The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening

[1]  Zsolt Zsoldos,et al.  Software tools for structure based rational drug design , 2003 .

[2]  Glen Eugene Kellogg,et al.  Very empirical treatment of solvation and entropy: a force field derived from Log Po/w , 2001, J. Comput. Aided Mol. Des..

[3]  Stefan Güssregen,et al.  Evidence for C-Cl/C-Br...pi interactions as an important contribution to protein-ligand binding affinity. , 2009, Angewandte Chemie.

[4]  Jiro Shimada,et al.  Bootstrap-Based Consensus Scoring Method for Protein-Ligand Docking , 2008, J. Chem. Inf. Model..

[5]  B. Berne,et al.  Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. , 2008, Journal of the American Chemical Society.

[6]  Gerhard Klebe,et al.  Tracing changes in protonation: a prerequisite to factorize thermodynamic data of inhibitor binding to aldose reductase. , 2007, Journal of molecular biology.

[7]  R. Clark,et al.  Consensus scoring for ligand/protein interactions. , 2002, Journal of molecular graphics & modelling.

[8]  Eric J Martin,et al.  Target-biased scoring approaches and expert systems in structure-based virtual screening. , 2004, Current opinion in chemical biology.

[9]  M Rarey,et al.  Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.

[10]  Holger Gohlke,et al.  Target flexibility: an emerging consideration in drug discovery and design. , 2008, Journal of medicinal chemistry.

[11]  Wei Zhao,et al.  A statistical framework to evaluate virtual screening , 2009, BMC Bioinformatics.

[12]  Cornel Catana,et al.  Novel, Customizable Scoring Functions, Parameterized Using N-PLS, for Structure-Based Drug Discovery , 2007, J. Chem. Inf. Model..

[13]  Cristiano Ruch Werneck Guimarães,et al.  MM-GB/SA Rescoring of Docking Poses in Structure-Based Lead Optimization , 2008, J. Chem. Inf. Model..

[14]  A. Nicholls,et al.  How to do an evaluation: pitfalls and traps , 2008, J. Comput. Aided Mol. Des..

[15]  Jennifer R. Krumrine,et al.  Statistical tools for virtual screening. , 2005, Journal of medicinal chemistry.

[16]  C. Sander,et al.  Errors in protein structures , 1996, Nature.

[17]  E. Shakhnovich,et al.  SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. , 2002, Journal of medicinal chemistry.

[18]  Brian K Shoichet,et al.  Interpreting steep dose-response curves in early inhibitor discovery. , 2006, Journal of medicinal chemistry.

[19]  M Stahl,et al.  Development of filter functions for protein-ligand docking. , 1998, Journal of molecular graphics & modelling.

[20]  G. Klebe,et al.  pH‐Dependent Binding Modes Observed in Trypsin Crystals: Lessons for Structure‐Based Drug Design , 2002 .

[21]  Christine Humblet,et al.  Investigation of MM-PBSA Rescoring of Docking Poses , 2008, J. Chem. Inf. Model..

[22]  Daniel A. Gschwend,et al.  Analysis and optimization of structure-based virtual screening protocols. (3). New methods and old problems in scoring function design. , 2003, Journal of molecular graphics & modelling.

[23]  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..

[24]  Colin McMartin,et al.  QXP: Powerful, rapid computer algorithms for structure-based drug design , 1997, J. Comput. Aided Mol. Des..

[25]  R. Friesner,et al.  Generalized Born Model Based on a Surface Integral Formulation , 1998 .

[26]  Ajay N. Jain,et al.  Customizing scoring functions for docking , 2008, J. Comput. Aided Mol. Des..

[27]  C. M. Freeman,et al.  Lost hydrogen bonds and buried surface area: rationalising stability in globular proteins , 1993 .

[28]  G. Petsko,et al.  Weakly polar interactions in proteins. , 1988, Advances in protein chemistry.

[29]  M. James,et al.  Lowering the entropic barrier for binding conformationally flexible inhibitors to enzymes. , 1998, Biochemistry.

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

[31]  G. Klebe,et al.  DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. , 2002, Journal of medicinal chemistry.

[32]  Hans Matter,et al.  Probing the subpockets of factor Xa reveals two binding modes for inhibitors based on a 2-carboxyindole scaffold: a study combining structure-activity relationship and X-ray crystallography. , 2005, Journal of medicinal chemistry.

[33]  Simona Distinto,et al.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? , 2008, J. Comput. Aided Mol. Des..

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

[35]  Jeremy R. H. Tame,et al.  Scoring functions: A view from the bench , 1999, J. Comput. Aided Mol. Des..

[36]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[37]  Irene Luque,et al.  Structural parameterization of the binding enthalpy of small ligands , 2002, Proteins.

[38]  Markus H J Seifert,et al.  Targeted scoring functions for virtual screening. , 2009, Drug discovery today.

[39]  Arnold T. Hagler,et al.  Crystal packing, hydrogen bonding, and the effect of crystal forces on molecular conformation , 1980 .

[40]  Marcel L Verdonk,et al.  General and targeted statistical potentials for protein–ligand interactions , 2005, Proteins.

[41]  Steve Scheiner,et al.  Strength of the CαH··O Hydrogen Bond of Amino Acid Residues* , 2001, The Journal of Biological Chemistry.

[42]  Tudor I. Oprea,et al.  Integrating virtual screening in lead discovery. , 2004, Current opinion in chemical biology.

[43]  Jacob Kongsted,et al.  An improved method to predict the entropy term with the MM/PBSA approach , 2009, J. Comput. Aided Mol. Des..

[44]  Ajay N. Jain,et al.  Recommendations for evaluation of computational methods , 2008, J. Comput. Aided Mol. Des..

[45]  W. Hagmann,et al.  The many roles for fluorine in medicinal chemistry. , 2008, Journal of medicinal chemistry.

[46]  Natasja Brooijmans,et al.  Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.

[47]  Shaomeng Wang,et al.  An Extensive Test of 14 Scoring Functions Using the PDBbind Refined Set of 800 Protein-Ligand Complexes , 2004, J. Chem. Inf. Model..

[48]  Hans Matter,et al.  Structural requirements for factor Xa inhibition by 3-oxybenzamides with neutral P1 substituents: combining X-ray crystallography, 3D-QSAR, and tailored scoring functions. , 2005, Journal of medicinal chemistry.

[49]  Hans Matter,et al.  Recent advances in the design of matrix metalloprotease inhibitors. , 2004, Current opinion in drug discovery & development.

[50]  Ajay N. Jain,et al.  Parameter estimation for scoring protein-ligand interactions using negative training data. , 2006, Journal of medicinal chemistry.

[51]  T. Maren,et al.  A new class of carbonic anhydrase inhibitor. , 1993, The Journal of biological chemistry.

[52]  Pierangelo Metrangolo,et al.  Halogen bonding in supramolecular chemistry. , 2008, Angewandte Chemie.

[53]  Niu Huang,et al.  Physics-Based Scoring of Protein-Ligand Complexes: Enrichment of Known Inhibitors in Large-Scale Virtual Screening , 2006, J. Chem. Inf. Model..

[54]  Y. Cheng,et al.  Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. , 1973, Biochemical pharmacology.

[55]  Xun Li,et al.  Interpretation of the Binding Affinities of PTP1B Inhibitors with the MM-GB/SA Method and the X-Score Scoring Function , 2009, J. Chem. Inf. Model..

[56]  Kenny K H Ang,et al.  Divergent modes of enzyme inhibition in a homologous structure-activity series. , 2009, Journal of medicinal chemistry.

[57]  S. Homans,et al.  Water, water everywhere--except where it matters? , 2007, Drug discovery today.

[58]  Renxiao Wang,et al.  The PDBbind database: methodologies and updates. , 2005, Journal of medicinal chemistry.

[59]  G. McGaughey,et al.  pi-Stacking interactions. Alive and well in proteins. , 1998, The Journal of biological chemistry.

[60]  Keith T. Butler,et al.  Toward accurate relative energy predictions of the bioactive conformation of drugs , 2009, J. Comput. Chem..

[61]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[62]  Peter Politzer,et al.  An overview of halogen bonding , 2007, Journal of molecular modeling.

[63]  Julian Tirado-Rives,et al.  Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. , 2006, Journal of medicinal chemistry.

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

[65]  Nicolas Moitessier,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 5. Force-Field-Based Prediction of Binding Affinities of Ligands to Proteins , 2009, J. Chem. Inf. Model..

[66]  Gilles Marcou,et al.  Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints , 2007, J. Chem. Inf. Model..

[67]  E. Freire Do enthalpy and entropy distinguish first in class from best in class? , 2008, Drug discovery today.

[68]  Ricardo L. Mancera,et al.  Expanded Interaction Fingerprint Method for Analyzing Ligand Binding Modes in Docking and Structure-Based Drug Design , 2004, J. Chem. Inf. Model..

[69]  François Diederich,et al.  Orthogonal multipolar interactions in structural chemistry and biology. , 2005, Angewandte Chemie.

[70]  K. P. Murphy,et al.  Thermodynamics of structural stability and cooperative folding behavior in proteins. , 1992, Advances in protein chemistry.

[71]  J. Dunitz The entropic cost of bound water in crystals and biomolecules. , 1994, Science.

[72]  Paul A. Bartlett,et al.  Differential binding energy: a detailed evaluation of the influence of hydrogen-bonding and hydrophobic groups on the inhibition of thermolysin by phosphorus-containing inhibitors , 1991 .

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

[74]  Sangtae Kim,et al.  Position Specific Interaction Dependent Scoring Technique for Virtual Screening Based on Weighted Protein-Ligand Interaction Fingerprint Profiles , 2009, J. Chem. Inf. Model..

[75]  D A Dougherty,et al.  Acetylcholine binding by a synthetic receptor: implications for biological recognition , 1990, Science.

[76]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

[77]  K A Dill,et al.  Additivity Principles in Biochemistry* , 1997, The Journal of Biological Chemistry.

[78]  Tudor I. Oprea,et al.  Pursuing the leadlikeness concept in pharmaceutical research. , 2004, Current opinion in chemical biology.

[79]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

[80]  Alessandro Curioni,et al.  New Scoring Functions for Virtual Screening from Molecular Dynamics Simulations with a Quantum-Refined Force-Field (QRFF-MD). Application to Cyclin-Dependent Kinase 2 , 2006, J. Chem. Inf. Model..

[81]  Nathan A. Baker Biomolecular Applications of Poisson-Boltzmann Methods , 2005 .

[82]  Jeffrey Skolnick,et al.  Assessment of programs for ligand binding affinity prediction , 2008, J. Comput. Chem..

[83]  Zhihai Liu,et al.  Comparative Assessment of Scoring Functions on a Diverse Test Set , 2009, J. Chem. Inf. Model..

[84]  Reiji Teramoto,et al.  Supervised Scoring Models with Docked Ligand Conformations for Structure-Based Virtual Screening , 2007, J. Chem. Inf. Model..

[85]  Gerard J Kleywegt,et al.  Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. , 2003, Angewandte Chemie.

[86]  Andrew D Westwell,et al.  The role of fluorine in medicinal chemistry , 2007, Journal of enzyme inhibition and medicinal chemistry.

[87]  P. Kollman,et al.  An all atom force field for simulations of proteins and nucleic acids , 1986, Journal of computational chemistry.

[88]  C. Pace,et al.  Contribution of hydrogen bonding to the conformational stability of ribonuclease T1. , 1992, Biochemistry.

[89]  Manfred Kansy,et al.  Fluorine Interactions at the Thrombin Active Site: Protein Backbone Fragments HCαCO Comprise a Favorable CF Environment and Interactions of CF with Electrophiles , 2004, Chembiochem : a European journal of chemical biology.

[90]  G. Klebe,et al.  Statistical potentials and scoring functions applied to protein-ligand binding. , 2001, Current opinion in structural biology.

[91]  Paul D Lyne,et al.  Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. , 2006, Journal of medicinal chemistry.

[92]  J. Thornton,et al.  Satisfying hydrogen bonding potential in proteins. , 1994, Journal of molecular biology.

[93]  Matthias Rarey,et al.  Towards an Integrated Description of Hydrogen Bonding and Dehydration: Decreasing False Positives in Virtual Screening with the HYDE Scoring Function , 2008, ChemMedChem.

[94]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[95]  D. Case,et al.  Rescoring docking hit lists for model cavity sites: predictions and experimental testing. , 2008, Journal of molecular biology.

[96]  D. J. Price,et al.  Assessing scoring functions for protein-ligand interactions. , 2004, Journal of medicinal chemistry.

[97]  J. Warwicker,et al.  Calculation of the electric potential in the active site cleft due to alpha-helix dipoles. , 1982, Journal of molecular biology.

[98]  Anthony Nicholls,et al.  What do we know and when do we know it? , 2008, J. Comput. Aided Mol. Des..

[99]  Duan Yang,et al.  A Cα-H...O hydrogen bond in a membrane protein is not stabilizing , 2004 .

[100]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[101]  Bin Ye,et al.  Crystal structures of two potent nonamidine inhibitors bound to factor Xa. , 2002, Biochemistry.

[102]  Gennady M Verkhivker,et al.  Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. , 1995, Chemistry & biology.

[103]  Janet M. Thornton,et al.  BLEEP—potential of mean force describing protein–ligand interactions: I. Generating potential , 1999 .

[104]  J. Sühnel,et al.  C-h⋯π-interactions in proteins , 2001 .

[105]  Malcolm E. Davis,et al.  Electrostatics in biomolecular structure and dynamics , 1990 .

[106]  Martin Stahl,et al.  Fluorine in Medicinal Chemistry , 2004, Chembiochem : a European journal of chemical biology.

[107]  Markus H. J. Seifert,et al.  Robust optimization of scoring functions for a target class , 2009, J. Comput. Aided Mol. Des..

[108]  Loriano Storchi,et al.  Tautomer Enumeration and Stability Prediction for Virtual Screening on Large Chemical Databases , 2009, J. Chem. Inf. Model..

[109]  D. Rognan,et al.  Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. , 1999, Journal of medicinal chemistry.

[110]  Anjali Rohatgi,et al.  (www.interscience.wiley.com) DOI:10.1002/jmr.942 Scoring ligand similarity in structure-based virtual screening , 2022 .

[111]  Miklos Feher,et al.  The Use of Consensus Scoring in Ligand-Based Virtual Screening , 2006, J. Chem. Inf. Model..

[112]  Stephen R. Johnson,et al.  A Novel Method to Simulate the Hydrophobic Effect and Its Application to the Ranking of Host/Guest Complexes , 2006, J. Chem. Inf. Model..

[113]  Christian Griesinger,et al.  Dynamics in the p38alpha MAP kinase-SB203580 complex observed by liquid-state NMR spectroscopy. , 2008, Angewandte Chemie.

[114]  Istvan J. Enyedy,et al.  Can we use docking and scoring for hit-to-lead optimization? , 2008, J. Comput. Aided Mol. Des..

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

[116]  David G. Lloyd,et al.  Unbiasing Scoring Functions: A New Normalization and Rescoring Strategy , 2007, J. Chem. Inf. Model..

[117]  D W Banner,et al.  Molecular recognition at the thrombin active site: structure-based design and synthesis of potent and selective thrombin inhibitors and the X-ray crystal structures of two thrombin-inhibitor complexes. , 1997, Chemistry & biology.

[118]  Andreas Steffen,et al.  AIScore Chemically Diverse Empirical Scoring Function Employing Quantum Chemical Binding Energies of Hydrogen-Bonded Complexes , 2008, J. Chem. Inf. Model..

[119]  U. Ryde,et al.  Ligand affinities predicted with the MM/PBSA method: dependence on the simulation method and the force field. , 2006, Journal of Medicinal Chemistry.

[120]  Janet M. Thornton,et al.  BLEEP - potential of mean force describing protein-ligand interactions: I. Generating potential , 1999, J. Comput. Chem..

[121]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[122]  Jing Zhang,et al.  Design, structure-activity relationships, X-ray crystal structure, and energetic contributions of a critical P1 pharmacophore: 3-chloroindole-7-yl-based factor Xa inhibitors. , 2008, Journal of medicinal chemistry.

[123]  G. Klebe,et al.  Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. , 2002, Angewandte Chemie.

[124]  Sébastien Maignan,et al.  Molecular structures of human factor Xa complexed with ketopiperazine inhibitors: preference for a neutral group in the S1 pocket. , 2003, Journal of medicinal chemistry.

[125]  L. Kuhn,et al.  Virtual screening with solvation and ligand-induced complementarity , 2000 .

[126]  S. Brady,et al.  Design and synthesis of a series of potent and orally bioavailable noncovalent thrombin inhibitors that utilize nonbasic groups in the P1 position. , 1998, Journal of medicinal chemistry.

[127]  Zhan Deng,et al.  Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening. , 2005, Journal of medicinal chemistry.

[128]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[129]  Hanoch Senderowitz,et al.  SeleX-CS: A New Consensus Scoring Algorithm for Hit Discovery and Lead Optimization , 2009, J. Chem. Inf. Model..

[130]  Ajay N. Jain Bias, reporting, and sharing: computational evaluations of docking methods , 2008, J. Comput. Aided Mol. Des..

[131]  Z. Deng,et al.  Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. , 2004, Journal of medicinal chemistry.

[132]  Gerhard Klebe,et al.  Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. , 2003, Journal of molecular biology.

[133]  Steven W. Muchmore,et al.  Rapid Estimation of Relative Protein-Ligand Binding Affinities Using a High-Throughput Version of MM-PBSA , 2007, J. Chem. Inf. Model..

[134]  Robin Taylor,et al.  Using Buriedness To Improve Discrimination between Actives and Inactives in Docking , 2008, J. Chem. Inf. Model..

[135]  Anthony E. Klon,et al.  Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results. , 2004, Journal of medicinal chemistry.

[136]  Stefano Moro,et al.  In Silico Binding Free Energy Predictability by Using the Linear Interaction Energy (LIE) Method: Bromobenzimidazole CK2 Inhibitors as a Case Study , 2007, J. Chem. Inf. Model..

[137]  E. Jaeger,et al.  Comparison of automated docking programs as virtual screening tools. , 2005, Journal of Medicinal Chemistry.

[138]  Thomas E. Exner,et al.  Influence of Protonation, Tautomeric, and Stereoisomeric States on Protein-Ligand Docking Results , 2009, J. Chem. Inf. Model..

[139]  W. L. Jorgensen,et al.  General model for estimation of the inhibition of protein kinases using Monte Carlo simulations. , 2004, Journal of medicinal chemistry.

[140]  Gerhard Klebe,et al.  SFCscore: Scoring functions for affinity prediction of protein–ligand complexes , 2008, Proteins.

[141]  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.

[142]  K. Kitaura,et al.  Cl–π interactions in protein–ligand complexes , 2008, Protein science : a publication of the Protein Society.

[143]  Gianluca Pollastri,et al.  Structural artifacts in protein-ligand X-ray structures: implications for the development of docking scoring functions. , 2009, Journal of medicinal chemistry.

[144]  Suzanne C Brewerton,et al.  The use of protein-ligand interaction fingerprints in docking. , 2008, Current opinion in drug discovery & development.

[145]  J. H. Zhang,et al.  Simulation of NMR data reveals that proteins' local structures are stabilized by electronic polarization. , 2009, Journal of the American Chemical Society.

[146]  G. Vigers,et al.  Multiple active site corrections for docking and virtual screening. , 2004, Journal of medicinal chemistry.

[147]  John J. Irwin,et al.  Community benchmarks for virtual screening , 2008, J. Comput. Aided Mol. Des..

[148]  Martin Stahl,et al.  Modifications of the scoring function in FlexX for virtual screening applications , 2000 .

[149]  Nicolas Moitessier,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 4. Are Popular Scoring Functions Accurate for this Class of Proteins? , 2009, J. Chem. Inf. Model..

[150]  Markus H. J. Seifert Assessing the Discriminatory Power of Scoring Functions for Virtual Screening , 2006, J. Chem. Inf. Model..

[151]  I. Muegge PMF scoring revisited. , 2006, Journal of medicinal chemistry.

[152]  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.

[153]  G A Petsko,et al.  Aromatic-aromatic interaction: a mechanism of protein structure stabilization. , 1985, Science.

[154]  Agnieszka Bronowska,et al.  Residual ligand entropy in the binding of p-substituted benzenesulfonamide ligands to bovine carbonic anhydrase II. , 2008, Journal of the American Chemical Society.

[155]  H. Matter,et al.  Structural classification of protein kinases using 3D molecular interaction field analysis of their ligand binding sites: target family landscapes. , 2002, Journal of medicinal chemistry.

[156]  Z. Derewenda,et al.  The occurrence of C-H...O hydrogen bonds in proteins. , 1995, Journal of molecular biology.

[157]  D. Frank Hsu,et al.  Consensus Scoring Criteria for Improving Enrichment in Virtual Screening , 2005, J. Chem. Inf. Model..

[158]  B. Kuhn,et al.  Validation and use of the MM-PBSA approach for drug discovery. , 2005, Journal of medicinal chemistry.

[159]  R. Wade,et al.  Prediction of drug binding affinities by comparative binding energy analysis , 1995 .

[160]  J Tirado-Rives,et al.  Estimation of binding affinities for HEPT and nevirapine analogues with HIV-1 reverse transcriptase via Monte Carlo simulations. , 2001, Journal of medicinal chemistry.

[161]  M Pastor,et al.  Reliability of comparative molecular field analysis models: effects of data scaling and variable selection using a set of human synovial fluid phospholipase A2 inhibitors. , 1997, Journal of medicinal chemistry.

[162]  Shuichi Hirono,et al.  Comparison of Consensus Scoring Strategies for Evaluating Computational Models of Protein-Ligand Complexes , 2006, J. Chem. Inf. Model..

[163]  Loriano Storchi,et al.  Predicting protein pKa by environment similarity , 2009, Proteins.

[164]  Kazumasa Honda,et al.  Origin of the Attraction and Directionality of the NH/π Interaction: Comparison with OH/π and CH/π Interactions , 2000 .

[165]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[166]  D. A. Dougherty,et al.  Cation-π interactions in structural biology , 1999 .

[167]  Amedeo Caflisch,et al.  Efficient evaluation of binding free energy using continuum electrostatics solvation. , 2004, Journal of medicinal chemistry.

[168]  Anna Vulpetti,et al.  Novel Scoring Functions Comprising QXP, SASA, and Protein Side-Chain Entropy Terms , 2004, J. Chem. Inf. Model..

[169]  Manfred Kansy,et al.  A Fluorine Scan at the Catalytic Center of Thrombin: CF, COH, and COMe Bioisosterism and Fluorine Effects on pKa and log D Values , 2006, ChemMedChem.

[170]  Rubicelia Vargas,et al.  How Strong Is the Cα−H···OC Hydrogen Bond? , 2000 .

[171]  Min Zhou,et al.  Understanding noncovalent interactions: ligand binding energy and catalytic efficiency from ligand-induced reductions in motion within receptors and enzymes. , 2004, Angewandte Chemie.

[172]  John G Cumming,et al.  Novel, potent and selective anilinoquinazoline and anilinopyrimidine inhibitors of p38 MAP kinase. , 2004, Bioorganic & medicinal chemistry letters.

[173]  F. Diederich,et al.  Cation-pi interactions at the active site of factor Xa: dramatic enhancement upon stepwise N-alkylation of ammonium ions. , 2009, Angewandte Chemie.

[174]  Eric J. Martin,et al.  Exploiting Structure-Activity Relationships in Docking , 2008, J. Chem. Inf. Model..

[175]  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..

[176]  Anders Karlén,et al.  Ligand Bias of Scoring Functions in Structure-Based Virtual Screening , 2006, J. Chem. Inf. Model..

[177]  David A Pearlman,et al.  Evaluating the molecular mechanics poisson-boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase. , 2005, Journal of medicinal chemistry.

[178]  Eric J. Martin,et al.  AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data , 2008, J. Chem. Inf. Model..

[179]  J D Dunitz,et al.  Win some, lose some: enthalpy-entropy compensation in weak intermolecular interactions. , 1995, Chemistry & biology.

[180]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[181]  Scott P. Brown,et al.  Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes. , 2009, Journal of medicinal chemistry.

[182]  David Vidal,et al.  A Novel Search Engine for Virtual Screening of Very Large Databases , 2006, J. Chem. Inf. Model..

[183]  Xavier Morelli,et al.  GFscore: A General Nonlinear Consensus Scoring Function for High-Throughput Docking , 2006, J. Chem. Inf. Model..

[184]  Robin Taylor,et al.  Comparing protein–ligand docking programs is difficult , 2005, Proteins.

[185]  Peter Willett,et al.  Assessment of additive/nonadditive effects in structure-activity relationships: implications for iterative drug design. , 2008, Journal of medicinal chemistry.

[186]  Shaomeng Wang,et al.  M-score: a knowledge-based potential scoring function accounting for protein atom mobility. , 2006, Journal of medicinal chemistry.

[187]  Weiliang Zhu,et al.  Halogen bonding--a novel interaction for rational drug design? , 2009, Journal of medicinal chemistry.

[188]  Eric Westhof,et al.  Halogen bonds in biological molecules. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[190]  R. Wade,et al.  Comparative binding energy (COMBINE) analysis of influenza neuraminidase-inhibitor complexes. , 2001, Journal of medicinal chemistry.

[191]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[192]  Andrew C Good,et al.  Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. , 2007, Current opinion in drug discovery & development.

[193]  M Pastor,et al.  Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design. , 1998, Journal of medicinal chemistry.

[194]  Robin Taylor,et al.  A new test set for validating predictions of protein–ligand interaction , 2002, Proteins.

[195]  G. Bemis,et al.  Kinase inhibitors and the case for CH…O hydrogen bonds in protein–ligand binding , 2002, Proteins.

[196]  Chris Oostenbrink,et al.  Are Automated Molecular Dynamics Simulations and Binding Free Energy Calculations Realistic Tools in Lead Optimization? An Evaluation of the Linear Interaction Energy (LIE) Method , 2006, J. Chem. Inf. Model..

[197]  Thomas Lampe,et al.  Discovery of the novel antithrombotic agent 5-chloro-N-({(5S)-2-oxo-3- [4-(3-oxomorpholin-4-yl)phenyl]-1,3-oxazolidin-5-yl}methyl)thiophene- 2-carboxamide (BAY 59-7939): an oral, direct factor Xa inhibitor. , 2005, Journal of medicinal chemistry.