Integrating Ligand-Based and Protein-Centric Virtual Screening of Kinase Inhibitors Using Ensembles of Multiple Protein Kinase Genes and Conformations
暂无分享,去创建一个
[1] M Rarey,et al. Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.
[2] Shuichi Hirono,et al. Comparison of Consensus Scoring Strategies for Evaluating Computational Models of Protein-Ligand Complexes , 2006, J. Chem. Inf. Model..
[3] David G. Lloyd,et al. Considerations in Compound Database Preparation-"Hidden" Impact on Virtual Screening Results , 2005, J. Chem. Inf. Model..
[4] R. Abagyan,et al. Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening , 2011, PloS one.
[5] Gennady Verkhivker,et al. Deciphering common failures in molecular docking of ligand-protein complexes , 2000, J. Comput. Aided Mol. Des..
[6] Christopher R. Corbeil,et al. Docking Ligands into Flexible and Solvated Macromolecules, 1. Development and Validation of FITTED 1.0 , 2007, J. Chem. Inf. Model..
[7] Orr Ravitz,et al. Improving molecular docking through eHiTS’ tunable scoring function , 2011, J. Comput. Aided Mol. Des..
[8] Maria Kontoyianni,et al. Evaluation of docking performance: comparative data on docking algorithms. , 2004, Journal of medicinal chemistry.
[9] James L. Melville,et al. Better than Random? The Chemotype Enrichment Problem , 2009, J. Chem. Inf. Model..
[10] P. Hawkins,et al. Comparison of shape-matching and docking as virtual screening tools. , 2007, Journal of medicinal chemistry.
[11] Michael Nilges,et al. Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment , 2010, J. Chem. Inf. Model..
[12] Benjamin A. Ellingson,et al. Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database , 2010, J. Chem. Inf. Model..
[13] Gennady M Verkhivker. In silico profiling of tyrosine kinases binding specificity and drug resistance using Monte Carlo simulations with the ensembles of protein kinase crystal structures. , 2007, Biopolymers.
[14] Violeta I. Pérez-Nueno,et al. Improving VEGFR-2 Docking-Based Screening by Pharmacophore Postfiltering and Similarity Search Postprocessing , 2011, J. Chem. Inf. Model..
[15] Thomas Sander,et al. Comparison of Ligand- and Structure-Based Virtual Screening on the DUD Data Set , 2009, J. Chem. Inf. Model..
[16] G. Klebe. Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.
[17] Oliver Korb,et al. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test , 2012, Journal of Computer-Aided Molecular Design.
[18] Todd J. A. Ewing,et al. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..
[19] Nikolay V. Dokholyan,et al. Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets , 2011, J. Chem. Inf. Model..
[20] Didier Rognan,et al. Comparative evaluation of eight docking tools for docking and virtual screening accuracy , 2004, Proteins.
[21] D. Rognan,et al. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.
[22] Xiaoqin Zou,et al. Scoring and Lessons Learned with the CSAR Benchmark Using an Improved Iterative Knowledge-Based Scoring Function , 2011, J. Chem. Inf. Model..
[23] Gennady M Verkhivker. Computational proteomics of biomolecular interactions in the sequence and structure space of the tyrosine kinome: Deciphering the molecular basis of the kinase inhibitors selectivity , 2006, Proteins.
[24] 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..
[25] Ajay N. Jain. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.
[26] Fredrik Svensson,et al. Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods , 2012, J. Chem. Inf. Model..
[27] Qi Liu,et al. Virtual Drug Screen Schema Based on Multiview Similarity Integration and Ranking Aggregation , 2012, J. Chem. Inf. Model..
[28] Andreas Bender,et al. Recognizing Pitfalls in Virtual Screening: A Critical Review , 2012, J. Chem. Inf. Model..
[29] Robert P. Sheridan,et al. Comparison of Topological, Shape, and Docking Methods in Virtual Screening , 2007, J. Chem. Inf. Model..
[30] Jing Li,et al. Knowledge-Based Scoring Functions in Drug Design: 3. A Two-Dimensional Knowledge-Based Hydrogen-Bonding Potential for the Prediction of Protein-Ligand Interactions , 2011, J. Chem. Inf. Model..
[31] 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.
[32] Luhua Lai,et al. Binding Energy Landscape Analysis Helps to Discriminate True Hits from High-Scoring Decoys in Virtual Screening , 2010, J. Chem. Inf. Model..
[33] N. Foloppe,et al. Towards predictive ligand design with free-energy based computational methods? , 2006, Current medicinal chemistry.
[34] Hualiang Jiang,et al. Knowledge-Based Scoring Functions in Drug Design. 1. Developing a Target-Specific Method for Kinase-Ligand Interactions , 2010, J. Chem. Inf. Model..
[35] Dennis M. Krüger,et al. Comparison of Structure‐ and Ligand‐Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors , 2010, ChemMedChem.
[36] 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..
[37] J. Andrew Grant,et al. Molecular shape and electrostatics in the encoding of relevant chemical information , 2005, J. Comput. Aided Mol. Des..
[38] Jonas Boström,et al. Assessing the performance of OMEGA with respect to retrieving bioactive conformations. , 2003, Journal of molecular graphics & modelling.
[39] 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.
[40] Gennady M Verkhivker,et al. Energy landscape theory, funnels, specificity, and optimal criterion of biomolecular binding. , 2003, Physical review letters.
[41] J Andrew McCammon,et al. On the use of molecular dynamics receptor conformations for virtual screening. , 2012, Methods in molecular biology.
[42] Richard A. Friesner,et al. Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets , 2007, J. Chem. Inf. Model..
[43] David W. Ritchie,et al. Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening , 2011, J. Chem. Inf. Model..
[44] James Andrew McCammon,et al. Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening , 2011, J. Chem. Inf. Model..
[45] Ruben Abagyan,et al. Recipes for the Selection of Experimental Protein Conformations for Virtual Screening , 2010, J. Chem. Inf. Model..
[46] Ajay N. Jain,et al. Surflex-Dock: Docking benchmarks and real-world application , 2012, Journal of Computer-Aided Molecular Design.
[47] Miklos Feher,et al. The Use of Consensus Scoring in Ligand-Based Virtual Screening , 2006, J. Chem. Inf. Model..
[48] 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.
[49] Yvonne C. Martin,et al. Application of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping , 2008, J. Chem. Inf. Model..
[50] D. E. Clark,et al. Flexible docking using tabu search and an empirical estimate of binding affinity , 1998, Proteins.
[51] Zhihai Liu,et al. Comparative Assessment of Scoring Functions on a Diverse Test Set , 2009, J. Chem. Inf. Model..
[52] Dariusz Plewczynski,et al. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database , 2011, J. Comput. Chem..
[53] Yanli Wang,et al. Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review , 2012, The AAPS Journal.
[54] Wei Deng,et al. Evaluation of Different Virtual Screening Programs for Docking in a Charged Binding Pocket , 2008, J. Chem. Inf. Model..
[55] Bohdan Waszkowycz,et al. Towards improving compound selection in structure-based virtual screening. , 2008, Drug discovery today.
[56] I. Muegge. PMF scoring revisited. , 2006, Journal of medicinal chemistry.
[57] 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..
[58] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[59] Naomie Salim,et al. Combination of Fingerprint-Based Similarity Coefficients Using Data Fusion , 2003, J. Chem. Inf. Comput. Sci..
[60] 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.
[61] Ruben Abagyan,et al. Docking and scoring with ICM: the benchmarking results and strategies for improvement , 2012, Journal of Computer-Aided Molecular Design.
[62] Andrej Sali,et al. Virtual ligand screening against comparative protein structure models. , 2012, Methods in molecular biology.
[63] R. Abagyan,et al. Flexible ligand docking to multiple receptor conformations: a practical alternative. , 2008, Current opinion in structural biology.
[64] Claudio N. Cavasotto,et al. Ligand docking and structure-based virtual screening in drug discovery. , 2007, Current topics in medicinal chemistry.
[65] J. Pin,et al. Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. , 2005, Journal of medicinal chemistry.
[66] Guixia Liu,et al. Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening , 2012, J. Chem. Inf. Model..
[67] Y. Martin,et al. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.
[68] R. Kroemer. Structure-based drug design: docking and scoring. , 2007, Current protein & peptide science.
[69] J. A. Grant,et al. Gaussian docking functions. , 2003, Biopolymers.
[70] Ruben Abagyan,et al. ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation , 1994, J. Comput. Chem..
[71] Gennady M Verkhivker,et al. Towards understanding the mechanisms of molecular recognition by computer simulations of ligand–protein interactions , 1999, Journal of molecular recognition : JMR.
[72] Yongbo Hu,et al. Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..
[73] Jing Li,et al. Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched? , 2011, J. Chem. Inf. Model..
[74] J. A. Grant,et al. A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape , 1996, J. Comput. Chem..
[75] Thomas Lengauer,et al. A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.
[76] Richard A. Friesner,et al. Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide , 2012, Journal of Computer-Aided Molecular Design.
[77] P. Charifson,et al. Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. , 2004, Journal of medicinal chemistry.
[78] Ruben Abagyan,et al. Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles , 2010, J. Comput. Aided Mol. Des..
[79] Simona Distinto,et al. How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information , 2009, J. Chem. Inf. Model..
[80] P Willett,et al. Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.
[81] Reiji Teramoto,et al. Supervised Consensus Scoring for Docking and Virtual Screening , 2007, J. Chem. Inf. Model..
[82] W. L. Jorgensen. The Many Roles of Computation in Drug Discovery , 2004, Science.
[83] Brian K. Shoichet,et al. Virtual screening of chemical libraries , 2004, Nature.
[84] C. Venkatachalam,et al. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. , 2003, Journal of molecular graphics & modelling.
[85] W Patrick Walters,et al. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance , 2004, Proteins.
[86] Chang-Guo Zhan,et al. Ligand-Based Virtual Screening Approach Using a New Scoring Function , 2012, J. Chem. Inf. Model..
[87] Shaomeng Wang,et al. MCDOCK: A Monte Carlo simulation approach to the molecular docking problem , 1999, J. Comput. Aided Mol. Des..
[88] Xiliang Zheng,et al. Quantifying intrinsic specificity: a potential complement to affinity in drug screening. , 2007, Physical review letters.
[89] 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.
[90] Hanna Geppert,et al. Integrating Structure‐ and Ligand‐Based Virtual Screening: Comparison of Individual, Parallel, and Fused Molecular Docking and Similarity Search Calculations on Multiple Targets , 2008, ChemMedChem.
[91] J. Irwin,et al. Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.
[92] Sudipto Mukherjee,et al. Evaluation of DOCK 6 as a pose generation and database enrichment tool , 2012, Journal of Computer-Aided Molecular Design.
[93] David S. Goodsell,et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998 .
[94] Richard D. Smith,et al. CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions , 2011, J. Chem. Inf. Model..
[95] Jin Li,et al. On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors , 2006, J. Chem. Inf. Model..
[96] Christopher I. Bayly,et al. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..
[97] Thierry Langer,et al. Fast and Efficient in Silico 3D Screening: Toward Maximum Computational Efficiency of Pharmacophore-Based and Shape-Based Approaches , 2007, J. Chem. Inf. Model..
[98] 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..
[99] Gregory L. Wilson,et al. Integrating structure-based and ligand-based approaches for computational drug design. , 2011, Future medicinal chemistry.
[100] Hui Sun Lee,et al. Improving Virtual Screening Performance against Conformational Variations of Receptors by Shape Matching with Ligand Binding Pocket , 2009, J. Chem. Inf. Model..
[101] Charles L. Brooks,et al. Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm , 2003, J. Comput. Chem..
[102] Shaomeng Wang,et al. How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment , 2001, J. Chem. Inf. Comput. Sci..
[103] Jiwon Choi,et al. Optimization of High Throughput Virtual Screening by Combining Shape-Matching and Docking Methods , 2008, J. Chem. Inf. Model..
[104] Luhua Lai,et al. Further development and validation of empirical scoring functions for structure-based binding affinity prediction , 2002, J. Comput. Aided Mol. Des..
[105] B. Kuhn,et al. Validation and use of the MM-PBSA approach for drug discovery. , 2005, Journal of medicinal chemistry.
[106] Ruben Abagyan,et al. Comparative study of several algorithms for flexible ligand docking , 2003, J. Comput. Aided Mol. Des..
[107] C. E. Peishoff,et al. A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.
[108] 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.
[109] Christine Humblet,et al. Computation of 3D queries for ROCS based virtual screens , 2009, J. Comput. Aided Mol. Des..
[110] Richard D. Smith,et al. CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes , 2011, J. Chem. Inf. Model..
[111] Natasja Brooijmans,et al. Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.
[112] Mark McGann,et al. FRED and HYBRID docking performance on standardized datasets , 2012, Journal of Computer-Aided Molecular Design.
[113] Reiji Teramoto,et al. Consensus Scoring with Feature Selection for Structure-Based Virtual Screening , 2008, J. Chem. Inf. Model..
[114] Kenji Onodera,et al. Evaluations of Molecular Docking Programs for Virtual Screening , 2007, J. Chem. Inf. Model..
[115] John W. Raymond,et al. Conditional Probability: A New Fusion Method for Merging Disparate Virtual Screening Results , 2004, J. Chem. Inf. Model..
[116] Renxiao Wang,et al. Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.
[117] Nikolay V. Dokholyan,et al. Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge-Based Pose Scoring and Physical Force Field-Based Hit Scoring Functions Improves the Accuracy of Structure-Based Virtual Screening , 2012, J. Chem. Inf. Model..
[118] Nagamani Sukumar,et al. Current trends in virtual high throughput screening using ligand-based and structure-based methods. , 2011, Combinatorial chemistry & high throughput screening.
[119] Lazaros Mavridis,et al. Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods , 2010, J. Chem. Inf. Model..
[120] Jung-Hsin Lin. Accommodating protein flexibility for structure-based drug design. , 2011, Current topics in medicinal chemistry.
[121] J. Bajorath,et al. Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.
[122] 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..
[123] E. Jaeger,et al. Comparison of automated docking programs as virtual screening tools. , 2005, Journal of Medicinal Chemistry.