Molecular Docking: Shifting Paradigms in Drug Discovery
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[1] Giulio Rastelli,et al. Advances and applications of binding affinity prediction methods in drug discovery. , 2012, Biotechnology advances.
[2] Aurélien Lesnard,et al. Dual Histamine H3R/Serotonin 5-HT4R Ligands with Antiamnesic Properties: Pharmacophore-Based Virtual Screening and Polypharmacology , 2014, J. Chem. Inf. Model..
[3] Gregory L. Wilson,et al. Integrating structure-based and ligand-based approaches for computational drug design. , 2011, Future medicinal chemistry.
[4] Luhua Lai,et al. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. , 2008, Journal of medicinal chemistry.
[5] Woody Sherman,et al. Use of an Induced Fit Receptor Structure in Virtual Screening , 2006, Chemical biology & drug design.
[6] L. Dardenne,et al. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges , 2018, Front. Pharmacol..
[7] Mire Zloh,et al. Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity. , 2012, Bioorganic & medicinal chemistry.
[8] J. Janin,et al. Computer analysis of protein-protein interaction. , 1978, Journal of molecular biology.
[9] Sona Warrier,et al. Reverse docking: a powerful tool for drug repositioning and drug rescue. , 2014, Future medicinal chemistry.
[10] Lin He,et al. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome , 2011, Nucleic Acids Res..
[11] Ying Sun,et al. A nonsynonymous SNP in human cytosolic sialidase in a small Asian population results in reduced enzyme activity: potential link with severe adverse reactions to oseltamivir , 2007, Cell Research.
[12] Oliver Koch,et al. A novel interaction fingerprint derived from per atom score contributions: exhaustive evaluation of interaction fingerprint performance in docking based virtual screening , 2018, Journal of Cheminformatics.
[13] Michael M. Mysinger,et al. Automated Docking Screens: A Feasibility Study , 2009, Journal of medicinal chemistry.
[14] I. Kola,et al. Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.
[15] Ram Samudrala,et al. CANDO and the infinite drug discovery frontier. , 2014, Drug discovery today.
[16] A. Cavalli,et al. Dynamic Docking: A Paradigm Shift in Computational Drug Discovery , 2017, Molecules.
[17] Pedro J Ballester,et al. Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening , 2015, Wiley interdisciplinary reviews. Computational molecular science.
[18] I. Kuntz,et al. DOCK 6: combining techniques to model RNA-small molecule complexes. , 2009, RNA.
[19] Daniel A. Gschwend,et al. Molecular docking towards drug discovery , 1996, Journal of molecular recognition : JMR.
[20] Xue-wen Chen,et al. Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.
[21] Sunyong Yoo,et al. In silico profiling of systemic effects of drugs to predict unexpected interactions , 2018, Scientific Reports.
[22] Giovanni Chillemi,et al. Modeling conformational transitions in kinases by molecular dynamics simulations: achievements, difficulties, and open challenges , 2014, Front. Genet..
[23] G. Maggiora,et al. Molecular similarity in medicinal chemistry. , 2014, Journal of medicinal chemistry.
[24] S. Kim,et al. "Soft docking": matching of molecular surface cubes. , 1991, Journal of molecular biology.
[25] Sunhwan Jo,et al. Application of Binding Free Energy Calculations to Prediction of Binding Modes and Affinities of MDM2 and MDMX Inhibitors , 2012, J. Chem. Inf. Model..
[26] Luca Pinzi,et al. Refinement and Rescoring of Virtual Screening Results , 2019, Front. Chem..
[27] Andrea Clematis,et al. Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects , 2013, BioMed research international.
[28] R Abagyan,et al. High-throughput docking for lead generation. , 2001, Current opinion in chemical biology.
[29] J. Bajorath,et al. Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.
[30] S Vajda,et al. Flexible docking and design. , 1995, Annual review of biophysics and biomolecular structure.
[31] Jian-Ping Zhou,et al. Combined SVM-based and docking-based virtual screening for retrieving novel inhibitors of c-Met. , 2011, European journal of medicinal chemistry.
[32] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[33] Ram Samudrala,et al. Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform. , 2016, Current pharmaceutical design.
[34] A. Cavalli,et al. Protein conformational transitions: the closure mechanism of a kinase explored by atomistic simulations. , 2009, Journal of the American Chemical Society.
[35] Peter V Coveney,et al. Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration. , 2017, Journal of chemical theory and computation.
[36] J. Gready,et al. Combining docking and molecular dynamic simulations in drug design , 2006, Medicinal research reviews.
[37] M. Gilson,et al. Calculation of protein-ligand binding affinities. , 2007, Annual review of biophysics and biomolecular structure.
[38] Cheng Wang,et al. Improving scoring‐docking‐screening powers of protein–ligand scoring functions using random forest , 2017, J. Comput. Chem..
[39] Pedro J. Ballester,et al. Performance of machine-learning scoring functions in structure-based virtual screening , 2017, Scientific Reports.
[40] Andrea Cavalli,et al. Recent advances in dynamic docking for drug discovery , 2017 .
[41] David E. Gloriam. Bigger is better in virtual drug screens , 2019, Nature.
[42] 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.
[43] Dongsup Kim,et al. Using reverse docking for target identification and its applications for drug discovery , 2016, Expert opinion on drug discovery.
[44] Richard J. Hall,et al. Protein-Ligand Docking against Non-Native Protein Conformers , 2008, J. Chem. Inf. Model..
[45] Z. Cournia,et al. Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations , 2018, Scientific Reports.
[46] Michael J. Keiser,et al. Predicting new molecular targets for known drugs , 2009, Nature.
[47] J. Bajorath,et al. Polypharmacology: challenges and opportunities in drug discovery. , 2014, Journal of medicinal chemistry.
[48] Yun-Xin Fu,et al. Protein dynamics and motions in relation to their functions: several case studies and the underlying mechanisms , 2013, Journal of biomolecular structure & dynamics.
[49] Sergio E. Wong,et al. Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines , 2014, PloS one.
[50] Jung-Hsin Lin,et al. idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach , 2012, Nucleic Acids Res..
[51] W. Somers,et al. Automated systems for protein crystallization. , 2004, Methods.
[52] Gabriele Cruciani,et al. A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application , 2007, J. Chem. Inf. Model..
[53] D. K. Friesen,et al. A combinatorial algorithm for calculating ligand binding , 1984 .
[54] P. Sanseau,et al. Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.
[55] Tiziano Tuccinardi,et al. Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies , 2019, International journal of molecular sciences.
[56] Jianyou Shi,et al. Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. , 2017, MedChemComm.
[57] Haoyang Cai,et al. ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds , 2016, Oncotarget.
[58] M. Mezei,et al. Molecular docking: a powerful approach for structure-based drug discovery. , 2011, Current computer-aided drug design.
[59] Xiaoqin Zou,et al. Docking-based inverse virtual screening: methods, applications, and challenges , 2018, Biophysics reports.
[60] Luca Pinzi,et al. Selection of protein conformations for structure-based polypharmacology studies. , 2018, Drug discovery today.
[61] I M Kapetanovic,et al. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. , 2008, Chemico-biological interactions.
[62] Alan L Harvey,et al. Natural products in drug discovery. , 2008, Drug discovery today.
[63] Philip E. Bourne,et al. A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing , 2011, J. Chem. Inf. Model..
[64] D. Goodsell,et al. Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.
[65] Lin He,et al. Harvesting Candidate Genes Responsible for Serious Adverse Drug Reactions from a Chemical-Protein Interactome , 2009, PLoS Comput. Biol..
[66] A. Poso,et al. Binding Affinity via Docking: Fact and Fiction , 2018, Molecules.
[67] Zhi-Wei Cao,et al. Proteomic characterization of the possible molecular targets of pyrrolizidine alkaloid isoline-induced hepatotoxicity. , 2012, Environmental toxicology and pharmacology.
[68] Gisbert Schneider,et al. Advancing drug discovery via GPU-based deep learning , 2018, Expert opinion on drug discovery.
[69] Zbigniew Dauter,et al. Progress in protein crystallography. , 2016, Protein and peptide letters.
[70] Ashutosh Kumar,et al. Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery , 2018, Front. Chem..
[71] 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.
[72] Lirong Wang,et al. ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research , 2017, J. Chem. Inf. Model..
[73] J. Bajorath,et al. Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space , 2017 .
[74] Benjamin P. Cossins,et al. Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations. , 2016, Journal of the American Chemical Society.
[75] W. L. Jorgensen. The Many Roles of Computation in Drug Discovery , 2004, Science.
[76] Ashutosh Kumar,et al. A cross docking pipeline for improving pose prediction and virtual screening performance , 2017, Journal of Computer-Aided Molecular Design.
[77] F. Gervasio,et al. Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH. , 2018, Journal of chemical theory and computation.
[78] Xing Chen,et al. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences , 2017, Scientific Reports.
[79] Jie Li,et al. Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints , 2017, BMC Bioinformatics.
[80] Pierre Tufféry,et al. Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets , 2018, Oncotarget.
[81] Robert J. Doerksen,et al. Docking Challenge: Protein Sampling and Molecular Docking Performance , 2013, J. Chem. Inf. Model..
[82] X. Chen,et al. TTD: Therapeutic Target Database , 2002, Nucleic Acids Res..
[83] Lingling Jiang,et al. Pharmacophore-Based Similarity Scoring for DOCK , 2014, The journal of physical chemistry. B.
[84] Leonardo L. G. Ferreira,et al. Molecular Docking and Structure-Based Drug Design Strategies , 2015, Molecules.
[85] John B. O. Mitchell,et al. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking , 2010, Bioinform..
[86] Didier Rognan,et al. sc-PDB: an Annotated Database of Druggable Binding Sites from the Protein Data Bank , 2006, J. Chem. Inf. Model..
[87] Nathan Brown,et al. Best of Both Worlds: On the Complementarity of Ligand-Based and Structure-Based Virtual Screening , 2014, J. Chem. Inf. Model..
[88] Ronak Y. Patel,et al. Template-based protein modeling: recent methodological advances. , 2010, Current topics in medicinal chemistry.
[89] Antonio Lavecchia,et al. Machine-learning approaches in drug discovery: methods and applications. , 2015, Drug discovery today.
[90] J. Warwicker,et al. Investigating protein-protein interaction surfaces using a reduced stereochemical and electrostatic model. , 1989, Journal of molecular biology.
[91] Weilin Zhang,et al. Computational Multitarget Drug Design , 2017, J. Chem. Inf. Model..
[92] Ruben Abagyan,et al. Pocketome: an encyclopedia of small-molecule binding sites in 4D , 2011, Nucleic Acids Res..
[93] Y Z Chen,et al. Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. , 2013, Current medicinal chemistry.
[94] Muhammed Tilahun Muhammed,et al. Homology modeling in drug discovery: Overview, current applications, and future perspectives , 2018, Chemical biology & drug design.
[95] David S. Wishart,et al. DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..
[96] Chengfei Yan,et al. Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015 , 2017, Journal of Computer-Aided Molecular Design.
[97] Tingjun Hou,et al. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking , 2011, J. Comput. Chem..
[98] Jacob D. Durrant,et al. Molecular dynamics simulations and drug discovery , 2011, BMC Biology.
[99] Ching-Feng Weng,et al. Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme , 2017, Scientific Reports.
[100] Stefano Alcaro,et al. A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins , 2015, J. Chem. Inf. Model..
[101] Heng Luo,et al. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome , 2016, Scientific Reports.
[102] Jürgen Bajorath,et al. Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes , 2016, J. Chem. Inf. Model..
[103] Ajay N. Jain. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation , 2009, J. Comput. Aided Mol. Des..
[104] J. Berg,et al. Molecular dynamics simulations of biomolecules , 2002, Nature Structural Biology.
[105] Ola Engkvist,et al. On the Integration of In Silico Drug Design Methods for Drug Repurposing , 2017, Front. Pharmacol..
[106] Mohieddin Jafari,et al. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database , 2015, Briefings Bioinform..
[107] Edward W. Lowe,et al. Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.
[108] Michal Vieth,et al. Lessons in Molecular Recognition, 2. Assessing and Improving Cross-Docking Accuracy , 2007, J. Chem. Inf. Model..
[109] S. Wodak,et al. Hemoglobin interaction in sickle cell fibers. I: Theoretical approaches to the molecular contacts. , 1975, Proceedings of the National Academy of Sciences of the United States of America.
[110] Rashmi R. Hazarika,et al. Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana , 2018, bioRxiv.
[111] Ralf Blossey,et al. Molecular docking as a popular tool in drug design, an in silico travel , 2016, Advances and applications in bioinformatics and chemistry : AABC.
[112] G. Rastelli,et al. Evaluation of Amides, Carbamates, Sulfonamides, and Ureas of 4-Prop-2-ynylidenecycloalkylamine as Potent, Selective, and Bioavailable Negative Allosteric Modulators of Metabotropic Glutamate Receptor 5. , 2019, Journal of medicinal chemistry.
[113] B Coupez,et al. Docking and scoring--theoretically easy, practically impossible? , 2006, Current medicinal chemistry.
[114] 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..
[115] Dmitri B. Kireev,et al. Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study , 2014, J. Chem. Inf. Model..
[116] Peer Bork,et al. The SIDER database of drugs and side effects , 2015, Nucleic Acids Res..
[117] Cícero Nogueira dos Santos,et al. Boosting Docking-Based Virtual Screening with Deep Learning , 2016, J. Chem. Inf. Model..
[118] Didier Rognan,et al. Binding of Protein Kinase Inhibitors to Synapsin I Inferred from Pair-Wise Binding Site Similarity Measurements , 2010, PloS one.
[119] Giulio Rastelli. Emerging Topics in Structure-Based Virtual Screening , 2013, Pharmaceutical Research.
[120] Ray Luo,et al. Recent Developments and Applications of the MMPBSA Method , 2018, Front. Mol. Biosci..
[121] Yurii S. Moroz,et al. Ultra-large library docking for discovering new chemotypes , 2019, Nature.
[122] Arielle Rowe,et al. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery , 2018, International journal of molecular sciences.
[123] D J Diller,et al. High throughput docking for library design and library prioritization , 2001, Proteins.
[124] I. Kuntz,et al. Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure. , 1988, Journal of medicinal chemistry.
[125] B. Bush,et al. Macromolecular shape and surface maps by solvent exclusion. , 1978, Proceedings of the National Academy of Sciences of the United States of America.
[126] F R Salemme,et al. An hypothetical structure for an intermolecular electron transfer complex of cytochromes c and b5. , 1976, Journal of molecular biology.
[127] Pietro Liò,et al. DrugClust: A machine learning approach for drugs side effects prediction , 2017, Comput. Biol. Chem..
[128] J. Irwin,et al. Lead discovery using molecular docking. , 2002, Current opinion in chemical biology.
[129] Hui Wang,et al. Ginsenosides as Anticancer Agents: In vitro and in vivo Activities, Structure–Activity Relationships, and Molecular Mechanisms of Action , 2012, Front. Pharmacol..
[130] G. Rastelli,et al. Molecular Dynamics Simulations and Classical Multidimensional Scaling Unveil New Metastable States in the Conformational Landscape of CDK2 , 2016, PloS one.
[131] G. Torrie,et al. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .
[132] G. Degliesposti,et al. Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening , 2009, Chemical biology & drug design.
[133] Oakland J. Peters,et al. Predicting new indications for approved drugs using a proteochemometric method. , 2012, Journal of medicinal chemistry.
[134] Hae-Sang Park,et al. A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..
[135] J. A. Gavira,et al. Current trends in protein crystallization. , 2016, Archives of biochemistry and biophysics.
[136] S. Ekins. Predicting undesirable drug interactions with promiscuous proteins in silico. , 2004, Drug discovery today.
[137] F. Koehn,et al. The evolving role of natural products in drug discovery , 2005, Nature Reviews Drug Discovery.
[138] 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.
[139] Ye-chun Xu,et al. Microsecond molecular dynamics simulation of Aβ42 and identification of a novel dual inhibitor of Aβ42 aggregation and BACE1 activity , 2013, Acta Pharmacologica Sinica.
[140] Giulio Rastelli,et al. BEAR, a Novel Virtual Screening Methodology for Drug Discovery , 2011, Journal of biomolecular screening.
[141] Rona R. Ramsay,et al. A perspective on multi-target drug discovery and design for complex diseases , 2018, Clinical and Translational Medicine.
[142] W. Xiao,et al. Polypharmacology in Drug Discovery: A Review from Systems Pharmacology Perspective. , 2016, Current pharmaceutical design.
[143] Irina G. Tikhonova,et al. Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles , 2013, J. Chem. Inf. Model..
[144] Pratyush Tiwary,et al. Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations. , 2016, Journal of chemical theory and computation.
[145] Oliver Koch,et al. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction , 2019, J. Chem. Inf. Model..
[146] Stephani Joy Y Macalino,et al. Role of computer-aided drug design in modern drug discovery , 2015, Archives of Pharmacal Research.
[147] Irwin D. Kuntz,et al. Development and validation of a modular, extensible docking program: DOCK 5 , 2006, J. Comput. Aided Mol. Des..
[148] Eleftheria Polychronidou,et al. Molecular dynamics simulations through GPU video games technologies. , 2014, Journal of molecular biochemistry.
[149] David S. Wishart,et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..
[150] Kyung-Tae Lee,et al. Repurposing mosloflavone/5,6,7-trimethoxyflavone-resveratrol hybrids: Discovery of novel p38-α MAPK inhibitors as potent interceptors of macrophage-dependent production of proinflammatory mediators. , 2019, European journal of medicinal chemistry.
[151] T. Blundell,et al. Molecular mechanism of SSR128129E, an extracellularly acting, small-molecule, allosteric inhibitor of FGF receptor signaling. , 2013, Cancer cell.
[152] Giulio Rastelli,et al. Exploiting computationally derived out-of-the-box protein conformations for drug design. , 2016, Future medicinal chemistry.
[153] Joo Chuan Tong,et al. Recent advances in computer-aided drug design , 2009, Briefings Bioinform..
[154] Jamie Munro,et al. Trends in clinical success rates and therapeutic focus , 2019, Nature Reviews Drug Discovery.
[155] F E Cohen,et al. Structure-based inhibitor design by using protein models for the development of antiparasitic agents. , 1993, Proceedings of the National Academy of Sciences of the United States of America.
[156] F. von Delft,et al. Where is crystallography going? , 2018, Acta crystallographica. Section D, Structural biology.
[157] A. Laio,et al. Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[158] Jochen L. Leidner,et al. Computational drug repositioning based on side-effects mined from social media , 2016, PeerJ Comput. Sci..
[159] A. Cho,et al. Using reverse docking to identify potential targets for ginsenosides , 2016, Journal of ginseng research.
[160] Jianying Hu,et al. Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions. , 2018, Combinatorial chemistry & high throughput screening.
[161] Pierre Tufféry,et al. MTiOpenScreen: a web server for structure-based virtual screening , 2015, Nucleic Acids Res..
[162] I. Kuntz,et al. Docking flexible ligands to macromolecular receptors by molecular shape. , 1986, Journal of medicinal chemistry.
[163] Philip E. Bourne,et al. Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis , 2009, PLoS Comput. Biol..
[164] Xin Wen,et al. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..
[165] Xiaomin Luo,et al. PDTD: a web-accessible protein database for drug target identification , 2008, BMC Bioinformatics.
[166] J M Blaney,et al. A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.
[167] A. Cavalli,et al. Role of Molecular Dynamics and Related Methods in Drug Discovery. , 2016, Journal of medicinal chemistry.
[168] David S. Goodsell,et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..
[169] M. Kinch,et al. An overview of FDA-approved new molecular entities: 1827-2013. , 2014, Drug discovery today.
[170] Xiaomin Luo,et al. TarFisDock: a web server for identifying drug targets with docking approach , 2006, Nucleic Acids Res..
[171] 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.
[172] Rommie E. Amaro,et al. Ensemble Docking in Drug Discovery. , 2018, Biophysical journal.
[173] Stefano Moro,et al. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview , 2018, Front. Pharmacol..
[174] Klaus Schulten,et al. GPU-accelerated molecular modeling coming of age. , 2010, Journal of molecular graphics & modelling.
[175] G. Rastelli,et al. In Silico Repositioning of Cannabigerol as a Novel Inhibitor of the Enoyl Acyl Carrier Protein (ACP) Reductase (InhA) , 2019, Molecules.
[176] M. Parrinello,et al. Funnel metadynamics as accurate binding free-energy method , 2013, Proceedings of the National Academy of Sciences.
[177] Aurélien Grosdidier,et al. SwissDock, a protein-small molecule docking web service based on EADock DSS , 2011, Nucleic Acids Res..
[178] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[179] R. Zhou. Replica exchange molecular dynamics method for protein folding simulation. , 2007, Methods in molecular biology.
[180] Feng-Cheng Liu,et al. Association Between Tuberculosis and Parkinson Disease , 2016, Medicine.
[181] B. Roux,et al. Computational Study of the “DFG-Flip” Conformational Transition in c-Abl and c-Src Tyrosine Kinases , 2014, The journal of physical chemistry. B.
[182] S. Genheden,et al. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities , 2015, Expert opinion on drug discovery.
[183] Arthur J. Olson,et al. Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge , 2014, Journal of Computer-Aided Molecular Design.
[184] P. Kollman,et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.
[185] Zhenming Liu,et al. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing , 2017, PloS one.
[186] Yi Wang,et al. In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction. , 2006, Toxicology letters.
[187] Wu Zhong,et al. Parallelization of Molecular Docking: A Review. , 2018, Current topics in medicinal chemistry.
[188] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[189] Ashutosh Kumar,et al. Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise , 2016, J. Chem. Inf. Model..
[190] G K Wenning,et al. A clinical and pharmacokinetic case study of an interaction of levodopa and antituberculous therapy in Parkinson's disease , 1995, Movement disorders : official journal of the Movement Disorder Society.
[191] A. Leach,et al. Ligand docking to proteins with discrete side-chain flexibility. , 1994, Journal of molecular biology.
[192] William L Jorgensen,et al. Perspective on Free-Energy Perturbation Calculations for Chemical Equilibria. , 2008, Journal of chemical theory and computation.
[193] Michael M. Mysinger,et al. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.
[194] 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.
[195] N. Klimas,et al. Using a Consensus Docking Approach to Predict Adverse Drug Reactions in Combination Drug Therapies for Gulf War Illness , 2018, International journal of molecular sciences.
[196] F. Gervasio,et al. Conformational Selection and Induced Fit Mechanisms in the Binding of an Anticancer Drug to the c-Src Kinase , 2016, Scientific Reports.
[197] Khichar Shubhakaran,et al. Postmarketing adverse drug reactions: A duty to report? , 2014, Neurology. Clinical practice.
[198] John P. Overington,et al. ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..
[199] Wolfgang Wenzel,et al. Optimization methods for virtual screening on novel computational architectures. , 2011, Current computer-aided drug design.
[200] Yen-Jen Oyang,et al. MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm , 2005, Nucleic Acids Res..
[201] Luca Pinzi,et al. Computational polypharmacology comes of age , 2015, Front. Pharmacol..
[202] Thomas Lengauer,et al. A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.
[203] H. Carlson. Protein flexibility and drug design: how to hit a moving target. , 2002, Current opinion in chemical biology.