authors now do not include any indication of a QSAR in their abstract. The
last attempt at complete coverage was made by Jaffe in 1953 when he listed about 400 that weretermed LFER! We now have 8,900 such equations that cover every type of chemical reaction.
The situation with biological reactions is far more complex and it is difficult to findparticular mathematical relationships between structure and activity (QSAR), primarily becausevery few authors attempt to publish their results in such terms. Usually it's, "here are thechemicals; here are biological activities—you take it from there." It has not been easy to collectdata for these two classes. We have obtained leads from chemical abstracts or from perusing
certain key journals. Then, from the references in those articles, uncovered new articles and soon and on.
What we have been attempting to do for the last 40 years is to develop a dynamic, integrated,computerized system covering all chemical-chemical interactions and chemical-biologicalinteractions. We enter any information into this system of chem.-bioinformatics that can be
described in mathematical terms with reasonable statistics. Of course any particular QSAR may
be of interest, but that is not our primary concern. Comparative QSAR is our main goal. This
becomes the foundation for developing a science of how chemicals affect living systems. This
will take decades to develop properly, but it is already very helpful. We are constantly finding
useful, unexpected relationships. For example, a study at the EPA on the toxicity of phenols to
rat embryos in vitro set us on a path that yielded a QSAR that rationalized the estrogenic and5carcinogenic activity (or the lack of it) in many simple phenols and complex phenols such as
diethylstilbestrol, Bisphenol A, estradiol, estriol, equilin and equilenin.1 More recently we have
unexpectedly discovered a way to establish a wide variety of allosteric interactions.2, 2a Now thatour database of QSAR has grown to 17,800 equations of which 8,900 are bio QSAR, the greatly
enlarged database offers real possibilities for finding entirely new relationships that are truly
exciting.
It must be emphasized that developing a science of chemical-biological interactions is notpossible unless one employs standard parameters whose meaning is clearly understood.
Moreover, one must be reasonably certain that the dependent variables for a given QSAR arereasonably uniform. If the chemicals are causing a similar reaction but by different mechanisms
the final QSAR may be more confusing than helpful. There are many ways of formulating a
QSAR these days, but so far ours is the only one the parameters of which can be comparedamong large numbers of equations.3, 4, 7, 8, 9, 9aLead generation has always been of paramount interest to those designing new chemicals of
biological interest. Our approach to lead generation is covered in section IV. In the past, clues
were obtained from studying naturally occurring biologically active chemicals. Today the hot
subject in lead generation is combinatorial synthesis. However, QSAR can play a vital role inlead exploitation. Possibly the best published example is that of Koga's5 demonstration that therather mediocre drug nalidixic acid could be transformed with the help of QSAR into what has
become the fabulous quinolone carboxylates. Other examples of QSAR success in thedevelopment of products of industrial importance have also been discussed by Fujita.6 Of course,
as soon as a new drug hits the market competing companies start to work to make improved
modifications. In doing so, the total application of QSAR is important. Also after getting a leadcompound via combinatorial synthesis, the next step is modification guided by QSAR to insure
the best overall properties in the compound going to market. Data mining is a current buzzword.
We are doing model mining. Behind every molecule stands a QSAR model that points to ways tomake more, or less, active congeners.

II. Structure of the System
An overview of our system is outlined in Tables 1 – 3. Our major concern, and most difficult
problem, has been to organize topics and data so that information related to a particular problem
6

can be easily located with extraneous material omitted. Since one is most often concerned witheither the biological or physical data, the system is divided into two sections; either of which canbe searched independently or jointly.
The input data in Table 1 contains the information that must be associated with every QSAR.
The systems are different for the biological and physical data. For the former, a name (enzyme,
cell, animal, etc.) must be entered, while in the case of the phys data, the name of the reaction
solvent is entered. The output data (fields 15-20) are that obtained from the regression analysis.
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) logP, 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.

Table 2 shows how the biological data are categorized. There are six major classes and theseare broken down into sub-classes. All of these can be searched independently or in any
7combination. For instance, loading B2A with B6F would gather all QSAR on oxidoreductases
and fish so that one could look at QSAR for chemicals reacting with fish and compare these withoxidoreductases. The numbers in parenthesis indicates that number of QSAR in each sub-class.
Table 2: Class Codes--Biological Database{Number of sets in parentheses}*BO

Unknown (9)B1Nonenzymatic Macromolecules(DNA, Fibrin, Hemoglobin, Soil,
Albumin, etc.) (286)

B2
B2A
B2B
B2C
B2D
B2E
B2F
B2G

Enzymes
Oxidoreductases (812)Transferases (283)Hydrolases (928)
Lyases (58)
Isomerases (23)
Ligases (2)Receptors (1665)

B3
B3A
B3B
B3C
B3M
B3R
B3S

Organelles
Mitochondria (91)
Microsomes (99)
Chloroplasts (84)Membranes (118)
Ribosomes (0)Synaptosomes (23)

B4
B4A
B4B
B4C
B4E
B4F
B4P
B4V
B4Y

Single-Celled Organisms
Algae (42)Bacteria (814)
Cells in culture (962)
Erythrocytes (79)Fungi, Molds (295)Protozoa (116)
Viruses (200)
Yeasts (54)

B5
B5C
B5G
B5H
B5I
B5N
B5S
B5L

Organs/Tissues
Cancer (259)
Gastro-intestinal tract (79)Heart (91)Internal/soft organs (64)Nerves, Brain, Muscles (365)Skin (55)Liver (30)

B6
B6A
B6B
B6F
B6H
B6I
B6P

Multi-Cellular Organisms
Animal (vertebrates) (698)
Insects (232)Fish (202)
Human (43)Invertebrates (non-insect) (114)
Plants (125)* These numbers are constantly changing as new data are added daily.
Classification is the most difficult problem facing the study of biological QSAR. The major
areas such as biochemistry, medicinal and pesticide chemistry, toxicology, etc. all have large
numbers of sub-classes, e.g., enzymology, anesthesiology, cancer, mutagenesis, metabolism,
cardiology, psychobiology, bacteriology, plant physiology, urology, etc. It is apparent from
Table 2 that, beyond the few keywords listed, we have not as yet attempted a very extensive
8

classification. We hope that this can be handled by string searching (see below).
The phys database has been somewhat easier to organize, however, even here we have been
forced to create a miscellaneous class that now contains almost 529 QSAR. In compiling thephys database from mechanistic physical organic chemistry studies, we have concentrated onreactions in solution. Although there are a few examples based on gas phase reactions andspectra, we gave up on these, as they seemed to be of little value in understanding biologicalprocesses. We started out with a class for Brønsted reactions but gave up on this and now enter
these under the appropriate chemical type (e.g., nucleophilic substitution). Many papers reportresults at a variety of temperatures. Generally for lack of resources, we report one example at thetemperature nearest to 25o. In examples where results have been obtained in various
concentrations of mixed solvents representative examples are given. We have not attempted tostandardize the dependent variable hence intercepts cannot be compared as they can forbiological reactions, where C in log 1/C is the molar concentration producing a standard
response.
Many data sets have been reported with little, or no thought, about collinearity problems (weare now correcting these early results) and parameterizing the data so that all types of steric,electronic or hydrophobic properties of substituents have been covered. Synthetic chemists areoften unfamiliar with such properties and are more concerned with the immediate problems,difficulties in synthesis. Nevertheless, we have tried to do the best we could to include such data
as we have often obtained clues from modest studies that have helped our general understanding
and suggested new experiments.
Of course it is by no means clear what quality of correlation equation one can expect for abio QSAR even when all possible care has been taken in the design of say 30 to 40 congeners.
Even in treating something as simple as a cell culture (let alone mice or people), the problems areawesome. Nonetheless, the enormous drug industry and the agencies trying to classify theenvironmental risks of industrial chemicals constantly face these problems. The DNA codes for
50 to 100 thousand cellular proteins that account for the many enzymes and components of thevariety of organelles and cellular membranes. All sorts of biochemical processes such as
endocytosis and exocytosis are apt to be effected. The possibility for 'side reactions' is enormous.
No doubt the weakest links (with respect to a given set of congeners) in this complex machinerywill be perturbed. However the concentration of chemical necessary to reach a particular 'end
9

point' may vary greatly. For example, rather high concentrations are required to disrupt a cell
membrane (10-3 molar). Other compounds are active at 10-9 molar or even lower (10 -11). The timeallowed for the experiment is also crucial. These factors allows us to gain meaningful QSAR
whose shape can be shown to be supported with other bio QSAR and most surprising, in many
instances, with those from studies in mechanistic physical organic chemistry.9a An importantfactor is that over the eons, nature has favored the evolution of highly specific receptors that
exclude extraneous molecules. Allosteric effects may be involved.2, 2a
Our current premise is that the major interaction forces to consider for a set of congeners
acting on a biological system are hydrophobic, steric and electronic (including polarizability).
Less important are hydrogen bonding and dipole moments. Hydrogen bonding can be importantbut, as yet there is no general way to parameterize it. For example, the orientation and distance
between an OH on a substrate or inhibitor and the bonding site on the receptor is so critical that ageneral method for parameterization appears to be impossible. Indicator variables can be helpful.

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

Theoretical (47)PO

Unknown

P1
P1P
P1X

Ionization (1732)
Ionization Potential (39)
Proton Exchange (74)P2

Hydrolysis (872)P3

Solvolysis (646)

P4
P4I
P4E
P4M
P4N
P4R
P4U

SpectraIonization Spectra (60)
ESR Spectra (6)Mass Spectra (12)NMR Spectra (194)
IR Spectra (9)UV Spectra (23)

P5

Miscellaneous Reactions (529)

P6
P6E
P6N

Substitution
Electrophilic Substitution (264)
Nucleophilic Substitution (1192)

P7
P7D
P7E
P7N
P7P

Addition
Dimerization (12)Electrophilic Addition (151)
Nucleophilic Addition (253)
Polymerization (10)

P8
P9
P10
P12
P13

Elimination (173)
Rearrangement (220)Oxidation (557)
Radical Reactions (613)Complex Formation (105)

P14 Partitioning (130)P14C Chromatography (21)
P15
P16
P17
P18
P19
P20
P21
P22
P23

Pyrolysis (90)H-Bonding (35)Electrochemical (301)BrØnsted (121)
Esterification (238)
Photochemical (48)
Hydrogenation (16)
Isokinetic (2)Reduction (91)* These numbers are constantly changing as new data are added daily.

11III.Using the Databases
The primary idea to keep in mind is the structure of the system as outlined in Scheme I.
Total Database
Biological Database (Table 2)

Physical Database (Table 3)

6 Major Sub-Classes (Table 2)

24 Major Sub-Classes (Table 3)

34 Minor Sub-Classes (Table 2)

15 Minor Sub-Classes (Table 3)

Parameters
Electronic, Hydrophobic, Steric
Polarizability (Table 4)

Parameters
Electronic, Steric(Table 4)

Scheme 1 outlines a biodynamic system that is like an electronic set of two books. One canread one book or the other or merge the two and page through them. The difference is that since
paper is not involved, the books undergo continuous updating. Our present goal is about 100 new
QSAR/month. Still, this is not enough to keep abreast of the voluminous flow of new literature.
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 this exercise.
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 alist of functions , and if one were interested in 'eigenvalues', entering ?? eigenvalue will tell how
to calculate them for a set of parameters. ?? Pred shows the various way results from a
calculation can be displayed. Additional help can be obtained by calling Mike Medlin at BioByte
Corporation [(909) 624-5992].
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 be obtained. The grammar for
string searching can be illustrated with the word 'in'. It can stand alone (as a preposition) or bepart of another word. The following examples show four distinct contexts in which it can appear.
12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
the letter string, while a blank and quote after the string of letters restricts matches to the end ofwords. 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. CASEI.
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.
Any word or parameter can be negated by prefacing it with NOT (discussed later). This
causes the result to be the reverse (logical complement) of what it would otherwise be.
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
the results 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 bydata physical. A prompt then asks for a password. For read only access, press RETURN and themain menu is displayed (table 4). Occasionally, because of a bad entry, the system will crash,enter unlock followed by QSAR and then data physical to restore the system.
1
2
3
4
5
6
7

Table 4: Main Menu
SummaryShow
SearchBrowse
MedChemManager
Save Database

DIR
HELP
PRINT
QUIT
READ
REG
VMS
WRITE
13

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 151,840 data points(compounds). The physical base contains 88,280. Calling for 'help' will instruct you how to exitfrom any mode and call another as well as reinforce the explanations in this Manual.
2. Searching and Show
The search and show feature is accomplished in two steps: first one enters the search mode
from the main menu (Table 4), which is obtained by entering data and then sea, and press
return to start the search. After the search is completed, use the show mode to display the hits.
The Search Menu is shown in Table 5.
Table 5: Search Menu
SEARCHBACKUP
BLANK
NOT
LIST

0 Equations
1 System
2 Class
3 Compound
4 Action
5 Reference
6 Source

8 Note9 Date10 Parameters
11 Substituents
12 SMILES
13 MERLIN
14 Prmmax/min

7 Check

15 Terms in eq.
16 Coefs. in eq.
17 Ideal/logB
18 Statistics
19 Residuals
20 Predicted

DIR
HELP
PRINT
QUIT
READ
REG
VMS
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 canget more than the expected result. For instance, one might search the physical database with thecommand 2 P1 where 2 refers to the 'class' as listed in table 1 and 1 is thought to refer to thecode for 'Ionization' as listed in table 3. However with this entry one would recover 3,968 sets,
which is far too many for ionization alone. As entered the command locates, via string searching,all sets with classes P1, P10, P12...etc. and finds 'oxidation', radical reactions' etc. The correctentry has leading and trailing spaces 2 " P1 " and finds only 1,732. Entering SHOW takes one tothe show mode where the results 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 the14search will be performed on the entire physical database of 8,900 sets and the search requested is'Action....BROMIN'. To perform the search, enter search, then press return. 180 hits are found.
To peruse the 'catch', enter show and then 4 (action) and one finds bromination, brominolysis,
photobromination, dehydrobromination, etc. If you were interested only in equations wherebromine is involved in an addition reaction, which is classed as P7 in table 3, enter search toreturn to that Menu, and then enter bl to blank out the last command then 2 P7 (Table 3)
followed by 4 bromin and then press return, which shows the status check indicating that the
search will be made on the hits made previously. Enter search, which finds 55 sets of bromine
addition reactions. If you want to see bromine in radical reactions, you need to return to theoriginal 180 hits. This can be done by blanking out the last search with the entry bl 2. Then enter
2 P12 and after approving the status check, enter search. This returns the Menu showing 55 hits.
One might want to view these sets showing 'system', 'action' and 'terms', and would enter show 1
4 15. To return from any Menu to the Main Menu, enter q. To illustrate another way to search
the bio database, enter data bio, press return then sea and enter 1 HIV to find 204 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: Category 1 in Table 1 is SYSTEM, and for the physicaldatabase it refers to the solvent in which 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 wewish to search for reactions run in aqueous solution. ('aqueous' is always used, not 'water').
Searching with 1 aqueous would make 3,867 hits. Adding to the search 1 not % eliminates
mixed solvents and would reduce this to 1,633. Now searching with 2 " P1 " finds 617 QSAR
for ionization in water.
Remember that results from all previous searches can be removed by entering blank.
Alternatively, one or the other can be removed by bl 1 or bl 2. To find which of the 8,900 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 status15

report shows them on two lines. This search finds 725 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 B5.
There are 797 hits. Entering show and then 15 displays the variables used in each set , with thedependent 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 (1,487). The quotes on P2 are required bystring searching grammar so that P20, P21, etc. would not be included.
Collecting studies based on the various Hammett-Taft sigma constants requires somethought. 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,Ywhere the sigma 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), (σ)
including those at specified positions but excluding the 'special' sigmas (S+, S-, etc.), thefollowing steps should be followed. Entering 15 " S finds 7,979 sets including both positional
and 'special' sigmas. To remove the latter, enter 15 not S- S+ SI S' S. and 4,209 remain. Noticethat the 'not' command is not necessary in searching for the 'special' sigmas because of string
searching. For example, 15 " S- finds all QSAR based on sigma-minus parameters (1,145),
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:
.
.
.
.
.
.
.

15 " S
15 not S- S+ S' SI
15 not MR
15 not B1 B5
15 not ES
15 not logP PI
15 not **2 bilin

7,979 hits
4,822 hits
4,787 hits
4,556 hits
4,402 hits
4,278 hits
4,235 hits

The symbol **2 represents squared terms such as S2 or PI2 and bilin represents bilinear
equations.14 (pg. 195) Note that the NOTS must be listed as a separate entry. If one wants QSAR
with only σ (S), a direct approach is to enter 15 " S," " S ". This includes substituents σ, X etc.,
but excludes σ –, σ+, σ*, σI.
Moving to show and entering 15 we see that except for a very few examples, all QSAR are16

based on the single term S.
Non linear QSAROver the years nonlinear QSAR have been found, and a variety of approaches have beendevised to deal with them. Most of these involve using more than one variable. Because of thelack of general agreement on how to correlate such data we have normally used a single
parameter and either a parabolic equation (aσ + bσ2) or a bilinear equation. These can besearched for as follows:
15 S+**2

finds 63 eq. parabolic in σ+

15 bilinlocates 27 sets bilinear in any parameter

Range Searching
Searching the physical database with 15 " S " finds 4,030 sets. It does not include electronicparameters such as S, X, etc. Normally, this is too large a list to scroll through. If one wanted tosee 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.

(1,229 examples with a slope between –0.8 and 1). In the Search Menu, 16 is the code for thecoefficient which, in the present case, is that for S. We might now check these sets to see how
many examples of radical reactions have 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 by increasing
sigma coefficient. First enter show. The Show Menu lists all the items that can be displayed witheach 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 space means '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 areordered in terms of increasing values of the coefficient, ρ, from -0.769 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.
CompoundIn Section 3 of Table 1, entries such as phenols, X-C6H4-COOH or benzodiazepines havebeen used. Compounds can more effectively be found by using the SMILES notation or by17

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 Searchmode enter 2 P12 followed by search. This locates 613 sets. For a quick overview of thecompounds studied, enter show and then 3. Scrolling through these examples takes less than ten
minutes and set numbers for those of interest can be noted. These can be examined in detail byswitching to the regression mode (reg) which will be covered in Section III-7. Not surprisingly,
it is seen that toluenes are a favorite subject for radical study. Returning to search and entering 3
C6H4CH3 C6H4Me Toluene (note string searching is being called for) finds 73 cases where
various derivatives of toluene have been the subject of investigation. This gives a quick, but
incomplete, answer. A more systematic method 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 202 examples where this word
was employed. Perusing the hits in the show mode (4), we find words such as photochemical,
electrochemical and chemical shift. Repeating the search entering 4 chemical, shift uncovers
157 NMR studies. Searching with 2 P4N locates 194 NMR studies which includes those basedon coupling and splitting constants in addition to chemical shifts. We have made very little effort
to include 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 141 sets. These can be narrowed by checking certain
years; e.g., 5 (1998) (1999) (2000). Five QSAR came from recent papers published in theseyears. Entering 5 Bordwell, Cheng isolates 35 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); 150 hits. 5 (1970) (1971) (1972);
946 hits. 5 (1998) (1999) (2000); 410 hits. It is necessary to put parentheses around the year to
distinguish it from page numbers. Remember that these are equations not individual papers.
Several QSAR may come from one article. Although remaining strong, interest in the Hammett
18

type equations in the 90's seems to be subsiding. Publications in particular journals can bechecked. 5 J.Am.Chem.Soc. finds 1,775. Note that one should not leave spaces between thewords in the title, but case is not important. From another point of view we can see where mostpublications occur by entering the following three searches in sequence:
5 not J.Chem.Soc.
5 not J.Org.Chem.
5 not J.Am.Chem.Soc.
The hits decline as: 7162, 5983, and 4081. Thus out of 8,900 sets only about half have been
published outside of these three journals. Entering show followed by 5 one can scan the list of
the less '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 thenew one. For example, suppose that you have formulated a new QSAR for an aromatic
nucleophilic substitution using the (S-) parameter with 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 byentering:
2 P6N which delivers 1,192 hits for nucleophilic substitutions, and then 16 2.6 < S- < 2.97
which narrows the sets of interest to 12.
The first command isolates all nucleophilic substitution reactions. The second finds thosewith ρ- in the range 2.6 to 2.97 (12 QSAR). Going to Sh and listing with /sort = 16 1 3 4 15 16
18 followed by S- yields the following representative examples.

1.
2.
3.
4.
5.
6.
7.

System20° Aqueous
20° Methanol
20° Aqueous
75° Benzene
20° Benzene
50° Methanol
20° Methanol

Compound
X-C6H4OCO(CH2)3NMe2
2-p-nitrophenoxy-3-NO2-5-S-thiophenes
X-C6H4OCO(CH2)4NMe2
6-X-2-NO2-C6H3Cl
2-phenoxy-3-NO2-5-X-Thiophenes
4-X-1-I-2-nitroiodobenzene
2-Br-3-NO2-5-X-thiophenes

Reagent
Internal
piperidine
Internal
piperidine
C6H5CH2NH2
N3piperidine

ρ−
2.60
2.61
2.65
2.72
2.75
2.90
2.96

It is sometimes desirable in comparative QSAR to focus on equations with a specific numberof terms with a limited number of variables. This can be illustrated with a search for all QSAR
19

containing only 3 terms, all of which are variations of σ.
1. 18 2<terms<4219 hits
2. 15 " S," " S "147 hits
3. 15 not ES I MR D F B1 B5 **2 bilin
32 hits
4. 18 n>70
2 hits
The first search isolates all QSAR containing 3 terms. The second ensures that a σ term
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 goodpractice to inspect search results at each step to see if ones expectations are being met. A good
example of such a three term equation is the following: (set #4405)
2X-C6H4NH2 + Y-C6H4CH2CH2OSO2C6H4-Z →Y-C6H4(CH2)2NHC6H4-X + Z-C6H4SO3- + C6H4NH3+
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,(1)

q2 = 0.991

SourceThis compartment contains the name of the person who actually entered the data, not the
paper from which it came. Entering 6 Gao uncovers 2,743 data sets entered by Hua Gao.
Check
The name of the person who checked the entry is contained here. If it has not been checked'unknown' is entered. Entering 7 not unknown finds 862 have at present been checked bysomeone other than the person deriving the QSAR.
Note, data and parameters
There is little or no value in searching these fields.
Substituents
Information on the kinds of substituents that have been used in QSAR can be found in two
ways. Common names can be employed or using SMILES (for those not familiar with the
SMILES notation, see section VI). Searching on 11 CH3 locates 1,376 QSAR, 11 Me finds
7,131 and 11 methyl uncovers 86. Thus, out of 8,900 QSAR, 8,593 have a parent compoundcontaining a methyl substituent. More complex structures can be isolated. 11 2-NH2 O-NH2
finds 82 QSAR with an amino group ortho to the functional group. 11 3,4,5-Cl 3,4,5-Cl3 locates20

9 QSAR; 11 CF3 uncovers 1,167; 11 SF5, 9; 11 NHSO2Me, 6.
The SMILES approach can locate more complex 'substituents'. From the search menu, enter
12 and then enter thiophene and its SMILES is presented and one is asked if editing is desired.
*
Answer Yes. Now place an asterisk on positions of interest e.g., (*)clcccsl yields
S

Going to the search mode and searching finds 38 QSAR.
Our system contains the names and SMILES (53,647) for drugs and common chemicals.
Again using 12 and entering aspirin, one obtains the SMILES and the 2-D structure. One canenter an asterisk in parenthesis at any point for a substituent.
One of the most important classes of data in the phys databank is that of pKa values andionization constants. These have been entered as the authors reported them without trying toplace them on a common scale. The following search illustrates the potential where one wants tofind solutes whose aqueous pKa's fall between 10 and 12.
1. 15 pK
1,736 hits
2. 15 not logK 1,646 hits
3. 1 aqueous
1,172 hits
4. 1 not %586 hits
5. 14 10<pka<12
9 hitsThe potential for calculating new pKa values is great. We have 1997 σP, 1232 σM, 398 σ+,
375 σ − and 887 σ* values. We have established that σM can be used with σ+ and σ −. The 565
pKa QSAR for the aqueous system can be expanded with these substituent constants. The firststep isolates all pKa's, some of which have a different modifier than a. Step 2 then eliminates
examples where pK is used as a dependent variable. Steps 3 and 4 isolate studies based in purewater and 5 illustrates how one can isolate compounds having pK's of values within a given
range. The following examples illustrate the possibilities. Remember that each of the 9 hits is a
QSAR that can be used to calculate many other values.
HO
X

N

CH

pKa range 10.46 to 11.17

X

SeOH

pKa range 10.17 to 10.84

Searching for stronger acids we can change step 5 to 14 2<pka<3 which isolates 15 examples
X

among which are:

H
N
O

X

OCONHSO 3Me

COOH

pKa range 1.53 to 2.22

pKa range 1.23 to 2.03
21

By changing step 5 to 14 0<pka<6 followed by 18 n>10 sequesters 57 sets having eleven or
more datapoints. Three examples are:
+ H2
N
CH
X

S

COOH
CH2
CH2

pKa range 4.55 to 5.50

XNH2

pKa range 0.65 to 2.72

X
N
X

X
N

pKa range 2.38 to 4.75
Of course one could search for any particular class of compound via the SMILES or the Merlin
approach.
From the search mode enter 12 and in the projected line enter the SMILES for the structureof interest or if there is a common name enter that and the SMILES will be generated. Entering
benzoic acid, it's 2-D structure is displayed. Moving to search, we find 253 QSAR. To examine
the solvent systems used move to show and enter 1, that will depict the wide variety of solvents
that have been studied. Searching the bio section with pKa, we find 84 examples where pKa is
the independent variable.
Now doing a MERLIN search, enter 13 and then the SMILES or compound name. Thissimilarity search finds all examples where one or more H of the selected compounds has been
substituted by another element. For example, benzoic acid finds 1,600 QSAR.
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 usefulfor 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.
22

4. Statistics
With each QSAR the following statistics are given:
N

Number of datapoints

DF

Degrees of freedom

R

Correlation coefficient

R2

Squared correlation coefficient

S

Standard Deviation

SS1

Sum of squares about the mean of the dependent variable

SS2

Sum of squares from the deviations from the regression line

D+

Number of positive deviations from the QSAR

D-

Number of negative deviations from the QSAR

Omit Number of datapoints omitted in deriving the QSAR
Q2

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 (none in Table 1). Checking the Phys data illustrates the usefulsearches. The command 18 n>20 in the search mode finds only 524 out of 8,900 QSAR that are
based on 21 or more data points. 18 n>10 hits 2,166 sets and 18 n>4 locates 7,667 based on 5 ormore compounds. Physical organic chemists have mostly been interested in establishing a valueof ρ for a reaction with a minimum of effort and hence often have not studied as many
derivatives as those interested in Bio QSAR do. Normally one wants a minimum of 5 data
points/variable with well spread parameter values. We have kept sets with 4 too, since they are
better than nothing.
The quality of the correlations can be evaluated by means of the correlation coefficient r.
Entering 18 r >.90 shows that 8,764 sets having correlation coefficients greater than 0.90. 18 r>.95 yields 8,144 QSAR and 18 r >.99 finds 3,731. Quality can also be checked with respect tothe standard deviation S. 18 S<.10 hits 4,618 QSAR with standard deviations less than 0.1 while
18 S< .20 makes 7,017 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 1,217 examples where NO
calculated value in the set deviates by more than ±.05 from the experimental value. This is a very
stringent standard. Relaxing the standard to ±.2 finds 4,332 examples.

23

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 calculationof the dependent variable; they can be the result of an experimental error or they can result froma side reaction. Whatever their source, it is extremely important that omitted points not be
forgotten. Keeping them in the equation can distort QSAR meaning.
In our system, to omit a point it must be marked by an asterisk (starred) which is held withthe data point so that it will not be forgotten. Moreover it becomes possible to make generalizedstudies of outliers. For example, entering from the Phys search mode 2 P12 collects all examplesof radical reactions (613). Now entering 18 omit>0 isolates all QSAR with one or more starreddata points (256). Moving to show and entering 11 lists all substituents for each of these data sets
along with the starred substituent. Now go to sea enter bl and add 2 P6N followed by 18 omit>0.
This isolates 411 QSAR for nucleophilic substitution with one or more starred points.
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,at present 529 examples. Some of these can be re-classified. Moving to show 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, is the residual, that is, the difference between the observed and experimental value
for the current stored equation. In this manner, the poorly behaved substituents for any type ofreaction can be determined.
The following 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 casenucleophilic substitution) can be found.
15
18
18
19

Example 1S+
1,928 hitsn > 10398 hitsr > .98
198 hits-.1<dev<.1
20 hits

Example 2
2 P6N
1,192 hits
18 n>10330 hits
18 r>.98
330 hits
19 omit <1
330 hits
19 -.1<dev<.1
69 hits

Example 3
15 S+
1,928 hits
18 n > 15
179 hits
18 r > .91
2 hits24

6. SMILES
Almost all structures in the databases are entered in the SMILES notation (section VI) so thatany compound can be located via its SMILES or a common name. For example, to find QSAR
which contain phenol, go to the search mode (from regression, enter data and then sea) in the
Phys bank 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 340 sets that
contain phenol. Normally this locates a set of phenols; however, sometimes phenol may be oneof a miscellaneous set of compounds. Going to show and entering 3 one sees a variety ofexamples other than all-phenol sets. Many of these sets can be removed by 3 not misc. For morediscussion of SMILES, see section VI and ref. 16 and 17.
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. Searching finds 21
examples. Moving to show and entering 15 it is seen that 7 QSAR are based on σ +, 12 on σ and2 on σ -. Entering 4 (action) shows that 9 examples involve bromination, 7 iodination, 4 onchlorination, 2 on nitrosation.
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 is very helpful with complex drugs or natural products where the SMILES takes time towrite and where there is a standard name. Entering Quinine to get the SMILES and thensearching, one set of data is found in the physical database that contains quinine.
From search, enter 12 and then strychnine. Two data sets are found; move to show and enter5 from these the set numbers are found to be 881 and 886. Enter reg and then load /d 886.
Entering summary lists key items in the data set.
Dataset name: PHYS_886
Substituents:
42
Parameters:
4
SMILES:
42
Active:32
Starred:
10
Inactive:0
System
LOG P OCTANOL VS LOG P DIETHYL ETHER
Class
P14 ; Partitioning, binding
Compound
H-ACCEPTOR SOLUTES
ActionEQUATION B
Reference
LEO,A. HANSCH,C. J.ORG.CHEM. 36,1539(1971) R1309
SourceLEO POMONACheck
UNKNOWN
25

DateParameters

2001 September 24
YPRED DEV LP-ETHER LP-OCT

Next enter eq /run which shows the stored equation.
qsar> eq /run
LogP-ETHER = 1.142(0.128)LogP-Octanol - 1.070(0.122)
N = 32
DF = 30

R = 0.957
R2 = 0.916

Q2 = 0.900
S = 0.326

SS1 = 37.809
SS2 = 3.187

DEV+ = 20
DEV− = 12

seeparameters:
1
YPRED

2
DEV

3
LP-ETHER

4
LP-OUT

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 panelof 4 structures. The process can be stopped at any point by entering q. This can be very
important in dealing 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 16 , To view all structures up to 16 enter depict ,16. To see those between 16 and 20
enter depict 16,20.
The following steps isolate all compounds containing a thiophene moiety:1. 15 pKa

1,592 hits

2. 13 thiophene

29 hits

13 calls for a MERLIN search

Examples of the 29 hits are:S
X
S

SO2 NH

COOH

X

wide range of pKa
in various studies

pKa range 7.1 to 10.27

COOH
Me C Me O
O
C N
N
C CNH
H2 N

X
CH2NHCO
OH

S
OH

S

O
C CHC CF2 CHF2
SHS

pKa range 5.5 to 8.5

pKa 5.25

The range of structures for which pKa values have been determined is remarkable. The above
examples are from studies in aqueous solution. In the last example, there were a variety of26

substituents where thiophene is attached, but only one example with thiophene. Other examplesof the use of MERLIN will be discussed.
7. MERLIN and SMARTS
A good example of using MERLIN for searching would be finding the sets which contain theethanolamine moiety. The SMILES entry is simple: NCCO, but if we wish the amine to be
primary and the carbons to be 'hydrocarbon' (not carbonyl) the SMILES should be followed bythis SMARTS: [N&H2][C&H][O&H]. SMARTS is just a scheme for putting inside brackets any
restrictions on the bonding environment on any of the atoms in the SMILES. This search gives
169 hits when both bio and phys databases are searched. Some examples are:
OH
Cl

OH

CH

CH

CH2
Cl

CH2

NH2

NH2
CH

OH

CH3

CH3

OH

HO
C

CH
CH 2
H 3C

CH

NH 2
HC
CH3

O

H 2N

CH2
OH

If we allow the amine nitrogen to be secondary [N&H], the number of hits rises to 2,181.
Some examples are:
CH3
HO CH2 CH2 NH

HO CH2 CH2 NH
CH3Returning to the Phys database (reg then data phys then sea) we need to consider thesubsection on miscellaneous reactions. Sequestering this group of QSAR by 2 P5 yields 529
27

QSAR. Of course, one could page through these by going to show and entering 1 3 4 15 16 18 tosee the unusual reactions. However, if one has a particular reaction in mind, the whole databasecan be string searched as follows:4

reaction name
Diels
FriedelCyclization
Mercuration
Salt
Alkyl
Decomp
Wolf
DipoleDecarboxyl
Racemi
Meerwein
Bromi
Hydration(# of hits)
44
3
38
12
25
32
111
6
15
27
2
1
268
53

Diels-Alder reaction
Friedel Craft reaction

salt formation
Alkylation
Decomposition
Wolf-Kishner reduction
Dipole moments
Decarboxylation
Racemization reactions
Meerwein-Pondorf reduction
Reactions with bromine
Hydration reactions

8. Loading from 'Database Search' to 'Workspace'
After a data set of interest has been found by means of the 'search' and 'show' modes, it canbe examined in greater detail if transferred 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 isdone by entering regression from either the Bio or Phys database and for this and the following
exercises transfer to the biological database with the entry data bio (password = press return)followed by reg. The screen prompt becomes qsar>. Entering load /d 464 (set number) transfers
the set to the workspace. Any data set can be viewed in the following ways by entering theindicated commands:
Sum Lists key items of data set
SystemHuman
Class
B6H; Human
Compound X-2-amino-4-nitro benzenes
ActionSweet taste
Reference Blanksma, J.J.; Hoegen, D. Rec. Trav. Chim. Pays-Bas 65, 333, 1946. See
Hansch & Deutsch, Nature 211, 75, 1966.
SourceHansch28

CheckSeep

L. Zhanglog RBR σ+ Pi S ER PE Clog P

Seeeq

log RBR = -0.66(±0.28) σ+ + 1.32(±.24) Clog P – 0.07(±0.48)
2Pred

n = 9 r = 0.973 s = 0.132
log RBR
H
1.60
F
1.60
OMe
2.52
OC2H5
3.15
OC3H7
3.70
Cl
2.60
Br
2.90
I
3.10
Me
2.52

(1)

2

q = 0.936
Ypred
1.592
1.833
2.426
3.127
3.827
2.575
2.813
3.104
2.392

Dev
.008
-.233
.094
.023
-.127
.025
.087
-.004
.128

S+
0
-.07
-.78
-.78
-.78
-.11
.15
.13
-.31

ClogP
1.258
1.405
1.498
2.027
2.556
2.055
2.255
2.465
1.707

In this research, 'RBR' represents the relative potency of the compounds in several test humans.
'Source' indicates who entered the data, 'check' indicates the name of the person checking theresults. 'Parameters' shows all parameters considered in the study. 'Ypred' is the predicted value
from the latest stored equation. 'Dev' is the difference between this figure 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 ,which depicts sequentially all of the compound structures as one presses return after each panelof 4 structures. The process can be stopped at any point by entering q. This can be very
important in dealing 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 5, enter
depict 5, to view all structures up to 9, enter depict ,9. To see those between 3 and 8, enter
depict 3,8.
D. Biological Database
The biological database is more difficult to master than the Phys section. To get some feelingfor this, think of the complexity of organic chemistry. One would not expect to quickly readthrough a textbook of organic reactions and then start to develop a new synthesis for quinine. To
study the interface between chemistry and biology, one needs all of the chemistry and biologythat one can master over a number of years. We believe that our system will greatly expedite theprocess.
29

Our database of 8,900 Bio QSAR can now be used as a chem-bioinformatics system fordeveloping a science 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. An
important aspect of the bio database is that it contains many complex structures of drugs forwhich SMILES and synonyms have been entered even though they may not appear in a QSAR.
There are 151,400 SMILES and 50,290 common names which, upon entry, yield the SMILES.
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 database to even better
advantage than in the physical database to become acquainted with its contents. In the Main
Menu, entering 4 displays the Browse Menu. As an example, in browsing the 'system' you mightfind '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

cat papillary muscles

Entering 1 sea lists all of the 8,900 systems that have been studied. Paging through a few
thousand gives an idea of what all has been studied. The more sophisticated work has high set
numbers.
2. Searching
String searching the 'system' field (1) in this database can be of more value than it was for thephysical database. Field 1 does not have standardized information, but use of 'Class' (2) can
increase its utility. For instance, entering data then sea followed by 2 B4B yields 814 sets
relating to bacteria. Moving to the show mode, one can quickly page through these sets byentering 1 to survey just what kind of microorganisms have been studied. Or entering 3 gives thetypes of compounds that can also be quickly scanned. Entering 1 3 yields both. Such a survey ofall of the work on bacteria can be made in less than 30 minutes. It would take days in the libraryor on line. Returning to search and entering bl then (blanking out aureus and entering) 1 aureus

30makes 153 hits most of which are for S. aureus and A. aureus. Now entering 15 not logP PI RM
eliminates all QSAR with hydrophobic terms, leaving 23 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 10 QSAR with various sigma terms. Going to show and entering/sort=16 1 3 4 15,18 and " S on the prompt, displays the QSAR in order of increasing value of ρ
(coefficient with σ).
Combining 'system' and 'class' in searching can also be effective in other ways. Searchingwith 1 " rat " results in 1,000 hits but includes studies on, for example, 'oxidase monamine liverrat'. If only whole animal studies are of interest, this can be narrowed by entering 2 B6A, which
reduces the hits to 221. 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 isbroadened rather than narrowed. For example, one might want to look for similarities inequations dealing with the enzymes oxidoreductase, the organelle microsome, and wholeanimals. This search 2 followed by B2A B3B and B6A to yield 1,594. Now we could isolate
QSAR containing a σ+ term for comparative analysis by 15 S+ to find 128 cases. Next move toshow 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 systematicallydefine searchable terms. Still, searching this field can be helpful. For example, moving to sea
and using 4 metab finds 27 examples where the authors referred to their work as metabolism. Of
course, there are hundreds of examples of such studies that have been referred to in other ways:
dealkylation, hydroxylation, hydrolysis, etc. Some examples are:4bind

1,321 hits for binding of chemicals to various bio systems

4

uncoup43 hits for uncoupling of oxidative phosphoralation

4

Biocon.

37 hits for bioconcentration in various ways

4

Glutath

17 hits in which glutathione was employed1

Glutath

30 hits including glutathione transferase

4

Inflam18 hits on anti-inflammatory agents1

Cycloox61 hits on cyclooxygenase 1 and 2

1HIV

218 hits on human immunodeficiency virus

4

Mutag

44 hits on mutagenesis
31Combined searches of the 'parameter' category can be fruitful. The following set ofcommands can be used to look for patterns in electronic and hydrophobic effects in various
biological systems:
1.
2.
3.

15
15
15

logP
not logP' **2 bilin" S," " S "5,038 hits
3,476 hits
298 hits

Note that log P also isolates sets based on ClogP via string searching. The second command
removes a few 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 **2, which indicates a squared term and bilin a bilinear QSAR which makes for easierfirst time comparisons. Step 3 isolates QSAR based on simple σ terms (i.e., not σ+, σ –, σ* or ES).
Now moving to show, two types of comparisons can be made. These are mostly QSAR linear inlog P. First, ordering on σ (S) we find a group of antitumor agents of the aniline mustard type
X-C6H4N(CH2CH2Y)2 with ρ of about -2 to –1.5 and a small generally negative slope with log P.
The crucial factor for reactivity of these agents with DNA is the electron density on N. Set 1780
for the enzymatic acylation of X-C6H4-NH2 which is dependent on the electron density on NH2
has a ρ of -2.1. At ρ ~ 0.3 to 0.5 we find examples of uncoupling of oxidative phosphorylationin mitochondria. The dependence of biological activity on σ and comparison with examples
from physical organic chemistry has been reviewed.7, 9a Ordering on log P yields a more
complex picture.
Another example might be to check the bio system for possible oxidation of anilines thatwould likely be associated with σ+ as follows:
1.
2.
3.
4.

12
15
16
2

aniline143 hitsS+
16 hits-10<S+<0 12 hits
B2A
8 hitsMost of the 16 examples involve oxidoreductases (B2A) with, possibly, radical mechanisms. 8
Comparing a new QSAR with those in hand is more easily done with the Bio data where wehave often been able to standardize the dependent variable log 1/C. Except when marked as log
1/C′, C refers to molar concentration needed to produce a standard reaction in a standard time.
The following is illustrative.
32

1.

15 " log1/C "5,618 hits

2.

15 not **2 bilin

4,195 hits

3.

15 not " S

3,213 hits

4.

15 not Es B1 B5 MR Pi Pka I D

1,571 hits

5.

16 .7<logP<.9347 hits

6.

16 0<const<0.5

48 hits7.

3 not misc

37 hits

The first step insures that all dependent variables are uniform (i.e., eliminates log 1/C'). The
second eliminates all nonlinear QSAR. Step 3 removes all equations with any kind of σ term.
Step 4 removes QSAR having any of the indicated independent variables. 5 selects only thosewith slopes between 0.7 and 0.9 and 6 insures the constant term lies between 0.0 and 0.5. The
last step removes QSAR based on sets of miscellaneous compounds.
Some examples are:I50 chloroplasts by X-C6H4NHCOCH(CH3)2

set 223

Inh. cholinesterase from electric eel by FCH2COOR

set 493

Inh. potassium uptake of liver mitochondria by X-C6H4COOCH2N(C2H5)2

set 880

I50 of Na+-k+ in mouse brain synaptosomes by CH3COOR

set 3251

Now looking for examples 1000 times more potent we can change step 6 to 3<const<3.5 andisolate 176 examples such as:

OH

I50 Human polymorphonuclear leukocytes by

set 5160
CH2 NH C6H 4

X
N R

I50 binding of [H3]-Naloxone to rat brain opiate receptors by

set 4919

C
COOC2H5

Increase in nerve membrane potential by 20mV in mollusk buccal ganglion by salicylates.
set 411
MIC of eimera tenella in leghorn cockerels by

N
N

O
HN

O

Y

X

set 5073

The above examples of model mining illustrate how simple examples can be found. One could ofcourse focus a search by placing all sorts of limitations on the search engine. One might study
cells and whole animals by entering 2 B4 and 2 B6A. This garners 3,101 QSAR. Now thefollowing steps might be of interest:

33

1.

15

" log1/C "2,613 sets

2.

15

not **2 bilin

1,862 sets

3.

15

logP

1,098 sets

4.

15

" S," " S "95 sets – isolates only QSAR containing a σ term

5.

16

.7<logP<1

8 sets – log P terms in the range 0.7 to 1.0 ant.

6.

16

.8<" S," " S "<1.5

4 sets – a σ term in the range 0.8 to 1.5

I50 displacement of [3H] bremazocine from opioid receptor from guinea pig brain by
O(Y)CH2C N CH2

XZ

set 5068

N
CH
CH3
CH
CH3

log 1/C = 0.84(±0.28)Clog P + 1.30(±0.45)σX – 1.13(±0.53)MR + 3.90(±1.0)
n = 14,r2 = 0.912,

s = 0.229,

q2 = 0.785(2)

1 outlierI50 antinociceptive activity of the above compounds in miceset 5069

log 1/C = 0.73(±0.26)Clog P + 0.88(±0.46)σX + 2.92(±1.01)(3)

n = 11,

r2 = 0.889,

s = 0.212,

q2 = 0.809

1 outlier

The same sets of rather complex chemicals yield rather similar QSAR for two different systems.
The largest difference being in the intercepts and the negative MR term. The guinea pig system
is about ten times more sensitive. Small changes in the linker unit (Y) do not appear to be
especially important. Another search for more complex QSAR the activity of which might bedependent on radicals is the following:
15 logP
15 not **2 bilin
15 S+
16 .6<logP<1
16 –2<S+<0
Two examples of interest are:5,038 sets
3,566 sets
103 sets
18 sets
10 sets

I50 sheep vesicle prostaglandin cycloxygenase by phenols
log 1/C = -1.71(±0.25)σ+ + 0.69(±0.12)Clog P + 1.80(±0.32)
n = 25,r2 = 0.933,

s = 0.186,

set 1593(4)

q2 = 0.911

We have often found σ+ to correlate radical reactions.8
Acetyltransferase of acyl group from p-nitrophenyl acetate to X—C6H4NH2
log Vmax/Km = -1.25(±0.46)σ+ + 0.89(±0.46)log P + 0.65(±0.31)Es3 + 1.3(±0.74)
n = 10,r2 = 0.907,

s = 0.243,

set 1431(5)q2 = 0.787
34In equation 4, σ+ suggests a reaction whereby anilines are converted to phenoxyl radicals. In eq.
5 increase in the electron density on NH2 makes it a more potent reagent for displacing the–

O–C6H4NO2 moiety.

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 time inspecting the findings;
however, it must be used with care since one does not normally know just how close twocoefficients 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 new equation
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.

15

logP

5,038 hits

2.
3.
4.

15
15
16

"_log1/C__"
not logP' **2 bilin.7<logP<.93,299 hits
2,256 hits
444 hits

5.

16

1.5<const<2

73 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 these conditions we cancompare intercepts if the slopes are essentially the same.
One might expect to find a rather uniform set of compounds and actions for the 73 QSAR.
But, moving to show and perusing the catch with 1 3 4 reveals a variety of chemicals engaged ina variety of actions. We would term these nonspecific because of the low value of the intercept.
An intercept of 2 says that when P = 1 (log P = 0) a 10-2 molar concentration of chemical
produces the standard response. For comparison isopropanol has a log P of 0.05. Returning tosearch and blanking command 5, then replacing it with 16 4<const<10 and searching finds only
51 examples with intercepts above 4. These are for rather complex chemicals (compared toalcohols) and the activity would not be considered nonspecific.
The major difference between the parameters of the physical and biological QSAR is theimportance of the hydrophobic terms log P and Pi. Out of 8,900 bio QSAR, 4,378 (49%) containsuch terms.
35

Considering the five major categories the following disposition is found.

Enzymes
Receptors
Organelles
Cells
OrgansWhole Organisms

QSAR withhydrophobic terms(log P or PI)
1999
733
319
1755
672
1135

Total QSAR in
class

Percentage

3983
1665
433
2497
1008
1418

51%
44%
70%
73.7%
66.7%
80%

As might be expected, enzymes have rather hydrophilic terms and whole organisms have themost hydrophobic. In the last few years we have found a great increase in studies with more orless purified receptors. We now have 1,665 such QSARs of which only 44% have hydrophobic
terms. Electronic terms (including HOMO and LUMO) appear in 2,067 and the steric parameters
Es, B1, B5 and L appear in 2,167. Clearly hydrophobic terms are most important. Of course ourmeans for defining steric effects is limited. We have recently reviewed QSAR lacking positivehydrophobic terms.9
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 13 is obviously important for bioavailability, 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,9 but the reason for this is not always clear at present. Compounds that appear
to react with DNA seem to fall in this class.
Searching for studies of a particular drug or similar chemicals is illustrated in the following
table. The database contains over 52,500 common chemical names as well as official names ofdrugs currently on the market or discontinued, or interesting, but not yet on the market. Table 7
shows the results of both SMILES or MERLIN searches on a variety of compounds. For
SMILES based search enter 12 from the search mode and for MERLIN enter 13.

SMILESMERLIN
SMILES

Mescaline
Mescaline
Epinephrine

Table 7hits
5SMILES
22
MERLIN
14
SMILES

Testosterone
Testosterone
Phenoxyacetic Acid

hits
21
41
7
36

MERLIN
SMILESMERLIN
SMILESMERLINMERLINMERLINMERLINMERLINMERLIN

Epinephrine
Naproxen
Naproxen
Methotrexate
Methotrexate
[Pt]
[Se]
[Fe]
[P]
[S]

21
9
10
13
15
6
19
5
265
2,587

MERLIN
SMILESMERLIN
SMILESMERLIN
SMILESMERLIN
SMILESMERLIN

Phenoxyacetic Acid
Isoniazid
Isoniazid
Adamantane
Adamantane
GlucoseGlucoseCortisone
Cortisone

69
4
15
0
81
5
39
13
13

In the examples of Pt, Fe, Se, P and S only a MERLIN type search is possible since no
QSAR have been reported for the bare elements. This yields all compounds that contain such anelement. It is interesting that adamantine itself has never been tested, but after the discovery of
the antiviral activity of aminoadamantane there was a wild flurry of testing derivatives ofadamantine or using it as a substituent. In the case of cortisone it was surprising to find no'similar' compounds. The large number of hits with phenoxyacetic acid is due to the great interestin these chemicals as weed killers. In fact QSAR was developed out of interest in this class ofchemicals.10
The same approach can be used with CMR or any other parameter. There are 1,292 QSAR
with CMR terms of which 190 have CMR2 terms. Of the 190, 55 have positive CMR2 terms and
135 have a corresponding negative CMR term. That is, the shape of curve is that of an inverted
parabola. The search used to uncover this information is as follows:1.
2.

15 CMR**2
16 0<CMR**2<10190 hits
148 hits

3.
4.

15 CMR
16 –10<CMR<01292 hits
734 hits

We have taken the result of step 4 to imply that an allosteric reaction is occurring between
ligand and receptor. That is, at first interaction between ligand and receptor decreases as CMR
increases, but then at an inversion point as CMR continues to increase activity turns around andincreases.2 Clearly a change in mechanism has occurred. We assume this to be the result ofchange in shape of the receptor.31 We have a few examples where such inverted parabolas arefound with Clog P or molar volume, but most occur with CMR. The data must be plotted to be
sure that the inversion point is well defined.
37

Pauling and Pressman11 and Agin, et al.,12 first used molar refractivity in attempts torationalize biological processes, although the concept of bond polarizability had been used formany years to help understand simple chemical reactions. We use the following definition:
MWdWhere n is the refractive index, MW is the molecular weight and d is density. Many years ago,
MR = (n2 – 1 / n2 + 2)

A. Leo developed an additive constitutive approach using interatomic bonds to calculate CMR
from structure. Our program automatically calculates CMR and loads it for regression analysis.
Our MR values are scaled by 0.1 to make them somewhat more equiscalar with log P. Although
CMR is rather collinear with molar volume it is often definitely superior for correlation analysis.2
Fortunately, during the first part of the 20th century, the Russian chemists routinely publishedindex of refraction and density of new chemicals from which Leo devised the means forcalculating CMR.
To search the database for compounds having log Po (optimum log P), use the following
commands:
1. 15 logP
5,038 hits
2. 15 logP**2 bilin(logP) bilin(ClogP)
1,303 hits
3. 17 1.5<logP<2.5
127 hits
Command 3 narrows the catch to log P o values between 1.5 and 2.5. To inspect the results, moveto show 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 10-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 thebilinear model. Further information on these QSAR can be obtained using the usual codes. 1 3 417 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 2,497 QSAR on all types of cells. Then
15 logP** 2 bilin(logP) bilin(ClogP) isolates QSAR where log Po is found (434). Moving toshow 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 187 sets where the mean log Po value is about 2.8. This is significantly less than that38

for cells. We believe that the difference is largely due to the random walk process andmetabolism. That is, log Po is an important measure of Bio availability. However, log Po of about
13

2 is often, but not always, ideal for penetration of the CNS, which may or may not be desirable.

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 avariety of animals had a mean log Po value of 1.98. This figure is close to the above finding of
2.8 for a variety of biological end points. That is, log P of about 2 – 3 seems to be about ideal for
general Bio availability. This figure could be shifted up or down depending on the nature of thereceptor and any special metabolic liability. It does not hold for charged or partially ionizedcompounds. However, it is our belief that one wants to make drugs as hydrophilic as possiblecommensurate with efficacy.13 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.
These results have some bearing on Lipinski's rule that to maximize an oral drug's probabilityof surviving development, the following properties are desirable: molecular weight < 500;
number of hydrogen donors > 5; number of hydrogen bond acceptors < 10; Clog P < 5. His
proposal has been criticized.13a Our results indicate that in cell culture tests one would tend toobtain high activity drug candidates having a log P greater than 4, but this would not be ideal foranimals or humans.
The trend to do screening of potential drugs on cells, rather than animals, makes it difficult toselect only a few compounds for trial in animals. One may be mislead to select compounds thatare too hydrophobic for ideal results in animals.
IV.Searching for New Lead Compounds
A. Locating QSAR that are Based on Highly Active Compounds
The most difficult and important aspect of medicinal chemistry is finding new leadcompounds for drug development. Strange to say, they are all around us, and yet almost
impossible to recognize. A good example is the conversion of nalidixic acid, a mediocre
antibiotic, into the fabulous quinolone carboxylates, QSAR played an important role in thisprocess.5 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. Viagra,
39designed as a heart drug, turned out to be great for erectile dysfunction. The great excitement atpresent is combinatorial synthesis. Of course with our present C-QSAR system, one can selectany subject and study it for possible clues to increase potency or decrease toxicity. Since the vast
majority of the bio QSAR in our system were developed by us from data published by others inwhich no attempt to formulate a QSAR was made, it is highly unlikely that the most active
congeners were discovered. 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 = 9 means activity at 10-9 molar concentration.
A selective approach is to consider QSAR that cover highly active compounds. Two searchmodes are possible:
1. 14 " log1/C>9 " makes 6 hits. All members of the three sets have log 1/C values > 9.
2. 14 " log1/C "@max>9 finds 431 data sets. In this case, any set having a congener withlog 1/C greater than 9 is selected. Lowering the standard to 8, we find 47 and 1,157 sets,
respectively, for the two searching modes. At log 1/C of 7, we find 249 and 2,168 sets.
Searching the entire bank with 15 " log1/C " 5,611 sets are characterized by molar log
1/C terms.
These approaches can be generalized in the following ways:
3. 14 " log1/C " <2965 hits

4. 14 2<" log1/C " <4222 hits

5. 14 logP>6

1,570 hits

6. 14 logP@max>6

1,875 hits

7. 14 6<logP<83 hits8. 14 –1<S+<0

11 hits

As illustrated in the above examples one can find ranges for any parameter. Example 7 finds
only 3 QSAR based on log P values in the range 6 to 8. Example 8 uncovers all QSAR having σ+
values between –1 and 0.
As the database increases, it becomes more arduous to get exactly what one wants
without sorting out extraneous material. Another example of focusing is the following:
1.
2.
3.
4.

2 B4 B3
2,929 hits (cells and organelles)
14 " log1/C " @max>7805 hits
5 (1990) (1991) (1992) (1993) (1994) (1995)
219 hits
13 Imidazole
11 hits
40

This searches all QSAR from organelles and cells published in 1990 to 1995 that contain an
imidazole moiety.
B. MERLIN 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 has been 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
depict , displays the 2-D structures. Entering depict 1,10 would show the first 10, or depict ,enters all structures and the set number, where these structures occur. The depiction process canbe stopped at any point by entering quit.
Carrying out this process for aziridine in the bio database (entered as C1NC1 or by name)finds 323 examples. Entering depict , displays the 2-D structures and the set numbers in which
they occur. Note that after the first the program says that’s a lot of hits - - continue? Press Y tocontinue until the final number is obtained. In some instance, they occur in so many sets that the
numbers are hard to read. The process can be stopped by q and a particular compound can beinspected by depict #.
O

O
R2

N

N

R1

R

CH2 OCONH2
OCH3
N

O

NH

N
NO2

C(O)NHR
NO2

O

N

O
O=P-NHCOOR
N

N

R
S=P N
R
N

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 59 examples and of course searching from data
double 319 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, enter41

merlin and c1ccccc1C#N to find derivatives of benzonitrile. Choosing n starts the search and, on
the prompt asking whether or not to continue, entering y, sequesters 692 derivatives ofbenzonitrile. Enter depict , displays them 4 at a time. It would be rather laborious to look at all
678 structures, but not impossible. The same search with bio yields 937. If we limit the search byasking for derivatives of 4-chlorobenzonitrile (SMILES N#Cc1ccc(Cl)cc1), only 12 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 toenter chloramphenicol by name, choose y for editing, and then delete the N(=O)=O at the endof the SMILES string.
OH

OH

CHCHCH2 OH

CHCHCH2 OH
NHC(=O)CHCl2

NHC(=O)CHCl2
NO2

ChloramphenicolEntering depict, we find 24 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
CHCHCH2 OH
O2N

NHC(=O)CH3OCC(NC(=O)C)C(O)clccc(ccl)N(=O)=O

Now 20 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. There are no examples with substituents inthe 2, 3, 5 or 6 positions.
We can search the double database (see Section V) from the search mode by entering 13 and[Te]. So doing finds 10 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 thancan be handled. Substituent searching is different from a MERLIN search using the 13 entry(Phys base) where entering 13 C#N locates 1,841 data sets each of which may have one or morecompounds with a CN group.
42

V. 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 our primary reason for combining the two
is to search the Phys database for mechanistic support for Bio QSAR. Of course, physicalorganic chemists would be very interested in the phys section.
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 Biodata are the same in data double and data Bio and hence can be used to retrieve sets for study ineither mode. However, Phys data sets have been assigned new larger numbers that must be usedin data double to extract sets for study. To see the normal Phys dataset number enter 6 in show.
The number shown here is the Phys label which is used in the Phys mode to retrieve sets.
A large number of instructive comparisons of the electronic role of substituents havepreviously been published,8, 14, 15 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(CH3). Hence, for this reaction, the coefficient of Es is defined as 1.0. The values of
Es for all substituents larger than hydrogen are negative, and so a positive coefficient for Es
indicates the reaction is hindered by larger substituents.
Entering 15 ES makes 623 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 show and perusing under 15 we
see that a variety of labels other than Es are found; i.e., KES, ESC, PRES, EST, ESTD. Thesecan be removed by the not command (15 not KES ESC etc.). Doing so leaves 590 examples. To
narrow the 'catch' enter 16 .7<ES<.9 and sea which reduces the number to 43. Going to show
and entering /sort=16 1 3 4 15 16 18 on request for a label to sort on, enter ES. The following
representative examples illustrate comparative analysis.
1. Benzene

set # 1384 phys (10018 double)

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

43

2. Aqueous 50% Ethanol

set # 286 phys (8832 double)

Trans C6H5CB = CACOOH
Ionization 25 deg
pKa = 0.73(±.35)Es-2 – 5.61(±1.2)σ - A + 4.61(±.30)
n = 9,

r2 = 0.960,

s = 0.235

3. Isopropanol

set # 5245 phys (13791 double)

X-COCH3
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

4. Muscle Rectus Abdominis Frog

set # 969 bio

X-CH2COOCH2CH2N(CH3)3+
Contractionlog 1/C = 0.76(±.17)Es + 1.19(±.37)π + 4.65(±.28)
n = 6,

r2 = 0.984,

s = 0.076

5. Acetonitrile

set # 3424 bio (double 11970)

X-pyridines
Reaction with CH3I 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

6. Aqueous 50% Dioxane

set # 6632 (double 15170)

2-X-C6H4CONH2
Acid hydrolysis 90 deg
log k = 0.80(±.09)Es–2 + 0.08(±.12)
n = 13,r2 = 0.972

s = 0.086

7. S. aureus

set # 2028 bio

BrCH2CONHR
I100
log 1/C=0.83(±.30)Es + 1.59(±.30)logP – 0.26(±.05)(logP)2 + 2.06(±1.80)σ* + 3.20(±.40)
n = 15,r2 = 0.953,

s = 0.208

44

8. Aqueous
RCH2CONH2
Acid hydrolysis 75olog k = 0.84(±.12)Es + 1.62(±.20)
n = 9,

r2 = 0.974,

set # 7063 (double 15609)

s = 0.088

9. Chloroplast, Pea
X-C6H4NHCON(Me)CONHMe

set #2453 bio

I50 of photosystem II
log 1/C = 0.88(±.18)Es–2 + 5.69(±1.6)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

10. M. Tuberculosis
set # 2553 bio
2-X-4-CONHNH2 pyridines
MIC
log 1/C = 0.89(±.29)Es–2 – 3.70(±1.1)F + 5.78(±.50)
n = 17,r2 = 0.835
s = 0.368
11. Aqueous 60% Dioxane
RCOOMeAlkaline hydrolysis
log k2 = 0.89(±.15)Es+ 1.90(±.29)
n = 7,
r2 = 0.978,s = 0.085

set # 3863 (double 12409)

The 11 equations are for widely different reactions. Seven represent simple chemical
reactions and five are from biological systems. The parameter Es (with slope of 1) is based on
the acid hydrolysis of RCOOC2H5(CH3) that have a slope close to eq. 8 and 11. Several correlate
substituents ortho to the functional group (1, 5, 6, 10) which brings out a similar steric effect.
The examples with biological systems may involve intra- and/or intermolecular steric effects.
Bear in mind that values for Es are all negative except H so that a positive coefficient indicates adeleterious effect and vice versa. The molecules of equations 4 and 7 are closely related to thesystem defining Es that suggests a possible reaction involving nucleophilic attack on the
carbonyl group. These equations illustrate one way to search for lateral support for a new QSAR.
Es and B1 are similar, but we find that B1 is often better for modeling steric effects.
Another example involves radical reactions a subject of special interest to us.1, 8 Searching the
double database as follows is instructive.
45

1.

12

clccccclN

397 hits

2.

15 S+

44 hits

3.

15 not **2 bilin

43 hits

4.

3 not misc

42 hits

5.

sh

6.

/sort=16 1 3 4 6 15 16 18

7. Sort on S+
The following examples are instructive:
Oxidation of X-C6H4NH2 by MnO2 in aqueous solution

set # 6791 (double)

log k = -3.80(±1.28)σ+ + 1.49(±0.65)
n = 6,

r2 = 0.944,

s = 0.567,

q2 = 0.876

Oxidation of X-C6H4NH2 by vanadium V in 50% aqueous acetic acidset # 2393 (double 13904)+

log k2 = -3.31(±0.79)σ + 0.58(±0.41)
n = 7,

r2 = 0.958,

s = 0.263,

q2 = 0.916
NO2

Hydrogen abstraction by (C6H5)2 NN NN
log k3 = -2.83(±0.63)σ+ + 4.69(±0.17)n= 6,

r2 = 0.949,

s = 0.138,

NO2 of X-C6H4NH2 in CCl4
NO2

set #4703 (double 13249)

q2 = 0.932

The above QSAR establish a radical mechanism for the oxidation of anilines with which thefollowing biological QSAR can be compared.
Oxidation of X-C6H4-NH2 by horseradish peroxidase I

set #2393

log k2 = -3.17(±0.46)σ+ + 0.34(±0.08)Clog P + 5.14(±0.21)
n = 9,

r2 = 0.990,

s = 0.227,

q2 = 0.973

Oxidation of X-C6H4-NH2 by horseradish peroxidase II

set # 1682

log k = -3.00(±0.51)σ+ + 0.25(±0.10)Clog P + 4.52(±0.24)
n = 8,

r2 = 0.988,

s = 0.240,

q2 = 0.972

Oxidation of X-C6H4NH2 by cytochrome C peroxidase

set # 3072

log k2 = -2.86(±0.43)σ+ + 1.09(±0.21)
n = 7,

r2 = 0.984,

s = 0.193,

q2 = 0.962

The toxic action of radicals is an important factor to understand in drug research andenvironmental toxicology. To help in this process, we have over 600 phys QSAR on radicals.
46

VI.

SMILES Tutorial
Inventing the SMILES language for linear entry of complex structures of organic chemicals

into computers was a truly major achievement by David Weininger.16, 17 Simplified Molecular
Input Line Entry System has four basic rules that cover 98% of all structure notation. These canbe learned in a few minutes, but of course, it will take some practice to become proficient. One
can practice by entering udrive (Section VIII) from either regression mode. It is a bit moreconvenient to enter from the search mode by entering 12. In the displayed panel one can enter
the common name ascorbic acid or the SMILES to see the 2-D structure. If the common namewas used entering n goes to the search mode. Entering sea completes the search. Now entering
show, one can peruse the resulting catch of QSAR. For example, entering indole from the bio
database after the 12 command and then searching makes 11 hits.
The following examples illustrate the four basic rules.
1. Use ordinary atomic symbols. These represent aliphatic structures except for Cl and Br.
B, C, N, O, P, S, F, Cl, Br, I
2. Hydrogens are not normally entered except on compounds such as pyrrole, indole, carbazole, in these instances [nH] is used. The small n, c, o, p, s represent aromatic atoms.
3. Other atoms and any charges are placed in brackets, e.g., [Si]; [N+]. Also use H to fill
the valences of non-ordinaries, e.g., cl[SiH2]cl
4. Use upper case letters for aliphatic atoms and lower for aromatic. Except for the
halogens. These are entered as Br, Cl. The second letter is entered in lower case, [Sn] foraliphatic tin, [Se] for aromatic or aliphatic selenium.
5. For nonlinear structures branches are enclosed in parentheses. Neopentane is written as
C(C)(C)(C)(C).
CH3 CCH2 ClCH2 = CC(C(C)C)C(Cl)CCH
CH 3 CH3

Other examples:a. CH3CH2OH

CCO

b. CH3CH2NHCH3

CCNC

c.

clccnccl (the two "l"s serve to connect the linear structure to
the cyclic aromatic ring).

Nd.

ClCCCCCl47

e.

clc[nH]ccl
N
H

f. Pb[Cl]4

Pb(Cl)(Cl)(Cl)(Cl)g. Deutero chloroform[2H]C(Cl)(Cl)(Cl) (the isotopic mass number precedes theatom. For tritium, use [3H]).

6. Bonds are not specified except as follows:= for double bond# for triple bonda period (.) separates disconnected structures, e.g., ions, complexes or two or morereactants.
Examples:CH3C≡CN

CC#N

CH3COO– Na+

CC(=O)[O-].[Na+]HC=CNO2

C#CN(=O)=O

SO2 NH2

S

clccsclS(=O)(=O)N

Azido substituent*N=[H+]=[N-]-NH3+[N+](H)(H)(H)O

Reaction between 4-X—C6H4CCl and 4-Y-C6H4NH2=clcc(*)ccclC(=O)Clcl.clcc(*)ccclN
The (*) indicates positions where various forms of X and Y can be attached. A period
separates the two molecules.
7. A branched group is placed in parentheses
CF3CH2C(=O)CH3

FC(F)(F)C(=O)C
clccc[nH+]clN
H

clcccccl[N+]#N.[B-](F)(F)(F)(F)C6H5N NBF4
8. 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
CH3NH22-amino, 4-nitrotoluene

Cc1c(N)cc(N(=O)=O)cc1
NO2

48

Note that N is either trivalent or pentavalent. NO 2 is written as N(=O)=O; (CH 3)3N as
CN(C)C; (CH3)3N-O; O=N(C)(C)C.
1

2

1

c1ccc2ccccc2c1

2or

clc2ccccc2ccc1

CH3
Na+ O

CHOH
2

1

O C
NH2

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

*

3

4

4

1-Adamantyl

32-Adamantyl

As Shown by DEPICT:
CH2
HC

H2
C
CH CH
CH

CH2

CH2

CH2
C
H

2-Adamantyl

CH 2

H2
CHC

CH

C
H2

CH
CH 2

C
CH2

C
H

1-Adamantyl

An atom can be involved in more than one bond as shown above. A more complex example
is that of cubane.

49

HC

CH
HC

C

CH

C

2
C

1

C

5
HC
HC

CH

C
CH

C

4

C 12

3

C 25

C3

C5

C4

C
C

C3

C2

C4

SMILES for cubane = Cl2C3C4ClC5C4C3C25
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
and then leads into the center. Beginning arbitrarily at the top of the benzene ring as shown, thelower case 'c' is followed by the number '1' showing that it will later be joined to the carbon
fused to the pyrrolide ring (that 'c' will also be followed by '1').
N
6
start

1

7
end

2
N

3

5
4
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 clccc2N3C(=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 thannumbering both sides of the break as was done in the previous example. It takes a few hours ofpractice to become comfortable in entering complex structures, but if one can handle thestrychnine example, there are few in all of chemistry that will be more formidable.

50

VII.

Parameter Definition

When in either the Bio or Phys regression mode loading a set and then using the fetchcommand displays the following table:
0

CPI

Calculated Pi Value

22

S-O-

sigma ortho minus

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 acid8

E(s) from hydroboronation

30

S-L

sigma localized [Charton]

9

ESHYBO
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 ©

20

S-M-

sigma meta minus

42

S.M-C

sigma meta ©

21

S-O

sigma ortho

43

CMR-SUB

calc. MR for Sub

Any of the 43 different parameters can be automatically loaded for regression analysis,
perused for drug design or compared with each other for theoretical analysis. The morecommonly used (1, 3, 4, 5, 6, 7 11, 12, 13, 15, 16, 17, 18, 23, 32, 43) are discussed and their use
illustrated in ref. 14.
51

A brief definition and references follows.
1.
PI (π). Hydrophobic parameter for substituents defined by partitioning of X-C6H5
between octanol and water. (P)14
πx = logP X-C6H5 - logPC6H62.

MR-SUB. Molar refractivity of a substituent defined analogously to π.
2
MR = ( n − 1 2
) MW
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.14
3.

F. Swain-Lupton inductive/field effect parameter for aromatic systems.15

4.

R. Corresponding Swain-Lupton resonance parameter.15

5.

R+. Taft resonance parameter for substituent delocalization of a + charge.15

6.

R-. Taft resonance parameter for substituent delocalization of a - charge.15

7.

ES. Classic steric parameter for substituents defined by Taft from the hydrolysis of XCH2COOCH3(C2H5).18

8.

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

9.

ES-V. Charton's steric parameter.19 Using the command parameter /nolimit and entering
7 and 9 it is found that there are 117 substituents for which both Es parameters areavailable. Entering 3 reg 4 yields a correlation between Es and Es-V with r 2 = 0.942. Thiscan be improved to r2 = 0.964 by jackknifing to remove three badly correlated points:
C(CH2CH3)3, CHBr2, CH(Me)CH2CMe3.

10.

Austel's20 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.14, 21

12.

B1-STM. Sterimol parameter for the width of the first atom of the substituent.14, 21

13.

B5-STM. An estimate of the overall width of the substituent.14, 21

14.

O-STER. There are 30 substituents having this parameter for the effect of adjacentsubstituents inhibiting the reaction of pyridines with CH3I. There is little correlation
between O-STER and Es or B1.29

15.

S-P (σ). Normal Hammett constant for para substituents. It is based on the ionization
constants of benzoic acids.14

16.

S-P+ (σ+). Brown parameter where substituents delocalize a + charge or radical via
resonance. 14, 34
52

17.

S-P- (σ–). Hammett constant where substituents delocalize a negative charge via
resonance. It is derived from the ionization constants of phenols. 14

18.

S-M (σ). Hammett constant for meta substituents (non conjugated substituents).14

19.

S-M+(σ + ). Brown parameter for meta substituents. There is little difference between σmand σ +m . 14

20.

S-M- (σ –). Hammett constant for meta substituents (non conjugated substituents).14

21.

S-O- (σ). Hammett constant for ortho substituents. It correlates poorly with σp, for 51
substituents r2 = 0.303.35

22.

S-O (σ –). The parameter for ortho substituents.

23.

S-INDUC (σI). for the field/inductive effect. Originally defined from 4-X-bicyclo [2.2.2]
octane-1-carboxylic acids. 14

24.

S-AN-RS . 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.15

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.14

27.

S-PARNO. A set of normalized σp values.36

28.

S-ORTH+. σ + for ortho substituents.36

29.

S-PHOSP. σ for substituents attached to phosphorus.37

30.

S-L. σL for field/inductive effect.14

31.

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

32.

S-STAR. Classic σ* defined by Taft.14

33.

S-IND.P. Field/inductive parameter for substituents attached to phosphorus.37

34.

S-RES.P. Resonance parameter for substituents attached to phosphorus.37

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

Calculated MR for substituents.

These parameters were collected over the past 35 years. Many are obsolete and have been kept

53

for historical reasons. The following are those that we find useful today.
1.

CPI. Calculated π value that takes into account electronic effects of the molecule on thesubstituent of interest.

2.

MR-Sub. Calculated values for substituents.

3.

F. Field/inductive effect of substituents on aromatic rings.

4.

Es. Steric parameters for intramolecular interactions.

5.

L-STM. Sterimol parameter for substituent length.

6.

B1-STM. Sterimol parameter for size of first atom in substituent.

7.

B4-STM. An attempt to account for substituent bulk.

8.

S-P. Classic σ constant for aromatic substituents.

9.

S-P+. Classic σ+ when there is direct resonance between substituent and the reaction
center involving delocalization of a + charge.

10.

S- P-. Through-resonance involving delocalization of a – charge.

11.

S-inductive. (σI) for aliphatic reactions.

12.

S-star. (σ*) for aliphatic reactions. Sometimes σ* is best, sometimes σZ. They are rather
collinear.

13.

ER-P / ER-M. Used for aromatic radical reactions.

14.

S.P-C / S.M-C. Used for aromatic radical reactions.
The electronic parameters for aromatic substituents (σ, σ–, σ+) have been developed andextensively studied over the last 50 years. Simple σ is obtained from the ionization constants ofbenzoic acids where little through resonance is present.
The σ– parameter holds when direct through resonance is involved in the delocalization of apair of electrons.

O

O
N

OH

O

O

N

OH

The parameter σ+ (S+) is used when delocalization of a positive charge occurs. It wasdeveloped from solvolysis of cumyl chloride as follows:34
CH3
C Cl

CH3
C

solvolysis

CH3
X

C(CH3) 2

CH3
X

Surprisingly, it is widely applicable to radical reactions.8

X

The field/inductive effect F is useful in some aromatic cases. It is especially valuable for
substituents ortho to reaction centers. Such substituents should be tested by the combination F,
54

Es, B1, σ-p. That is, using F + σ-p can account for the extra electronic effect.22
For substituents on aliphatic molecules two sets of parameters have been developed σ* (S')
and σI (SI) although they are highly collinear sometimes one works somewhat better than theother. There are many more values of σ*.
In the case of radical reactions σ+ (S+) is the most widely useful parameter, however, ER or
S.-Dot. sometimes are better than σ+. 8 We still do not understand the reasons behind this.
There are two classes of hydrophobic parameters: log P and π (Pi). They are based onpartitioning between octanol / water. The expense of measuring log P has inspired a large effort
by many laboratories to develop methods of calculation. The following result comes from Leo's
work at BioByte.
log P = 0.96 Clog P + 0.07
n = 12,443,

r2 = 0.973,

s = 0.299

There are 228 outliers out of our present set carefully evaluated experimental values that are
not included in the above equation.
We have a large number of experimental π values from X-C6H5: π = log P X-C6H5 – log P
C6H6. These values have a serious limitation when there is a strong electronic interaction
between the substituent and other parts of the molecule. For such cases we subtract calculated
Clog P from the calculated value for the derivative (CPI). The steric parameters Es and thesterimols are interesting to compare. Searching the double data base of bio and phys of 17,800
QSAR, we find the following:
15 Es
15 B1
15 B5

623 hits
1,373 hits
917 hits

15 L
15 MR

63 hits
109 hits

We have not attempted to survey all of our earlier formulated QSAR to see where B1 canreplace Es. Verloop's sterimol parameters are an important extension of traditional QSAR in the
direction of 3-D QSAR. MR is a measure of the bulk of a substituent and it has not foundextensive use. Now that we can calculate MR for a substituent, we may find more use for it.
The first problem in designing a new set of congeners from a parent molecule is to proceed
carefully by studying one position at a time and choosing a set of substituents that shows good
variation in electronic, hydrophobic and steric properties. Now one needs to know the values thatare available for the set selected.
55

The problem can be approached from either the bio or phys systems. Entering parameter/nolimit in the regression mode displays the table of parameter values. Enter 2 13 16 17 18. This
isolates substituents all of which have values for Pi, B1, σP, σP+, σP. Entering sum shows that
there are 61 such substituents. Entering seedata displays the results ordered on increasing valuesof the first substituent (Pi) selected. The data set obtained can be worked with in the same
fashion as a standard data set. Next, first entering corr followed by 3 4 5 6 7 displays thefollowing correlation matrix:
Correlation matrix: R**2 and N
PI
B1
S-P
S-P+
PI
.
.014 .023
.004
B1
61
.
.147
.122
S-P
61
61
.
.852
S-P+ 6161
61.
S-P- 61
61
61
61

S-P.039
.079
.860
.732
.

The only significant collinearity is that among the electronic parameters. Although this mightseem serious, we have found that with some care in the selection of substituents, the mostsignificant σ parameter can be determined. We have established this fact via comparative
QSAR.8Another way to obtain a wider view is to select only S (σ) for which we have many morevalues. This would be sufficient to establish whether or not an electronic effect is present. Often
it is not. Entering parameter /nolimit and then 2 13 14 16 followed by sum finds 294
substituents ordered on π. Enter seed. If S was entered first, they would have been ordered on σ.
If interest is only in a single substituent, the following results can be obtained:
2 Pi yields 1088 values. Enter seed to find values from –5.96 to 3.03
13 B1 yields 1080 values from 1 to 4.65
16 σP yields 1997 values from –1.58 to 2.42
17 σP+ yields 398 values from –7.17 to 1.88
In each instance, one can enter seed to view the individual values.
VIII. 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 useful, it is much easier to save it in this area.
56

First enter regression 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:
A. Title Information (set B633)
T/system mouse embryo fibroblast cells
T/compound X-C6H4-NH2
T/action I50 Growth
T/reference 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 summary
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 beentered. Enter getp (get parameters). The program asks for a label for parameter 3. (As noted
previously, parameter 1 is reserved for predicted values and 2 is used for deviation). In thepresent instance log1/C is entered. The prompt is then for parameter 4, but since automatic
loading is to be used, just enter end 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 by hand.
Often data is not in logrithmic form. They can be entered as such and then converted to log orother form by using gettran.
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
57

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

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

Substituent
H
2-NO2
3-NO2
4-NO2
2-NH2
3-NH2
4-NH2
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-C3H7End

log 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, entersummary. To view parameter data entry, enter seedata. If entry errors need to be corrected, one
can enter editsub for editing substituents or editdat for editing data. 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 groupwhich are seen to increase toxicity. Note that the two most hydrophobic analogs, 20 and 21, arenot especially potent.

58

D. Entering Structures via SMILES
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
provides a panel with a prompt for structure 1. After the SMILES is entered, 'return' displays
the 2-D structure. If the structure needs editing, enter y, if not, enter n and the prompt for thesecond SMILES appears. Since there are a large number of SMILES stored in the database
together with name(s), the name can be entered at this point and the SMILES will be picked up
from the database. The name of the compound must be entered exactly as at least one of thestored synonyms, 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 avery time saving 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 /parent. 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, anda proper 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 fromNH2

the main pathway. Pressing return should then display:**

If the structure is not correct, enter y for editing. Deletions* and additions can be made to takeplace 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

2-nitroaniline*H *N(=O)=O *H

3-nitroaniline*H *H *N(=O)=O

4-nitroanine*N *H *H2-aminoaniline (Note that) H on NH2 or OH on CH3 is notentered, these are supplied by the system*C *H *H2-methylaniline*H *OC *H

3-methoxyaniline
59

*H *H *Cl

4-chloroaniline*H *H *CCC

4-propylaniline

Now enter end or quit.
Next enter depict , to check that all smiles correspond with substituent name.
Occasionally, it is not possible to enter a SMILES. The structure may not be known for amember of a set. Entering ** is necessary to account for the presence of the compound in thedata set. If a particular SMILES is incorrect, enter editsmi # (the compound number) and makethe correction.
For automatic loading of parameters enter fetch, which then shows Nclc(*)c(*)c(*)ccl
1 2 3
where the * refers to the ortho, meta and para positions of aniline. first enter Mlog P. Next add
the σ constants. Fetch displays the SMILES and the position of the substituents. Enter 1 and then
15 16 17 for ortho substituents. Again enter fetch 2 18 for meta substituents. Finally fetch 3
followed by 15 16 17. Now enter gett (for "get transformation"). In the displayed panel, enter S
and return. In the new panel, enter S-P-1 + S-M-2 + S-P-3. These are summed to yield S (σ)
repeat this process for S+ and S-. Next enter seep that displays all entered parameters. Now enter
delete/para 5 6 7 8 9 10 11 this removes the parameters used to derive S, S+ and S-. Now enterfetch and enter 1 to enter 3 7 12 13 for exploring the special electronic and steric effects of ortho
substituents.
F. Permuting
Now entering 3 perm 4 5 6 7 8 9 10 11 derives all possible equations for 1, 2 and 3variables. S.D. stands for standard deviation. As this value decreases, the quality of the fit isincreasing. CONST is the value of the constant in each QSAR. Clearly, σ+ is the most importantterm.
1 TERM REGRESSIONS
1
2
3
4
5
6
7
8

S.D.
.344
.417
.447
.459
.571
.579
.580
.581

MLOGP

S

S+
-.732

S-

F-1

ES-1

B1-1

B5-1

-.954
-.440

-.695
.168
.068
.109
-.027

CONST
3.731
3.877
3.911
4.366
3.655
3.903
3.757
3.884

60

2 TERM REGRESSIONS
1

S.D.
.298

MLOGP

S
1.716

2
3
4

.316
.331
.349

5
6

.352
.352

-.733
-.742

7

.353

-.726

8
9

.394
.411

10

.412

-.180

-.241
-.284

S+
-1.833S--1.303
-.609
-.744

.743

F-1

ES-1

B1-1

B5-1

CONST
3.514
3.583
3.955
3.703

.305
.143

3.567
3.712

-.056
.037

-.708

3.683

-.484

4.143
4.241

.949

3.832

-2.117

3 TERM REGRESSIONS
S.D.

MLOGP

S

S+

S-

1

.271

-.20

1.803

-1.753

2
3

.30
.301

-.175

1.916
1.873

-1.174-1.978

4
5

.304
.305

1.795
1.772

-1.927-1.877

6

.306

1.853

-1.869

7
8

.306
.321

-1.857-1.397

-.084
.847

9

.322

-1.376

.855

10

.324

-1.357

.823

F-1

ES-1

B1-1

B5-1

CONST
3.751

.731
-.092
-.203

3.803
3.609
3.513
3.518

.042
-.076

3.594
-.084

.077

3.513
3.672
3.587

-.175

3.583

If we run 5 reg 6 this shows that σ and σ+ are highly collinear (r 2 = 0.935). There seems to be no
special role for ortho substituents. Mlog P has a weak role. We can check for the overallcollinearity as follows
corr 4 5 6 7 8 9 10 11
Correlation matrix: R**2 and N
MLOGP

S

S+

S-

F-1

ES-1

B1-1

B5-1

MLOGP

.

.289

.292

.236

.047

-.218

.200

.078

S

21

.

.935

.943

.241

.059

.09

-.075

S+

21

21

.

.915

.017

.028

.019

-.077

S-

21

21

21

.

.099

.114

.144

.000

F-1

21

21

21

21

.

.713

.845

.756

ES-1

21

21

21

21

21

.

.782

-.751

B1-1

21

21

21

21

21

21

.

.807

B5-1

21

21

21

21

21

21

21

.

61

G. Checking for Parameter Collinearity
We can check for overall collinearity problems as follows: corr 4 6 5 10 11 15 13 14
MLOGP
MLOGP

S

S+

S-

F-2

ES-1

B1-1

B5-1

.289

.292

.240

.047

-.218

.200

.078

.935

.952

.241

.059

.096

-.075

.921

.017

.028

.019

-.177

.101

.114

.151

-.001

.713

.845

.756

.782

-.751

S

21

S+

21

21

S-

21

21

21

F-2

21

21

21

21

ES-1

21

21

21

21

21

B1-1

21

21

21

21

21

21

B5-1

21

21

21

21

21

21

.807
21

There is high collinearity among σ, σ+ and σ– and considerable collinearity with the stericparameters. The upper part f the matrix gives the correlation among the 21 different substituents.
Next exploring the two best terms: we obtain the following QSAR:
log 1/C = -0.18(±0.24) Mlog P – 0.61(±0.30) σ+ + 3.96(±0.33)
n = 21,

r2 = 0.693,

s = 0.331,

q2 = 0.615

This is obviously not a good equation.
H. Jackknifing
In picking the current data set for study we have not selected a good, easy to correlate
example. It is better to study how to do the best with a non-ideal set of data. At this point, we canlook for outliers. This could be due to poor experimental data. A major problem is a lack of
uniform reaction mechanisms of the various 'congeners'.42 Poor selection of substituents so thatbad collinearity is present from the start is so often the problem. It is rarely considered in thedesign of the project. We can now resort to jackknifing to obtain some perspective. 3 j 4 11
derives all possible regression equations by dropping a different data point in each instance.
Omitted
none
H
4-Cl
3-OH

r2
0.693
0.849
0.713
0.702

s
0.331
0.211
0.329
0.334

Dropping parent compound aniline (H) yields r2 of 0.849. To delete this compound use thecommand star /add 1. This places an asterisk on data point 1 and it is not then used in deriving
62

future equations. Any number of points can be withheld in this fashion. star /a 1 5,10 18 would
place asterisks on compound 1, 5 to 10, and 18. Asterisks can be removed by entering thecommand star /d 5 to restore data point 5 for study.
Starring 1 yields the following QSAR:
log 1/C = -0.22(±.15) Mlog P - 0.55 (±.19) σ + + 4.07 (±.21)
n = 20,r2 = 0.849,

s = 0.211,

q2 = 0.783

Jackknifing a second point points to 3-NH2 as being poorly fit. star /a 6 withholds this point.
The resulting QSAR is:log 1/C = -0.22 (±.16) Mlog P - 0.55 (±.20) σ + + 4.07 (±.22)
n = 19,r2 = 0.876,

s = 0.197, q2 = 0.833

Checking the reliability of Mlog P we can add ClogP. Substituting this in the above QSAR
yields an equations with r2 = 0.853. Removing a third point (3-NH2) offers improvement.
log 1/C = -0.30 (±.17) Mlog P – 0.50 (±.20) σ+ + 4.20 (±.25)
n = 18,r2 = 0.879,

s = 0.201,

q2 = 0.833

Note that q 2 is now approaching r2 in value. This implies that dropping another point would havelittle effect on the correlation. A problem with the jackknifing procedure is that it forces the data
to fit the model equation one starts with. Now repeating the perm procedure with Mlog P, S+, F,
Es, B1 and B5, we cannot find any value in parameters other than Mlog P and σ+.
I. Plotting Data
Sometimes insight can be gained by plotting the data. Any two parameters can be plottedagainst each other by the command a graph b, i.e., 3 graph 6 gives some idea of the fit to themost important variables. In the present instance this is of little help. Of more help is thecommand predict Absolute values Descending order. Viewing the deviations does not reveal
any pattern.
A point of interest is the use of amino groups as substituents when the study is concernedwith anilines. It is surprising that two of the three amino groups are well fit. This is not what wenormally find.
J. Cross Validation
Cross validation is a means for avoiding poor quality or meaningless QSAR. We haveincluded two ways of viewing the problem. The program automatically calculates a cross63validated r2 (q2) and prints this out with each equation. One wants to see the normal r2 and q 2 in'reasonable' agreement, but exactly what amounts to reasonable is a matter of judgment. If they
are not close (say r2 = 0.90, q2 = 0.7) then jackknifing to remove one or more points may be
worthwhile.
q2 is basically a kind of warning that the QSAR may not be as good as implied by r 2. 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 //full 4 5 randomly drops 10% of the data points in 20 trials
using 17 data points and displays the QSAR. In these examples r2 ranges from 0.849 to 0.900.
One can also set an arbitrary percentage drop: 3 cross//full /omit=30% 4 5 which yields 4
QSAR with r2 from 0.792 to 0.891. In this example, 30% of the data points were randomly
dropped. Any percentage can be selected by inserting the proper number along with %.
In the above examples it is found that the coefficients with Mlog P and σ + vary but little
from our first QSAR with only two points omitted.
K. 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 seedata 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

64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 seeparameter 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.
L. 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 summary gives a view of the set
including the parameters that were studied in the derivation of the equation. Entering eq /rundisplays the stored equation. Entering predict 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. Depict , pictures the 2D structures which can be paged through by pressing return. A particular structure or set ofstructures can be viewed by depict # or depict 1 3,10; 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 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 more than 50% 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.
Then entering 3 reg B4 yields the bilinear relationship:log 1/C = 1.02(±0.36) ClogP - 1.75(±0.78) bilin (ClogP) + 1.30(± 1.29)
n = 34,r2 = 0.520,

s = 0.699

q2 = 0.454

Activity initially increases with increase in log P with slope (h) of 1.02 and then falls off linearly65with slope of -1.75. The optimum (log Po) listed above is actually rapidly calculated from theparabolic equation. To get the exact value enter 3 j B4. The optimum shown here is 5.27.
The above bilinear term actually represents: -1.75 (±0.78) log (β • 10ClogP + 1) where β =-5.43. Generally researchers use the form log (β • P + 1). Since many of our parameters are on alog 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.
Note that the Dev + and Dev - which represent the number of points below and above theregression line are not badly out of balance. The 95% confidence limits on the coefficients are
not unreasonable. Although the correlation is not high in terms of r2, it seems promising. Next
we need to see the effect of adding the steric and electronic terms. Inspecting the substituents wesee only one example (#15) that contains a strong electron withdrawing via resonancesubstituent. It is not surprising to see the QSAR does not contain a σ - or 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 this point 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 - X 2
followed by Clog P. A very small lowering of the standard deviation occurs on the addition of
B1-X, but a 3 parameter equation 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
that bulky 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 parameterequations in ClogP and ES-X2 one finds that the latter is more important. The coefficients are in
reasonable agreement 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 r2 values between the various substituents. The correlation (r2) between
ES-X2 and the electronic parameters is not high. Entering 3 reg 4 14 shows that, indeed,
electronic effects do not appear to be significant (note confidence limits on coefficients). The
66

substituents studied in this position are rather small. Our results suggest that using largerhydrophobic 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.
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 ±1.2.7
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 32, this provides a set inwhich 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, but the QSAR isvery bad. Note r2 and entering 3 reg B4 8 13 yields a very similar equation to that stored, butwith a lower r2. Destarring all data points and then removing two by the jackknifing procedure
produces the stored equation.
Considering this equation what should the next move be? Obviously the negative sign with
B1-R2 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
are correcting this deficiency (above #3500 in physical databank and above #3000 in the Bio
databank). Once a set has been loaded, one can explore it as we have done with the above
example.

67

M. Substituent Selection in Molecular Design
A critical problem in undertaking a program to modify a parent compound in a structureactivity study is the selection of suitable substituents.(see ref. 14, Ch. 13) Not only does one needconvenient access to the widest possible variety of substituents, but it is time saving to be sure if
a particular substituent is selected that it has a range of known substituent constants (σ P, σ P+,
σ P −, π, MR, etc.). At the start of such a process it is generally not possible to anticipate whichparameters will be significant in the final QSAR. To utilize this feature of the C-QSAR program
there are two commands that can be entered from the regression mode: parameter, or parameter/nolimit. Entering parameter produces a table of labels (same as the fetch command). Any groupcan be selected. For example, entering 2 16 (Pi, σ) the prompt asks for the minimum andmaximum to set a range for each parameter. We might enter -1 1 -1 1 to define the range for eachof the two parameters. After the data have been collected, enter seed and we find that only 206
substituents fall in this range.
In selecting a set of substituents for combinatorial synthesis it can be very important to limit
the range of π or possibly other parameters. Thus for π, one might set limits such as -1 to 3 for π
and let 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, 876
substituents are found. Notice that when entering more than one parameter, the data are orderedon 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 / 2 3 13 16 19 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
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 r2.

68

P1
MR
L
S-P
S-M

P1
.
256
256
256
256

MR
.127
.
256
256
256

B1
.026
.085
.
256
256

S-P
0.50
.052
.118
.
256

S-M
.275
.011
.062
.843
.

The above approach may be sufficient, but especially in Combinatorial Synthesis one wants
to be sure that data space is well explored and collinearity minimized. To deal with this problem
enter parameter /E /P (E denotes euclidian space and P stands for pick). From the displayed
table select parameters of interest and all substituents having all of these parameters will besequestered. In the present instance enter 2 12 13 14 16 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 (294) 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
and distance in euclidean space. Then enter star /d followed by the number of the selected
substituents of interest. Now enter corr 4 5 6 7 8 or any combination of these to explore thecollinearity among the selected substituents.
Another way to select the most effective substituents to break collinearity enter parameter/nolimit. Next, select 16 and 17 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 thenumber of substituents having both σ and σ+ (199), where r2 = 0.878. Next, use gett (gettransformation) and enter S+-S for the name of the new variable. This then is defined on theprompt by entering S-P+ - S-P. This becomes new parameter 5. The command sort /null /abs
/des and then 5 on the prompt orders the substituents in decreasing size of difference. Of course
one could treat any number of parameters in this 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,69

these electronic parameters are very valuable in providing mechanistic insight via QSAR7, 8, 14
and should be studied separately. There is a high degree of collinearity among them. In a way
this is helpful as it means that σP can detect the presence of electronic effects that later can be
explored with the other two parameters.
VIII.

UDRIVE and Masterfile

UDRIVE allows interactive, unified access to the software modules which calculate log P(oct), and molar refractivity. In addition, it accesses the measured properties in Masterfile, suchas log P values in octanol and other solvent systems, pKa, activity types, and also the calculated
McGowan molar volumes, molformula and molecular weight.
Entry of udrive will return a panel which has the cursor on the default entry by SMILES, 'S'.
Entry of the find command, F(ind), returns a list of other access routes, such as name, CAS
number, etc. Entry of 2 gives the prompt that a name is expected. For example, entering AZT at
the name prompt results in a depiction (DEPICT) of the structure and calculated and measuredlog P values. Other data on the THOR page of Masterfile can be accessed with the entry of d.
Note that other synonyms, such as zidovudine or retrovir, could have been entered to access thiscalculation and the stored data. Entry of c will retrieve the details of the log P calculation, suchas the contribution of each fragment and their interactions. Masterfile also lists the Activity Typeas 'Reverse Transcriptase Inhibitor' and 'Antiviral'.
IX System Crashes
Occasionally, the system crashes. First enter unlock and then QSAR followed by data Bio
or Data Phys. Sometimes, if errors are made in the search and show process, the system does not
display results. Enter q until one reaches the $, then reload by entering QSAR followed by data
bio or data phys.
X. Caveats
It must not be forgotten that the present databanks were created over a period of about 40
years by many individuals. The biological database was developed by the chance discovery ofdata sets that 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 beenchecked, but the values of the independent variables may have been improved (especially log P)
70since the original entry, but as of the present have not been checked. At present, we are restudying all this early work to see what improvements can be made.
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
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 are remedying 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 thearea of physical organic chemistry. This was done by going through the indices of a number ofthe 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 4,600 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 havenoticed 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 to rectify 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 variationin the substituent properties is poor. In other cases, a number of data points have had to beomitted and sometimes 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 helpfulrole. When a QSAR for a similar set of chemicals acting on a similar system can be found, aweak QSAR can be supported or refuted. Such information is more helpful than conventional
statistics.

71

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 feelthat the under parameterized equations may be more easily compared.
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 thesite of action. There is good reason to expect this to be related nonlinearly to log P. At thereceptor, one would expect more specific hydrophobic interactions of certain parts of the ligands
and these would not necessarily parallel those of the random walk process. Hydrophobic effects
at the receptor, would best be modeled by π of certain substituents. In rare examples it is possible
to find clear roles for both π and log P in the same equation, but usually one accepts some kindof "average" hydrophobic term. To really understand the problem studies would have to be madeat the isolated receptor, on cell culture and in the whole organism. Only a very few such studies
have been made.7
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.
A shortcoming in all of the current methods of calculating log P is that while the relativevalues may be reasonable, the absolute values may be off the mark. QSAR obtained with thesewill have false log Po and intercepts. The only way to be sure of avoiding this uncertainty is tomeasure at least one log P for the set (ideally the parent compound) and use this to adjust thecalculated values. When using the automatic method for obtaining log P (add ClogP) it is a goodidea to also use add mlogP to see if any measured values are available for comparison. Even if
only one or two values exist this does help in getting a better estimate of log Po. Log Po is ofparamount importance in understanding bioavailability in whole organism research.
Parameterizing for local hydrophobic effects, can be difficult. It is not unusual to findexamples where meta substituents show a hydrophobic effect and para substituents do not andthis problem can be complicated by ring flipping.24 Ideally the substituent parameter, π, can beused to check for these possibilities. However, our current automatic loading uses only π values

72measured for X-C6H5 or values calculated from simple benzene derivatives. If there is a strong
electron withdrawing group nearby, it will affect the πvalue of the substituent being

parameterized, especially if lone pair electrons are on the attachment atom of the substituent ofinterest. 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 forthe 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 theirapparent 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
is good 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 seriousnessof the problem. We have now developed a system for the automatic calculation of π values (CPi)that avoids the problem of electronic interactions.
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 q2 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 thecause behind the problems. There are four major reasons for outliers: 1. The mathematical model
may be incorrect.

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 ofeven a 'simple' cell that might affect the measured activity. Of course, this can be minimized by73

using relatively unreactive substituents. As we gain background from Comparative QSAR, we
expect that it will be possible in some instances to identify very similar chemicals operating bydifferent mechanisms. 25, 42 Although outliers are intimidating, they can be very helpful. If a
compound is more active than expected this can be a lead worth following up. If less active
than predicted, it provides information on a direction to be avoided.
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
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
100 years.

74

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