Function and structure-based screening of compounds, peptides and proteins to identify drug candidates.

[1]  J. Folkman.  Tumor angiogenesis: therapeutic implications. , 1971, The New England journal of medicine.

[2]  M. Karplus,et al.  Protein-folding dynamics , 1976, Nature.

[3]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1978, Archives of biochemistry and biophysics.

[4]  D. Basu.  Randomization Analysis of Experimental Data: The Fisher Randomization Test , 1980 .

[5]  J. Denekamp.  Endothelial cell proliferation as a novel approach to targeting tumour therapy. , 1982, British Journal of Cancer.

[6]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[7]  D. E. Patterson,et al.  Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies , 1988 .

[8]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[9]  R. Cramer,et al.  Validation of the general purpose tripos 5.2 force field , 1989 .

[10]  Marvin Johnson,et al.  Concepts and applications of molecular similarity , 1990 .

[11]  M. Karplus,et al.  Functionality maps of binding sites: A multiple copy simultaneous search method , 1991, Proteins.

[12]  J. Aqvist,et al.  A new method for predicting binding affinity in computer-aided drug design. , 1994, Protein engineering.

[13]  Hans-Joachim Böhm,et al.  The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[14]  Gennady M Verkhivker,et al.  Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. , 1995, Protein engineering.

[15]  T. Hansson,et al.  Estimation of binding free energies for HIV proteinase inhibitors by molecular dynamics simulations. , 1995, Protein engineering.

[16]  W. L. Jorgensen,et al.  AN EXTENDED LINEAR RESPONSE METHOD FOR DETERMINING FREE ENERGIES OF HYDRATION , 1995 .

[17]  R. Wade,et al.  Prediction of drug binding affinities by comparative binding energy analysis. , 1997, Journal of medicinal chemistry.

[18]  E. Shakhnovich,et al.  SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence , 1996 .

[19]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[20]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[21]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[22]  W. L. Jorgensen,et al.  Binding affinities for sulfonamide inhibitors with human thrombin using Monte Carlo simulations with a linear response method. , 1997, Journal of medicinal chemistry.

[23]  M. Gilson,et al.  A new class of models for computing receptor-ligand binding affinities. , 1997, Chemistry & biology.

[24]  W. Im,et al.  Continuum solvation model: Computation of electrostatic forces from numerical solutions to the Poisson-Boltzmann equation , 1998 .

[25]  Christopher W. Murray,et al.  Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model , 1998, J. Comput. Aided Mol. Des..

[26]  R L Lipnick.  Correlative and mechanistic QSAR models in toxicology. , 1999, SAR and QSAR in environmental research.

[27]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[28]  I. Kuntz,et al.  Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model , 1999 .

[29]  J. Apostolakis,et al.  Exhaustive docking of molecular fragments with electrostatic solvation , 1999, Proteins.

[30]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[31]  I. Muegge.  A knowledge-based scoring function for protein-ligand interactions: Probing the reference state , 2000 .

[32]  G. Klebe,et al.  Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.

[33]  A. Caflisch,et al.  Fragment-Based Flexible Ligand Docking by Evolutionary Optimization , 2001, Biological chemistry.

[34]  Y.Z. Chen,et al.  Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule , 2001, Proteins.

[35]  Scott Myers,et al.  Drug discovery—an operating model for a new era , 2001, Nature Biotechnology.

[36]  A. Good,et al.  3-D pharmacophores in drug discovery. , 2001, Current pharmaceutical design.

[37]  Y. Z. Chen,et al.  Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. , 2001, Journal of molecular graphics & modelling.

[38]  J. Bajorath.  Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening , 2001 .

[39]  I. Muegge.  Effect of ligand volume correction on PMF scoring , 2001, J. Comput. Chem..

[40]  A. Caflisch,et al.  Efficient electrostatic solvation model for protein‐fragment docking , 2001, Proteins.

[41]  Sung-Kwun Oh,et al.  The design of self-organizing Polynomial Neural Networks , 2002, Inf. Sci..

[42]  Luhua Lai,et al.  Further development and validation of empirical scoring functions for structure-based binding affinity prediction , 2002, J. Comput. Aided Mol. Des..

[43]  From Knowledge‐Based Potentials to Combinatorial Lead Design in Silico , 2002 .

[44]  P. Cohen,et al.  The specificities of protein kinase inhibitors: an update. , 2003, The Biochemical journal.

[45]  R. W. Hansen,et al.  The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.

[46]  C. Chuaqui,et al.  Successful shape-Based virtual screening: The discovery of a potent inhibitor of the type I TGFβ receptor kinase (TβRI) , 2003 .

[47]  M. Vieth,et al.  Synthesis and activity of new aryl- and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-beta type I receptor kinase domain. , 2003, Journal of medicinal chemistry.

[48]  E. Sausville,et al.  Issues and progress with protein kinase inhibitors for cancer treatment , 2003, Nature Reviews Drug Discovery.

[49]  Peter Kolb,et al.  Automated docking of highly flexible ligands by genetic algorithms: A critical assessment , 2004, J. Comput. Chem..

[50]  Chris Oostenbrink,et al.  A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6 , 2004, J. Comput. Chem..

[51]  J. Sebolt-Leopold,et al.  Targeting the mitogen-activated protein kinase cascade to treat cancer , 2004, Nature Reviews Cancer.

[52]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[53]  Johan Åqvist,et al.  Binding affinity prediction with different force fields: Examination of the linear interaction energy method , 2004, J. Comput. Chem..

[54]  Zhe Wang,et al.  APD: the Antimicrobial Peptide Database , 2004, Nucleic Acids Res..

[55]  Johannes C. Hermann,et al.  A combined QM/MM approach to protein--ligand interactions: polarization effects of the HIV-1 protease on selected high affinity inhibitors. , 2004, Journal of medicinal chemistry.

[56]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[57]  David E. Kim,et al.  Computational Alanine Scanning of Protein-Protein Interfaces , 2004, Science's STKE.

[58]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[59]  N. Paul,et al.  Recovering the true targets of specific ligands by virtual screening of the protein data bank , 2004, Proteins.

[60]  H. Villar,et al.  Strategies for Indirect Computer-Aided Drug Design , 1993, Pharmaceutical Research.

[61]  M. Rarey,et al.  FlexX‐Scan: Fast, structure‐based virtual screening , 2004, Proteins.

[62]  D. Chaplin,et al.  Combretastatin A4 phosphate: background and current clinical status , 2004, Expert opinion on investigational drugs.

[63]  Anthony E. Klon,et al.  Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. , 2004, Journal of medicinal chemistry.

[64]  Wei Deng,et al.  Predicting Protein‐Ligand Binding Affinities Using Novel Geometrical Descriptors and Machine‐Learning Methods. , 2004 .

[65]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[66]  G. Klebe,et al.  DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. , 2005, Journal of medicinal chemistry.

[67]  L. Wodicka,et al.  A small molecule–kinase interaction map for clinical kinase inhibitors , 2005, Nature Biotechnology.

[68]  R. Abagyan,et al.  Optimal docking area: A new method for predicting protein–protein interaction sites , 2004, Proteins.

[69]  J. Irwin,et al.  ZINC ? A Free Database of Commercially Available Compounds for Virtual Screening. , 2005 .

[70]  K. Merz,et al.  Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. , 2005, Journal of medicinal chemistry.

[71]  R. Abagyan,et al.  Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* , 2005, Molecular & Cellular Proteomics.

[72]  SHENG-YOU HUANG,et al.  An iterative knowledge‐based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials , 2006, J. Comput. Chem..

[73]  Z. Xiang,et al.  Advances in homology protein structure modeling. , 2006, Current protein & peptide science.

[74]  G. Kroemer,et al.  Current development of mTOR inhibitors as anticancer agents , 2006, Nature Reviews Drug Discovery.

[75]  Didier Rognan,et al.  sc-PDB: an Annotated Database of Druggable Binding Sites from the Protein Data Bank , 2006, J. Chem. Inf. Model..

[76]  A. Caflisch,et al.  Automatic and efficient decomposition of two-dimensional structures of small molecules for fragment-based high-throughput docking. , 2006, Journal of medicinal chemistry.

[77]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[78]  Jian Zhang,et al.  Peptide deformylase is a potential target for anti‐Helicobacter pylori drugs: Reverse docking, enzymatic assay, and X‐ray crystallography validation , 2006, Protein science : a publication of the Protein Society.

[79]  Ramkumar Hariharan,et al.  Detailed Comparison of the Protein-Ligand Docking Efficiencies of GOLD, a Commercial Package and ArgusLab, a Licensable Freeware , 2006, Silico Biol..

[80]  Xiaoqin Zou,et al.  An iterative knowledge‐based scoring function to predict protein–ligand interactions: II. Validation of the scoring function , 2006, J. Comput. Chem..

[81]  A. Tropsha,et al.  Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. , 2006, Journal of medicinal chemistry.

[82]  L. Meijer,et al.  Inverse in silico screening for identification of kinase inhibitor targets. , 2007, Chemistry & biology.

[83]  V. Luzhkov,et al.  Virtual screening and bioassay study of novel inhibitors for dengue virus mRNA cap (nucleoside-2'O)-methyltransferase. , 2007, Bioorganic & medicinal chemistry.

[84]  W. Guida,et al.  Selective chemical probe inhibitor of Stat3, identified through structure-based virtual screening, induces antitumor activity , 2007, Proceedings of the National Academy of Sciences.

[85]  Jaques Reifman,et al.  DOVIS: an implementation for high-throughput virtual screening using AutoDock , 2008, BMC Bioinformatics.

[86]  Joannis Apostolakis,et al.  GlamDock: Development and Validation of a New Docking Tool on Several Thousand Protein-Ligand Complexes , 2007, J. Chem. Inf. Model..

[87]  T. Scior,et al.  Application of drug repositioning strategy to TOFISOPAM. , 2008, Current medicinal chemistry.

[88]  J. Beckwith,et al.  Bacterial species exhibit diversity in their mechanisms and capacity for protein disulfide bond formation , 2008, Proceedings of the National Academy of Sciences.

[89]  A. Caflisch,et al.  Is quantum mechanics necessary for predicting binding free energy? , 2008, Journal of medicinal chemistry.

[90]  Nikolay V. Dokholyan,et al.  MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening , 2008, J. Chem. Inf. Model..

[91]  C. Shekhar.  In silico pharmacology: computer-aided methods could transform drug development. , 2008, Chemistry & biology.

[92]  Noeris K. Salam,et al.  Novel PPAR‐gamma Agonists Identified from a Natural Product Library: A Virtual Screening, Induced‐Fit Docking and Biological Assay Study , 2007, Chemical biology & drug design.

[93]  Claudio N. Cavasotto,et al.  Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. , 2008, Journal of medicinal chemistry.

[94]  V. Hruby,et al.  Peptidomimetics, a synthetic tool of drug discovery. , 2008, Current opinion in chemical biology.

[95]  Solène Grosdidier,et al.  Identification of hot-spot residues in protein-protein interactions by computational docking , 2008, BMC Bioinformatics.

[96]  R. Dror,et al.  Long-timescale molecular dynamics simulations of protein structure and function. , 2009, Current opinion in structural biology.

[97]  Jennifer L. Martin,et al.  DSB proteins and bacterial pathogenicity , 2009, Nature Reviews Microbiology.

[98]  James Andrew McCammon,et al.  A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach , 2009, Comput. Biol. Chem..

[99]  Joo Chuan Tong,et al.  Recent advances in computer-aided drug design , 2009, Briefings Bioinform..

[100]  Thomas A. Halgren,et al.  Identifying and Characterizing Binding Sites and Assessing Druggability , 2009, J. Chem. Inf. Model..

[101]  B. Heras,et al.  Characterization of Two Homologous Disulfide Bond Systems Involved in Virulence Factor Biogenesis in Uropathogenic Escherichia coli CFT073 , 2009, Journal of bacteriology.

[102]  François Stricher,et al.  PepX: a structural database of non-redundant protein–peptide complexes , 2009, Nucleic Acids Res..

[103]  Xiaoqin Zou,et al.  Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein-Ligand Interactions , 2010, J. Chem. Inf. Model..

[104]  A. Bonvin,et al.  The HADDOCK web server for data-driven biomolecular docking , 2010, Nature Protocols.

[105]  Sheng-Yong Yang,et al.  Pharmacophore modeling and applications in drug discovery: challenges and recent advances. , 2010, Drug discovery today.

[106]  Jacob D. Durrant,et al.  NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes , 2010, J. Chem. Inf. Model..

[107]  John B. O. Mitchell,et al.  A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking , 2010, Bioinform..

[108]  Dominique Douguet,et al.  e-LEA3D: a computational-aided drug design web server , 2010, Nucleic Acids Res..

[109]  G. Koren,et al.  Colchicine poisoning: the dark side of an ancient drug , 2010, Clinical toxicology.

[110]  T. Pruett.  Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America , 2010 .

[111]  B. Baguley,et al.  Disrupting established tumor blood vessels , 2010, Cancer.

[112]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[113]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[114]  V. Sperandio,et al.  Anti-virulence strategies to combat bacteria-mediated disease , 2010, Nature Reviews Drug Discovery.

[115]  Gerhard Klebe,et al.  DSX: A Knowledge-Based Scoring Function for the Assessment of Protein-Ligand Complexes , 2011, J. Chem. Inf. Model..

[116]  Yuan Zhao,et al.  Automatic Tailoring and Transplanting: A Practical Method that Makes Virtual Screening More Useful , 2011, J. Chem. Inf. Model..

[117]  Christoph A. Sotriffer,et al.  Virtual screening : principles, challenges, and practical guidelines , 2011 .

[118]  Jacob D. Durrant,et al.  NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function , 2011, J. Chem. Inf. Model..

[119]  P. Willett,et al.  PHARMACOPHORE PERCEPTION , DEVELOPMENT , AND USE IN DRUG DESIGN , 2011 .

[120]  Alexander D. MacKerell,et al.  Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. , 2011, Current computer-aided drug design.

[121]  Ora Schueler-Furman,et al.  Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions , 2011, Nucleic Acids Res..

[122]  Marc Devocelle,et al.  CycloPs: Generating Virtual Libraries of Cyclized and Constrained Peptides Including Nonnatural Amino Acids , 2011, J. Chem. Inf. Model..

[123]  Ryan G. Coleman,et al.  ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..

[124]  Andreas Bender,et al.  Recognizing Pitfalls in Virtual Screening: A Critical Review , 2012, J. Chem. Inf. Model..

[125]  Albert C. Pan,et al.  Structure and Dynamics of the M3 Muscarinic Acetylcholine Receptor , 2012, Nature.

[126]  G. Bifulco,et al.  Inverse Virtual Screening allows the discovery of the biological activity of natural compounds. , 2012, Bioorganic & medicinal chemistry.

[127]  Gianluca Pollastri,et al.  PeptideLocator: prediction of bioactive peptides in protein sequences , 2013, Bioinform..

[128]  Reed B. Jacob,et al.  DockoMatic 2.0: High Throughput Inverse Virtual Screening and Homology Modeling , 2013, J. Chem. Inf. Model..

[129]  Premendu P. Mathur,et al.  PepBind: A Comprehensive Database and Computational Tool for Analysis of Protein–peptide Interactions , 2013, Genom. Proteom. Bioinform..

[130]  Albert C. Pan,et al.  The Dynamic Process of β2-Adrenergic Receptor Activation , 2013, Cell.

[131]  Christoph A. Sotriffer,et al.  SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes , 2013, J. Chem. Inf. Model..

[132]  Zheng Zheng,et al.  Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein-Ligand Interactions , 2013, J. Chem. Inf. Model..

[133]  Xia Wang,et al.  iDrug: a web-accessible and interactive drug discovery and design platform , 2014, Journal of Cheminformatics.

[134]  Matthias Rarey,et al.  Facing the Challenges of Structure-Based Target Prediction by Inverse Virtual Screening , 2014, J. Chem. Inf. Model..

[135]  Pierre Tufféry,et al.  PEP-SiteFinder: a tool for the blind identification of peptide binding sites on protein surfaces , 2014, Nucleic Acids Res..

[136]  María Martín,et al.  Activities at the Universal Protein Resource (UniProt) , 2013, Nucleic Acids Res..

[137]  Tom L. Blundell,et al.  Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity? , 2014, J. Chem. Inf. Model..

[138]  S. Noppen,et al.  Novel colchicine-site binders with a cyclohexanedione scaffold identified through a ligand-based virtual screening approach. , 2014, Journal of medicinal chemistry.

[139]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[140]  Vincent Zoete,et al.  Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function , 2014, J. Chem. Inf. Model..

[141]  Gajendra P. S. Raghava,et al.  CancerPPD: a database of anticancer peptides and proteins , 2014, Nucleic Acids Res..

[142]  Yolanda T. Chong,et al.  CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae , 2015, G3: Genes, Genomes, Genetics.

[143]  Martin Zacharias,et al.  Fully Blind Peptide-Protein Docking with pepATTRACT. , 2015, Structure.

[144]  Bernhard Y. Renard,et al.  Docking small peptides remains a great challenge: an assessment using AutoDock Vina , 2015, Briefings Bioinform..

[145]  Jie Liu,et al.  Classification of Current Scoring Functions , 2015, J. Chem. Inf. Model..

[146]  Claudio N. Cavasotto,et al.  Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. , 2015, Archives of biochemistry and biophysics.

[147]  Gajendra P. S. Raghava,et al.  SATPdb: a database of structurally annotated therapeutic peptides , 2015, Nucleic Acids Res..

[148]  David Ryan Koes,et al.  Pharmit: interactive exploration of chemical space , 2016, Nucleic Acids Res..

[149]  Björn Windshügel,et al.  LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance , 2016, J. Chem. Inf. Model..

[150]  P. Ramasami,et al.  Crystallizing Ideas – The Role of Chemistry , 2016 .

[151]  Sumudu P Leelananda,et al.  Computational methods in drug discovery , 2016, Beilstein journal of organic chemistry.

[152]  Feng-Xu Wu,et al.  ACFIS: a web server for fragment-based drug discovery , 2016, Nucleic Acids Res..

[153]  W. Rocchia,et al.  dMM-PBSA: A New HADDOCK Scoring Function for Protein-Peptide Docking , 2016, Front. Mol. Biosci..

[154]  Anna Tramontano,et al.  PepComposer: computational design of peptides binding to a given protein surface , 2016, Nucleic Acids Res..

[155]  Pierre Tufféry,et al.  PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex , 2016, Nucleic Acids Res..

[156]  Sona Warrier,et al.  Inverse Virtual Screening in Drug Repositioning: Detailed Investigation and Case Studies , 2016 .

[157]  Alan Wee-Chung Liew,et al.  Sequence‐based prediction of protein–peptide binding sites using support vector machine , 2016, J. Comput. Chem..

[158]  Xianjin Xu,et al.  The Usage of ACCLUSTER for Peptide Binding Site Prediction. , 2017, Methods in molecular biology.