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Timestamp: 2019-04-23 02:09:13+00:00

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Structural Preferences, Argon Nanocoating, and Dimerization of n-Alkanols As Revealed by OH Stretching Spectroscopy in Supersonic Jets. Tobias N.
An outstanding example of structural diversity and complexity is found in the compounds with the general formula ABi3Q5 (A = alkali metal; Q = chalcogen).
1. INTRODUCTION Since the seminal publications of Lipinski and co-workers,1 library design and lead optimization eﬀorts in small molecule drug discovery have included druglikeness considerations from the hit stage to candidate selection.221 The objective of this work was to systematically analyze druglikeness at the atomic and molecular levels, including atom type preferences and intrinsic structural diversity or Atom Type Diversity (ATD), of drugs, leads, and nondrugs (commercially available small molecule collections). Lipinski’s Rule of 5 (Ro5),1 GhoseViswanadhan Wendoloski (GVW) criteria,3,4 and other drug/nondrug comparisons7,8 were reassessed. Structural properties examined in detail include ATD, calculated log P (based on ALOGP method, ALOGP98),22 molar refractivity (AMR89),23 atom counts, and molecular weight, along with several others. Intrinsic structural diversity, also called atom type diversity (ATD), is shown to be a key diﬀerentiator of drugs and leads relative to nondrugs. A new and elaborate atom type representation, UALOGP (United Atom Log P) derived from ALOGP method,22,23 is described along with corresponding atomic physicochemical parameters for a detailed characterization of the property ranges, constitutional makeup, and structural diversity of drugs.
Values of RDPi > 1 indicate preferred types in drugs, while values <1 indicate the opposite. (b). Molecular Databases. For the present analysis, three sources of drug molecules were considered: (i) a database of FDA approved drugs, available as part of ZINC databases;25 (ii) the DrugBank,26 a database of nearly 4800 entries including over 1,350 FDA-approved small molecule drugs, 123 FDA-approved biotech (protein/peptide) drugs, and 71 nutraceuticals and over 3,243 experimental drugs; and (iii) JBLDrugDB,42 a small, wellannotated proprietary database of FDA approved drugs. These databases were filtered to exclude highly lipophilic (calculated log P > 8.0) and highly hydrophilic compounds (calculated log P < 5.0), similar to earlier analyses.3,4 We also excluded compounds such as nutraceuticals and protein/peptide drugs from our analysis, as well unusually small (<100 MW or <14 atoms or <10 heavy atoms) or large (>800 MW or >100 atoms) molecules. Also removed were polymers, peptides, quaternary ammonium, multiple acids, and phosphates. This additional filtering excluded entries not of particular interest as small molecule drugs. Two sets of 470 lead-drug pairs generated by Hann and coworkers28 were also analyzed to delineate diﬀerences between lead molecules and the corresponding drugs. The lead database was not subjected to ﬁltering. The present work also required an analysis of a “nondrug” database for an atomic level druglikeness assessment. Of several commercially available compound databases, we chose the Chembridge27 database, which is quite large (the latest version contains over 700,000 compounds, which includes both target class and general libraries) and extensively used for screening in the pharmaceutical industry. Two independent random sets of 10000 molecules each were picked from an earlier version of the data set and subjected the data set to the ﬁltering procedure mentioned in the methodology. This was necessary to reduce the size of nondrug data set for practical purposes. This marginally reduced the number of compounds to 9969 and 9965 for the two sets. One of the two sets was used as the nondrug data set. We also analyzed the drug and nondrug databases employed by Hutter7 for comparison. (c). Atom Types and Atomic Properties. The new united atom type representation (UALOGP) introduced here was derived from the ALOGP representation,22,23 with greater elaboration and distinctions of heavy atom types, grouped with bonded hydrogen atoms. This led to a total of 148 heavy atom types, based on the number and types of added hydrogens for each heavy atom. For example, a methyl carbon (ALOGP type 1) belongs to one of five united atom types (1a1e), which were created based on five different types of attached hydrogens. Shown in Table 1 are UALOGP group contributions derived from atomic constants for lipophilicity (log P) and molar refractivity (MR) developed by Ghose et al22 and Viswanadhan et al.23 (d). Atom Type Diversity. Characterization of intrinsic structural diversity was based on the concept that atom classification is hierarchical, with elemental types at the primary level. ALOGP22 and UALOGP classifications constituted secondary and tertiary levels of finer differentiation. Hydrogen atoms were considered implicit, and only heavy atoms were used for assessment. On the basis of these, three structural diversity measures were defined for a molecule with NHATS heavy atoms.
The original ALOGP atom types22 are identiﬁed, for each corresponding subset of UALOGP types. b R represents any group linked through carbon; X represents any heteroatom (O, N, S, P, Se, and halogens); Al and Ar represent aliphatic and aromatic groups, respectively; “d” represents a double bond; “t” represents a triple bond; “- -” represents a aromatic bonds as in benzene or delocalized bonds such as the NO bond in a nitro group; “ 3 3 ” represents aromatic single bonds as the CN bond in pyrrole. The C- -N bond order in pyridine may be considered as 2 while we have one such bond and 1.5 when we have two such bonds.
where i refers to the atom type, pi,d is the % occurrence of type i in the drug database and pi,n is the % occurrence of type i for the nondrug database (an approximation for expectation value, based on a typical distribution in commercial small molecule collections).
This deﬁnition ensured equal weight to each level of atom classiﬁcation.
Figure 1. (a) ALOGP98, (b) AMR89, (c) MW, and (d) NATS ranges covering diﬀerent fractions of each database. The middle bright colored part covers 50% of each database. Dark colored extensions on either side constitute another 30%, covering 80% range, and another 15% is added by further light colored extensions, covering 95% range. Databases shown are (A) Drugs_All, (B) Drugs from leads, (C) leads, (D) JBLDrugDB, (E) DrugBank, (F) FDA approved drugs, and (G) nondrugs.
Figure 2. Histogram distributions of (a) ALOGP98, (b) AMR89, (c) MW, and (d) NATS for the six databases: A, (magenta) Drugs_All; B, (green) Drugs from Leads; C, (light brown) leads; D, (purple) JBLDrugDB; E, (dark brown) DrugBank; F, (light blue) FDA approved drugs, and G, (dark blue) nondrugs.
Figure 3. Distribution of ATD scores for (a) drugs (Drugs_All) and nondrugs (b) drug and lead pairs used in this study.
Figure 4. Most frequently occurring united atom types of carbon, oxygen, and nitrogen represented as colored spheres in the chemistry space of UALOGP property values, with atomic/group values of lipophilicity22 along the X-axis and molar refractivity23 along the Y-axis. Sphere size proportional to (a) RDPi and (b) relative frequency of occurrence in drugs.
demonstrate the signiﬁcance of using an elaborate atom classiﬁcation as well as relevant nondrug data sets.
Supporting Information. Physico-chemical property ranges of various databases (Table S1), comparison of drug-lead pairs (Table S2), Descriptors for computing druglike index (Table S3), PCA components of descriptors (Table S4), and Relative druglikeness and other properties of atom types (Table S5). Histogram distributions of ALOGP98, AMR89, MW, and NATS for two sets of drugs and nondrugs (Figure S1), Comparison of DLI distributions for drugs and nondrugs (Figure S2), Scatter plot of PC’s for drugs and nondrugs (Figure S3). This material is available free of charge via the Internet at http://pubs. acs.org.
(b) Bemis, G. W.; Murcko, M. A. Properties of Known Drugs. 2. Side Chains. J. Med. Chem. 1999, 42, 5095–5099. (17) Wang, J.; Ramnarayan, K. Towards Designing Drug-Like Libraries: A Novel Computational Approach for Prediction of Drug Feasibility of Compounds. J. Comb. Chem. 1999, 1, 524–533. (18) Wagener, M.; van Geerestein, V. J. Potential Drugs and Nondrugs: Prediction and Identiﬁcation of Important Structural Features. J. Chem. Inf. Comput. Sci. 2000, 40, 280–292. (19) Rishton, G. M. Reactive Compounds and in vitro False Positives in HTS. Drug Discovery Today 1997, 2, 382–384. (20) Kutchukian, P. S.; Lou, D.; Shakhnovich, E. I. FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules Occupying Druglike Chemical Space. J. Chem. Inf. Model. 2009, 49 (7), 1630–1642. (21) Frimurer, T. M.; Bywater, R.; Nærum, L.; Lauritsen, Leif. N.; Brunak, S. Improving the Odds in Discriminating “Drug-like” from “Non Drug-like” Compounds. J. Chem. Inf. Comput. Sci. 2000, 40 (6), 1315–1324. (22) Ghose, A. K.; Viswanadhan, V. N.; Wendoloski, J. J. Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of ALOGP and CLOGP Methods. J. Phys. Chem. A 1998, 102, 3762–3772. (23) Viswanadhan, V. N.; Ghose, A. K.; Revankar, G. R.; Robins, R. K. Atomic Physicochemical Parameters for Three Dimensional Structure Directed Quantitative Structure-Activity Relationships. 4. Additional Parameters for Hydrophobic and Dispersive Interactions and Their Application for an Automated Superposition of Certain Naturally Occurring Nucleoside Antibiotics. J. Chem. Inf. Comput. Sci. 1989, 29, 163–172. (24) Crippen, G. M.; Wildman, S. A. Prediction of Physicochemical Parameters by Atomic Contributions. J. Chem. Inf. Comput. Sci. 1999, 39, 868–873. (25) Irwin, J. J.; Shoichet, B. K. ZINC - A Free Database of Commercially Available Compounds for Virtual Screening. J. Chem. Inf. Model. 2005, 45 (1), 177–182. (26) Wishart, D. S.; Knox, C.; Guo, A. C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006, Jan 1; 34 (Database issue): D668D672. (27) www.chembridge.com. (28) Hann, M. M.; Leach, A. R.; Harper, G. Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. J. Chem. Inf. Comput. Sci. 2001, 41, 856–864. (29) Wenlock, M. C.; Austin, R. P.; Barton, P.; Davis, A. M.; Leeson, P. D. A Comparison of Physiochemical Property Proﬁles of Development and Marketed Oral Drugs. J. Med. Chem. 2003, 46, 1250–1256. (30) Ritchie, T. J.; Luscombe, C. N.; Macdonald, S. J. F. Analysis of the Calculated Physicochemical Properties of Respiratory Drugs: Can We Design for Inhaled Drugs Yet? J. Chem. Inf. Model. 2009, 49 (4), 1025–1032. (31) Lajiness, M. S.; Vieth, M.; Erickson, J. Molecular properties that inﬂuence oral drug-like behavior. Curr. Opin. Drug Discovery Dev. 2004, 7, 470–477. (32) Durant, J. L.; Leland, B. A.; Henry, D. R.; Nourse, J. G. Reoptimization of MDL Keys for Use in Drug Discovery. J. Chem. Inf. Comput. Sci. 2002, 42 (6), 1273–1280. (33) (a) Ghose, A. K.; Crippen, G. M. Atomic Physicochemical Parameters for Three-Dimensional Structure-Directed Quantitative Structure-Activity Relationships I. Partition Coeﬃcients as a Measure of Hydrophobicity. J. Comput. Chem. 1986, 7, 565–577. (b) Ghose, A. K.; Crippen, G. M. Atomic Physicochemical Parameters for ThreeDimensional-Structure-Directed Quantitative Structure-Activity Relationships. 2. Modeling Dispersive and Hydrophobic Interactions. J. Chem. Inf. Comput. Sci. 1987, 27, 21–35. (34) Leo, A. J. The History of the development of CLOGP; available at http://www.daylight.com/meetings/mug98/Leo/clogp_history.html. (35) Viswanadhan, V. N.; Ghose, A. K.; Weinstein, J. N. Mapping the Binding Site of the Nucleoside Transporter Protein: A 3D-QSAR Study. Biochim. Biophys. Acta 1990, 1039 (3), 356–366.
(36) Viswanadhan, V. N.; Reddy, M. R.; Wlodawer, A.; Varney, M. D.; Weinstein, J. N. An Approach to Rapid Estimation of Relative Binding Aﬃnities of Enzyme Inhibitors: Application to Peptidomimetic Inhibitors of the Human Immunodeﬁciency Virus Type 1 Protease. J. Med. Chem. 1996, 39, 705–712. (37) Viswanadhan, V. N.; Ghose, A. K.; Singh, U. C.; Wendoloski, J. J. Prediction of Solvation Free Energies of Small Organic Molecules: Additive-Constitutive Models Based on Molecular Fingerprints and Atomic Constants. J. Chem. Inf. Comput. Sci. 1999, 39 (2), 405–412. (38) Ghose, A. K.; Viswanadhan, V. N.; Sanghvi, Y. S.; Dee Nord, L.; Willis, R. C.; Revankar, G. R.; Robins, R. K. Structural Mimicry of Adenosine by the Antitumor Agents 4-methoxy- and 4-amino-8-(β-Dribofuranosylamino)pyrimido[5,4-d]pyrimidine as Viewed by a Molecular Modeling Method. Proc. Natl. Acad. Sci. 1989, 86, 8242–8246. (39) Ghose, A. K.; Grippen, G. M.; Revankar, G. R.; McKernan, P. A.; Smee, D. F.; Robins, R. K. Analysis of the in Vitro Antiviral Activity of Certain Ribonucleosides against Parainﬂuenza Virus Using a Novel Computer Aided Receptor Modeling Procedure. J. Med. Chem. 1989, 32, 746–756. (40) Egan, W. J.; Merz, K. M., Jr.; Baldwin, J. J. Prediction of Drug Absorption Using Multivariate Statistics. J. Med. Chem. 2000, 43, 3867–3877. (41) Shelley, J. C.; Cholleti, A.; Frye, L. L.; Greenwood, J. R.; Timlin, M. R.; Uchimaya, M. Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des. 2007, 21, 681–691. (42) A highly annotated database of approved drugs developed at Jubilant Biosys (www.jubilantbiosys.com). (43) (a) Oprea, T. I.; Davis, A. M.; Teague, S. J.; Leeson, P. D. Is There a Diﬀerence between Leads and Drugs? A Historical Perspective. J. Chem. Inf. Comput. Sci. 2001, 41 (5), 1308–1315. (b) Oprea, T. I.; Allu, T. K.; Fara, D. C.; Rad, R. F.; Ostopovici, L.; Bologa, C. G. Leadlike, Druglike or Publike: How diﬀerent are they? J. Comput.-Aided Mol. Des. 2007, 21, 113–119. (44) Rishton, G. M. Molecular Diversity in the Context of Leadlikeness: Compound Properties that Enable Eﬀective Biochemical Screening. Curr. Opin. Chem. Biol. 2008, 12, 1–12.

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