Virtual screening in drug design.

Virtual screening has become a standard tool in drug discovery to identify novel lead compounds that target a biomolecule of interest. I present several concepts in ligand-based and structure-based virtual screening and discuss some of the current shortcomings and new developments. I also highlight approaches that combine concepts from structure- and ligand-based design.

[1]  A. N. Jain,et al.  Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. , 1996, Chemistry & biology.

[2]  Ajay N. Jain,et al.  Molecular Shape and Medicinal Chemistry: A Perspective , 2010, Journal of medicinal chemistry.

[3]  James G. Nourse,et al.  Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..

[4]  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..

[5]  X. Barril,et al.  Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. , 2005, Journal of medicinal chemistry.

[6]  Erik M. Bollt,et al.  What is Special about Diffusion on Scale-Free Nets? , 2004 .

[7]  Hanna Geppert,et al.  Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation , 2010, J. Chem. Inf. Model..

[8]  Marvin Waldman,et al.  Application of structure‐based focusing to the estrogen receptor , 2001, J. Comput. Chem..

[9]  Erin S. Bolstad,et al.  In pursuit of virtual lead optimization: Pruning ensembles of receptor structures for increased efficiency and accuracy during docking , 2009, Proteins.

[10]  Christopher W. Murray,et al.  The sensitivity of the results of molecular docking to induced fit effects: Application to thrombin, thermolysin and neuraminidase , 1999, J. Comput. Aided Mol. Des..

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

[12]  S. Stanley Young,et al.  Automated Pharmacophore Identification for Large Chemical Data Sets. , 1999 .

[13]  Jürgen Bajorath,et al.  Computational Methodologies for Compound Database Searching that Utilize Experimental Protein–Ligand Interaction Information , 2010, Chemical biology & drug design.

[14]  Yvonne C. Martin,et al.  Use of Structure-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection , 1996, J. Chem. Inf. Comput. Sci..

[15]  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..

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

[17]  Robert P. Sheridan,et al.  Comparison of Topological, Shape, and Docking Methods in Virtual Screening. , 2007 .

[18]  Liwei Li,et al.  Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation , 2011, J. Chem. Inf. Model..

[19]  Gregory L. Wilson,et al.  Integrating structure-based and ligand-based approaches for computational drug design. , 2011, Future medicinal chemistry.

[20]  Maria A Miteva,et al.  Structure-based virtual ligand screening: recent success stories. , 2009, Combinatorial chemistry & high throughput screening.

[21]  M. Lill.  Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. , 2011, Biochemistry.

[22]  M. Parrinello,et al.  Computing Free Energies and Accelerating Rare Events with Metadynamics , 2006 .

[23]  Peter Willett,et al.  GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D , 2006, J. Comput. Aided Mol. Des..

[24]  R Abagyan,et al.  Flexible protein–ligand docking by global energy optimization in internal coordinates , 1997, Proteins.

[25]  Yvonne C. Martin,et al.  The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding , 1997, J. Chem. Inf. Comput. Sci..

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

[27]  Xin Chen,et al.  Automated Pharmacophore Identification for Large Chemical Data Sets1 , 1999, J. Chem. Inf. Comput. Sci..

[28]  Jonathan D Hirst,et al.  Machine learning in virtual screening. , 2009, Combinatorial chemistry & high throughput screening.

[29]  B. Shoichet,et al.  Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. , 2003, Journal of medicinal chemistry.

[30]  J. Bajorath,et al.  State-of-the-art in ligand-based virtual screening. , 2011, Drug discovery today.

[31]  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.

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

[33]  J Andrew McCammon,et al.  Studying enzyme binding specificity in acetylcholinesterase using a combined molecular dynamics and multiple docking approach. , 2002, Journal of the American Chemical Society.

[34]  Hans-Joachim Böhm,et al.  LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads , 1992, J. Comput. Aided Mol. Des..

[35]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[36]  R. Jernigan,et al.  Anisotropy of fluctuation dynamics of proteins with an elastic network model. , 2001, Biophysical journal.

[37]  J. A. Grant,et al.  A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. , 2005, Journal of medicinal chemistry.

[38]  A. Atilgan,et al.  Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. , 1997, Folding & design.

[39]  Steven W. Muchmore,et al.  High-Throughput Calculation of Protein-Ligand Binding Affinities: Modification and Adaptation of the MM-PBSA Protocol to Enterprise Grid Computing , 2006, J. Chem. Inf. Model..

[40]  T Lengauer,et al.  Two-stage method for protein-ligand docking. , 1999, Journal of medicinal chemistry.

[41]  Campbell McInnes,et al.  Virtual screening strategies in drug discovery. , 2007, Current opinion in chemical biology.

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

[44]  Mengang Xu,et al.  Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking , 2012, J. Chem. Inf. Model..

[45]  P. Hawkins,et al.  Comparison of shape-matching and docking as virtual screening tools. , 2007, Journal of medicinal chemistry.

[46]  Roger S Armen,et al.  An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics. , 2009, Journal of chemical theory and computation.

[47]  D. E. Clark,et al.  Outstanding challenges in protein–ligand docking and structure‐based virtual screening , 2011 .

[48]  Xiaoqin Zou,et al.  Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. , 2010, Physical chemistry chemical physics : PCCP.

[49]  A. Laio,et al.  Flexible docking in solution using metadynamics. , 2005, Journal of the American Chemical Society.

[50]  Mengang Xu,et al.  Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling , 2011, J. Chem. Inf. Model..

[51]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[52]  H. Carlson.  Protein flexibility and drug design: how to hit a moving target. , 2002, Current opinion in chemical biology.

[53]  M L Teodoro,et al.  Conformational flexibility models for the receptor in structure based drug design. , 2003, Current pharmaceutical design.

[54]  Bo Wang,et al.  Support Vector Regression Scoring of Receptor-Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries , 2011, J. Chem. Inf. Model..

[55]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

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

[57]  D. Bojanic,et al.  Impact of high-throughput screening in biomedical research , 2011, Nature Reviews Drug Discovery.

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

[59]  Andrew Smellie,et al.  Identification of Common Functional Configurations Among Molecules , 1996, J. Chem. Inf. Comput. Sci..

[60]  J. Mongan,et al.  Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. , 2004, The Journal of chemical physics.

[61]  Markus A. Lill,et al.  Protein Pharmacophore Selection Using Hydration-Site Analysis , 2012, J. Chem. Inf. Model..

[62]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[63]  David E. Shaw,et al.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results , 2006, J. Comput. Aided Mol. Des..

[64]  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.

[65]  R. Abagyan,et al.  Flexible ligand docking to multiple receptor conformations: a practical alternative. , 2008, Current opinion in structural biology.

[66]  Christoph A Sotriffer,et al.  Accounting for induced-fit effects in docking: what is possible and what is not? , 2011, Current topics in medicinal chemistry.

[67]  Somesh D. Sharma,et al.  Managing protein flexibility in docking and its applications. , 2009, Drug discovery today.

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

[69]  Rommie E. Amaro,et al.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design , 2008, J. Comput. Aided Mol. Des..

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

[71]  Jung-Hsin Lin.  Accommodating protein flexibility for structure-based drug design. , 2011, Current topics in medicinal chemistry.

[72]  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.

[73]  G. Klebe.  Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.

[74]  Martin Zacharias,et al.  Tackling the challenges posed by target flexibility in drug design , 2010, Expert opinion on drug discovery.