1521-0081/66/1/334–395$25.00 http://dx.doi.org/10.1124/pr.112.007336 PHARMACOLOGICAL REVIEWS Pharmacol Rev 66:334–395, January 2014 Copyright © 2013 by The American Society for Pharmacology and Experimental Therapeutics ASSOCIATE EDITOR: ERIC L. BARKER Computational Methods in Drug Discovery Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, and Edward W. Lowe, Jr. Meiler Laboratory, Center for Structure Biology, Vanderbilt University, Nashville, Tennessee Abstract. ....................................................................................336 I. Introduction. ..............................................................................336 Downloaded from A. Position of Computer-Aided Drug Design in the Drug Discovery Pipeline .................337 B. Ligand Databases for Computer-Aided Drug Design . .....................................339 1. Preparation of Ligand Libraries for Computer-Aided Drug Design . ...................339 2. Representation of Small Molecules as “SMILES” ......................................340 3. Small Molecule Representations for Modern Search Engines: InChIKey . .............341 pharmrev.aspetjournals.org C. Target Data Bases for Computer-Aided Drug Discovery/Design . .........................341 D. Benchmarking Techniques of Computer-Aided Drug Design ..............................342 II. Structure-Based Computer-Aided Drug Design ...............................................342 A. Preparation of a Target Structure. ......................................................342 1. Comparative Modeling . ............................................................343 a. Template identification and alignment . ..........................................343 b. Model building. ..................................................................343 c. Model refinement and evaluation. ................................................345 d. Model data bases .................................................................345 at Vanderbilt Univ (EBSCO)Eskind Biomed Lib on January 3, 2014 e. Example application in computer-aided drug design ...............................345 2. Binding Site Detection and Characterization ..........................................345 a. Geometric method ................................................................345 b. Example application in computer-aided drug design ...............................346 c. Energy-based approaches . ......................................................346 d. Example application in computer-aided drug design ...............................346 e. Pocket matching ..................................................................346 f. Molecular dynamics-based detection ...............................................346 g. Example application in computer-aided drug design ...............................347 B. Representing Small Molecules and Target Protein for Docking Simulations . .............347 C. Sampling Algorithms for Protein-Ligand Docking . .....................................347 1. Systematic Methods ..................................................................347 a. Example application in computer-aided drug design ...............................348 2. Molecular Dynamics Simulations .....................................................348 3. Monte Carlo Search with Metropolis Criterion . .....................................349 a. Example application in computer-aided drug design ...............................349 4. Genetic Algorithms. ..................................................................350 a. Example application in computer-aided drug design ...............................350 5. Incorporating Target Flexibility in Docking . ..........................................350 D. Scoring Functions for Evaluation Protein-Ligand Complexes..............................350 1. Force-Field or Molecular Mechanics-Based Scoring Functions .........................350 2. Empirical Scoring Functions..........................................................351 3. Knowledge-Based Scoring Function . ................................................351 This work was supported by the National Science Foundation through the Office for Cyber Infrastructure Transformative Computational Sciences Fellowship [OCI-1122919] (E.W.L.); the National Institutes of Health National Institute of Mental Health [Grant R01 MH090192] (to the Meiler laboratory); the National Institutes of Health National Institute of General Medical Sciences [Grant R01 GM099842]; and the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases [Grant R01 DK097376]. Address correspondence to: Dr. Edward W. Lowe, Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725. E-mail: [email protected] dx.doi.org/10.1124/pr.112.007336 334 Computational Methods in Drug Discovery 335 4. Consensus-Scoring Functions . ......................................................351 a. Example application in computer-aided drug design ...............................351 E. Structure-Based Virtual High-Throughput Screening .....................................351 1. Inhibitors of Hsp90. ..................................................................352 2. Discovery of M1 Acetylcholine Receptor Agonists . .....................................352 F. Atomic-Detail/High-Resolution Docking . ................................................352 1. Inhibitors of Casein Kinase by Hierarchical Docking . ...............................352 2. Discovery of Peroxisome Proliferator-Activated Receptor g Agonists ...................353 3. Discovery of Novel Serotonin Receptor Agonists . .....................................353 4. Molecular Dynamics for High-Resolution Docking .....................................354 G. Binding Site Characterization ...........................................................355 1. Helicase Inhibitor . ..................................................................356 H. Pharmacophore Model . ..................................................................357 1. Virtual Screening Using a Pharmacophore Model .....................................357 2. Multitarget Inhibitors Using Common Pharmacophore Models ........................358 3. Dynamic Pharmacophore Models that Account for Protein Flexibility ..................358 I. Automated De Novo Design of Ligands. ................................................358 1. Example Application in Computer-Aided Drug Design. ...............................359 J. Strategies for Important Classes of Drug Targets.........................................360 III. Ligand-Based Computer-Aided Drug Design . ................................................361 A. Molecular Descriptors/Features ..........................................................362 1. Functional Groups . ..................................................................362 2. Prediction of Psychochemical Properties ..............................................362 a. Electronegativity and partial charge...............................................363 b. Polarizability . ..................................................................364 c. Octanol/water partition coefficient . ................................................364 3. Converting Properties into Descriptors................................................365 a. Binary molecular fingerprints .....................................................365 b. 2D description of molecular constitution . ..........................................365 c. 3D Description of molecular configuration and conformation .......................366 B. Molecular Fingerprint and Similarity Searches. ..........................................367 1. Similarity Searches in LB-CADD .....................................................367 2. Polypharmacology: Similarity Networks and Off-Target Predictions ...................368 3. Fingerprint Extensions . ............................................................368 C. Quantitative Structure-Activity Relationship Models .....................................369 1. Multidimensional QSAR: 4D and 5D Descriptors . .....................................369 2. Receptor-Dependent 3D/4D-QSAR ....................................................369 3. Linear Regression and Related Methods ..............................................370 4. Nonlinear Models Using Machine Learning Algorithms ...............................370 5. Quantitative Structure-Activity Relationship Application in Ligand-Based Computer-Aided Drug Design . ......................................................371 D. Selection of Optimal Descriptors/Features................................................374 E. Pharmacophore Mapping ................................................................375 1. Superimposing Active Compounds to Create a Pharmacophore ........................375 2. Pharmacophore Feature Extraction . ................................................376 3. Pharmacophore Algorithms and Software Packages ...................................376 ABBREVIATIONS: 3D, three dimensional; 11b-HSD1, 11b-hydroxysteroid dehydrogenase; ACD, Available Chemical Directory; ACE, angiotensin-converting enzyme; ADMET, absorption, distribution, metabolism, and excretion and the potential for toxicity; ADR, adverse drug reaction; ANN, artificial neural networks; CADD, computer-aided drug discovery/design; CK2, casein kinase 2; CPE, chemical penetration enhancers; DMPK, drug metabolism and pharmacokinetics; GPCR,