Applications of the PDBbind-CN database


Selected Applications of the PDBbind Database
2016 
(1) Bansal, N.; Zheng, Z.; Merz, K. M. Jr. Incorporation of side chain flexibility into protein binding pockets using MTflex. Bioorg. Med. Chem., 2016, 24, 4978-4987.
(2) Greenidge, P.A.; Lewis, R. A.; Ertl, P. Boosting Pose Ranking Performance via Rescoring with MM-GBSA. Chem. Biol. Drug Des., 2016, 88, 317-328.
(3) Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. J. Comput. Aided. Mol. Des. 2016, 30, 791-804.
(4) Grudinin, S.; Popov, P.; Neveu, E.; Cheremovskiy, G. Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential. J. Chem. Inf. Model. 2016, 56, 1053-1062.
(5) Hao, G. F.; Jiang, W.; Ye, Y. N.; Wu, F. X.; Zhu, X. L.; Guo, F. B.; Yang, G. F. ACFIS: a web server for fragment-based drug discovery. Nucleic Acids Res. 2016, 44, W550-W556.
(6) Kadukova, M.; Grudinin, S. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates. J. Chem. Inf. Model. 2016, 56, 1410-1419.
(7) Luo, H.; Zhang, P.; Cao, X. H.; Du, D.; Ye, H.; Huang, H.; Li, C.; Qin, S.; Wan, C.; Shi, L.; He, L.; Yang, L. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome. Sci Rep., 2016, 6, 35996.
(8) Piotto, S.; Di Biasi, L.; Fino, R.; Parisi, R.; Sessa, L.; Concilio, S. Yada: a novel tool for molecular docking calculations. J. Comput. Aided. Mol. Des. 2016, 30, 753-759.
(9) Wang, C.; Nguyen, P. H.; Pham, K.; Huynh, D.; Le, T. B.; Wang, H.; Ren, P.; Luo, R. Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis. J. Comput. Chem., 2016, 37, 2436-2446.
(10) Yan, C.; Grinter, S. Z.; Merideth, B. R.; Ma, Z.; Zou, X. Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks. J. Chem. Inf. Model. 2016, 56, 1013-1021.
(11) Zang, P.; Gong, A.; Zhang, P.; Yu, J. Targeting druggable enzymome by exploiting natural medicines: An in silico-in vitro integrated approach to combating multidrug resistance in bacterial infection. Pharm Biol., 2016, 54, 604-618.
(12) Yan, Z. Q.; Wang, J. Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks. J. Comput. Aided. Mol. Des. 2016, 30, 219-227.
(13) Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys., 2016, 18, 12964-12975.
(14) Tanchuk, V. Y.; Tanin, V. O.; Vovk, A. I.; Poda, G. A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina. Chem. Biol. Drug Des., 2016, 87, 618-625.
(15) Liu, X.; Liu, J.; Zhu, T.; Zhang, L.; He, X.; Zhang, J. Z. PBSA_E: A PBSA-Based Free Energy Estimator for Protein-Ligand Binding Affinity. J. Chem. Inf. Model. 2016, 56, 854-861.
(16) Koebel, M. R.; Schmadeke, G.; Posner, R.G.; Sirimulla, S. AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J. Cheminform., 2016, 8, 27.
(17) Kim, S.; Thiessen, P. A.; Cheng, T.; Yu, B.; Shoemaker B. A.; Wang, J.; Bolton, E. E.; Wang, Y.; Bryant, S. H. Literature information in PubChem: associations between PubChem records and scientific articles. J. Cheminform., 2016, 8, 32.
(18) Hain, E.; Camacho, C. J.; Koes, D. R. Fragment oriented molecular shapes. J. Mol. Graph. Model., 2016, 66, 143-154.
(19) Gong, H. L.; Yuan, Z. C.; Zhan, L. P. High-throughput screening against 6.1 million structurally diverse, lead-like compounds to discover novel ROCK inhibitors for cerebral injury recovery. Mol. Divers., 2016, 20, 537-549.
(20) Chen, A. S.; Westwood, N. J.; Brear, P.; Rogers, G. W.; Mavridis, L.; Mitchell, J. B. A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins. Mol. Inform., 2016, 35, 125-135.
(21) Bolia, A.; Ozkan, S.B.; Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility. J. Chem. Inf. Model. 2016, 56, 734-746.
(22) Basse, M. J.; Betzi, S.; Morelli, X.; Roche, P. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database, 2016, 2016, baw007.
2015 
(23) Zheng, Z.; Wang, T.; Li, P.; Merz, K. M. Jr.  KECSA-Movable Type Implicit Solvation Model (KMTISM). J. Chem. Theory Comput., 2015, 11, 667-682.
(24) Yilmazer, N. D.; Heitel, P.; Schwabe, T.; Korth, M. Benchmark of electronic structure methods for protein-ligand interactions based on high-level reference data. J. Theor. Comput. Chem., 2015, 14, 1540001.
(25) Yang, Z.; Liu, Y.; Chen, Z.; Xu, Z.; Shi, J.; Chen, K.; Zhu, W. A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions. J. Mol. Model., 2015, 21, 138.
(26) Yan, Z. Q.; Wang, J. Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect. Proteins: Struct. Funct. Bioinfor., 2015, 83, 1632-1642.
(27) Wojcikowski, M.; Zielenkiewicz, P.; Siedlecki, P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J. Cheminform., 2015, 7.
(28) Weber, J.; Achenbach, J.; Moser, D.; Proschak, E.  VAMMPIRE-LORD: A Web Server for Straightforward Lead Optimization Using Matched Molecular Pairs. J. Chem. Inf. Model. 2015, 55, 207-213.
(29) Wang, Y.; Guo, Y.; Kuang, Q.; Pu, X.; Ji, Y.; Zhang, Z.; Li, M. A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach. J. Comput. Aided. Mol. Des. 2015, 29, 349-360.
(30) Trepte, P.; Buntru, A.; Klockmeier, K.; Willmore, L.; Arumughan, A.; Secker, C.; Zenkner, M.; Brusendorf, L.; Rau, K.; Redel, A.; Wanker, E. E. DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells. J. Mol. Biol., 2015, 427, 3375-3388.
(31) Su, H.; Yan, J.; Xu, J.; Fan, X. Z.; Sun, X. L.; Chen, K.Y. Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension. Pharm. Biol., 2015, 53, 1201-1206.
(32) Razzaghi-Asl, N.; Razzaghi-Asl, N.; Sepehri, S.; Ebadi, A.; Miri, R.; Shahabipour, S. Effect of Biomolecular Conformation on Docking Simulation: A Case Study on a Potent HIV-1 Protease Inhibitor. Iran. J. Pharm. Res., 2015, 14, 785-802.
(33) Qiu, J. X.; Zhou, Z. W.; He, Z. X.; Zhao, R. J.; Zhang, X.; Yang, L.; Zhou, S. F.; Mao, Z. F.; Plumbagin elicits differential proteomic responses mainly involving cell cycle, apoptosis, autophagy, and epithelial-to-mesenchymal transition pathways in human prostate cancer PC-3 and DU145 cells. Drug Des. Devel. Ther., 2015, 9 349-417.
(34) Lizunov, A.Y.; Gonchar, A. L.; Zaitseva, N. I.; Zosimov, V. V. Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function. J. Chem. Inf. Model. 2015, 55, 2121-2137.
(35) Li, H. J.; Leung, K. S.; Wong, M. H.; Ballester, P. J. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. Molecules, 2015, 20, 10947-10962.
(36) Kothiwale, S.; Mendenhall, J. L.; Meiler, J. BCL::CONF: small molecule conformational sampling using a knowledge based rotamer library. J. Cheminform., 2015, 7.
(37) Khamis, M.A.; Gomaa, W. Comparative assessment of machine-learning scoring functions on PDBbind 2013, Eng. Appl. Artif. Intel., 2015, 45 136-151.
(38) He, W. H.; Shi, F.; Zhou, Z. W.; Li, B.; Zhang, K.; Zhang, X.; Ouyang, C.; Zhou, S. F.; Zhu, X. A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver. Drug Des. Devel. Ther., 2015, 9, 3989-4104.
(39) He, M.W.; Lee, P. S.; Sweeney, Z. K. Promiscuity and the Conformational Rearrangement of Drug-Like Molecules: Insight from the Protein Data Bank. Chemmedchem, 2015, 10, 238-244.
(40) Gaudreault, F.; Najmanovich, R. J. FlexAID: Revisiting Docking on Non-Native-Complex Structures. J. Chem. Inf. Model. 2015, 55, 1323-1336.
(41) Dias, R.; Kolazckowski, B. Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy. Proteins: Struct. Funct. Bioinfor., 2015, 83, 2100-2114.
(42) Degac, J.; Winter, U.; Helms, V. Graph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces. J. Chem. Inf. Model. 2015, 55, 1944-1952.
(43) Cui, Y. H.; Chen, J.; Xu, T.; Tian, H. L. Structure-based grafting and identification of kinase-inhibitors to target mTOR signaling pathway as potential therapeutics for glioblastoma. Comput. Biol. Chem., 2015, 54, 57-65.
(44) Chartier, M.; Najmanovich, R. Detection of Binding Site Molecular Interaction Field Similarities. J. Chem. Inf. Model. 2015, 55, 1600-1615.
(45) Bai, F.; Liao, S.; Gu, J.; Jiang, H.; Wang, X.; Li, H. An Accurate Metalloprotein-Specific Scoring Function and Molecular Docking Program Devised by a Dynamic Sampling and Iteration Optimization Strategy. J. Chem. Inf. Model. 2015, 55, 833-847.
(46) Ashtawy, H. M.; Mahapatra, N. R. BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC Bioinformatics 2015, 16.
(47) Ashtawy, H. M.; Mahapatra, N. R. A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction. IEEE/ACM Trans Comput Biol Bioinform., 2015, 12, 335-347.
(48) Ashtawy, H. M.; Mahapatra, N. R. Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins. BMC Bioinformatics 2015, 16.
(49) Alhossary, A.; Handoko, S. D.; Mu, Y.; Kwoh, C. K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics, 2015, 31, 2214-2216.
2014 
(50) Zhao, H. Y.; Yang, Y.; von Itzstein, M.; Zhou, Y. Carbohydrate-Binding Protein Identification by Coupling Structural Similarity Searching with Binding Affinity Prediction. J. Comput. Chem., 2014, 35, 2177-2183.
(51) Wang, X.; Chen, H.; Yang, F.; Gong, J.; Li, S.; Pei, J.; Liu, X.; Jiang, H.; Lai, L.; Li, H. iDrug: a web-accessible and interactive drug discovery and design platform. J. Cheminform., 2014, 6, 28.
(52) Todoroff, N.; Kunze, J.; Schreuder, H.; Hessler, G.; Baringhaus, K. H.; Schneider, G. Fractal Dimensions of Macromolecular Structures. Mol. Inform., 2014, 33, 588-596.
(53) Sun, H. Y.; Li, Y.; Tian, S.; Xu, L.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys., 2014, 16, 16719-16729.
(54) Su, J. H.; Chang, C.; Xiang, Q.; Zhou, Z. W.; Luo, R.; Yang, L.; He, Z. X.; Yang, H.; Li, J.; Bei, Y.; Xu, J.; Zhang, M.; Zhang, Q.; Su, Z.; Huang, Y.; Pang, J.; Zhou, S. F. Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study. Drug Des. Devel. Ther., 2014, 8, 2555-2602.
(55) Singh, K. D.; Naveena, Q.; Karthikeyan, M. Jak2 inhibitor - a jackpot for pharmaceutical industries: a comprehensive computational method in the discovery of new potent Jak2 inhibitors. Mol. Biosyst., 2014, 10, 2146-2159.
(56) Reddy, A.S.; Tan, Z.; Zhang, S. X. Curation and Analysis of Multitargeting Agents for Polypharmacological Modeling. J. Chem. Inf. Model. 2014, 54, 2536-2543.
(57) Perna, A. M.; Reisen, F.; Schmidt, T. P.; Geppert, T.; Pillong, M.; Weisel, M.; Hoy, B.; Simister, P. C.; Feller, S. M.; Wessler, S.; Schneider, G. Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site. Chem. Sci., 2014, 5, 3583-3590.
(58) Luo, J. S.; Guo, Y.; Zhong, Y.; Ma, D.; Li, W.; Li, M. A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity. J. Comput. Aided. Mol. Des. 2014, 28, 619-629.
(59) Luo, H.; Zhang, P.; Huang, H.; Huang, J.; Kao, E.; Shi, L.; He, L.; Yang, L. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Res. 2014, 42, W46-W52.
(60) Lindblom, P. R.; Wu, G.; Liu, Z.; Jim, K. C.; Baldwin,  J. J.; Gregg, R. E.; Claremon, D. A.; Singh, S. B. An electronic environment and contact direction sensitive scoring function for predicting affinities of protein-ligand complexes in Contour (R). J. Mol. Graph. Model., 2014, 53, 118-127.
(61) Li, H. J.; Leung, K. S.; Wong, M. H.; Ballester, P. J. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics 2014, 15.
(62) Li, H. J.; Leung, K. S.; Ballester, P. J.; Wong, M. H. istar: A Web Platform for Large-Scale Protein-Ligand Docking. Plos One, 2014, 9.
(63) Kuenemann, M. A.; Bourbon, L. M.; Labb¨¦, C. M.; Villoutreix, B. O.; Sperandio, O. Which Three-Dimensional Characteristics Make Efficient Inhibitors of Protein-Protein Interactions? J. Chem. Inf. Model. 2014, 54, 3067-3079.
(64) Kastritis, P. L.; Rodrigues, J. P.; Folkers, G. E.; Boelens, R.; Bonvin, A. M. Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. J. Mol. Biol., 2014, 426, 2632-2652.
(65) Hu, B. J.; Zhu, X.; Monroe, L.; Bures, M. G.; Kihara, D. PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions. Int. J. Mol. Sci., 2014, 15, 15122-15145.
(66) Hu, B.J.; Lill, M. A. PharmDock: a pharmacophore-based docking program. J. Cheminform., 2014, 6.
(67) Grinter, S. Z.; Zou, X. Q. A Bayesian Statistical Approach of Improving Knowledge-Based Scoring Functions for Protein-Ligand Interactions. J. Comput. Chem., 2014, 35, 932-943.
(68) Greenidge, P. A.; Kramer, C.; Mozziconacci, J. C.; Sherman, W. Improving Docking Results via Reranking of Ensembles of Ligand Poses in Multiple X-ray Protein Conformations with MM-GBSA. J. Chem. Inf. Model. 2014, 54, 2697-2717.
(69) Gabel, J.; Desaphy, J.; Rognan, D. Beware of Machine Learning-Based Scoring Functions-On the Danger of Developing Black Boxes. J. Chem. Inf. Model. 2014, 54, 2807-2815.
(70) Dunbar, J.; Krawczyk, K.; Leem, J.; Baker, T.; Fuchs, A.; Georges, G.; Shi, J.; Deane, C. M. SAbDab: the structural antibody database. Nucleic Acids Res. 2014, 42, D1140-D1146.
(71) Cao, Y.; Li, L. Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics, 2014, 30, 1674-1680.
(72) Cao, R.; Huang, N.; Wang, Y. L. Evaluation and Application of MD-PB/SA in Structure-Based Hierarchical Virtual Screening. J. Chem. Inf. Model. 2014, 54, 1987-1996.
(73) Ballester, P. J.; Schreyer, A.; Blundell, T. L. Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity? J. Chem. Inf. Model. 2014, 54, 944-955.
(74) Atkovska, K.; Samsonov, S. A. Paszkowski-Rogacz, M.; Pisabarro, M. T.; Multipose Binding in Molecular Docking. Int. J. Mol. Sci., 2014, 15, 2622-2645.
(75) Anand, P.; Nagarajan, D.; Mukherjee, S.; Chandra, N. PLIC: protein-ligand interaction clusters. Database, 2014, 2014, bau029.
2013 
(76) Hsin, K.Y.; Ghosh, S.; Kitano, H. Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology. Plos One, 2013, 8, e83922.
(77) Zheng, Z.; Ucisik, M. N.; Merz, K. M. Jr. The Movable Type Method Applied to Protein-Ligand Binding. J. Chem. Theory Comput., 2013, 9, 5526-5538.
(78) Liu, Y.; Xu, Z.; Yang, Z.; Chen, K.; Zhu, W. A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions. J. Mol. Model., 2013, 19, 5015-5030.
(79) Korth, M. Error estimates for (semi-)empirical dispersion terms and large biomacromolecules. Org. Biomol. Chem., 2013, 11, 6515-6519.
(80) Wang, W.; He, W.; Zhou, X.; Chen, X. Optimization of molecular docking scores with support vector rank regression. Proteins: Struct. Funct. Bioinfor., 2013, 81, 1386-1398.
(81) Zilian, D.; Sotriffer, C. A. SFCscore(RF): A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes. J. Chem. Inf. Model. 2013, 53, 1923-1933.
(82) Yilmazer, N. D.; Korth, M. Comparison of Molecular Mechanics, Semi-Empirical Quantum Mechanical, and Density Functional Theory Methods for Scoring Protein-Ligand Interactions. J. Phys. Chem. B, 2013, 117, 8075-8084.
(83) Perot, S.; Regad, L.; Reyn¨¨s, C.; Sp¨¦randio, O.; Miteva, M. A.; Villoutreix, B. O.; Camproux, A. C. Insights into an Original Pocket-Ligand Pair Classification: A Promising Tool for Ligand Profile Prediction. Plos One, 2013, 8.
(84) Wirth, M.; Volkamer, A.; Zoete, V.; Rippmann, F.; Michielin, O.; Rarey, M.; Sauer, W. H. Protein pocket and ligand shape comparison and its application in virtual screening. J. Comput. Aided. Mol. Des. 2013, 27, 511-524.
(85) Beato, C.; Beccari, A. R.; Cavazzoni, C.; Lorenzi, S.; Costantino, G. Use of Experimental Design To Optimize Docking Performance: The Case of LiGenDock, the Docking Module of Ligen, a New De Novo Design Program. J. Chem. Inf. Model. 2013, 53, 1503-1517.
(86) Schumann, M.; Armen, R. S. Systematic and efficient side chain optimization for molecular docking using a cheapest-path procedure. J. Comput. Chem., 2013, 34, 1258-1269.
(87) Zheng, Z.; Merz, K. M. Jr. Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein-Ligand Interactions. J. Chem. Inf. Model. 2013, 53, 1073-1083.
(88) Hu, B.; Lill, M. A. Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking. J. Chem. Inf. Model. 2013, 53, 1179-1190.
(89) Garcia-Sosa, A. T.; Maran, U. Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology. SAR QSAR Environ Res., 2013, 24, 585-597.
(90) Chen, J.; Sawyer, N.; Regan, L. Protein-protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area. Protein Sci., 2013, 22, 510-515.
(91) Yuan, Y.; Pei, J.; Lai, L. Binding Site Detection and Druggability Prediction of Protein Targets for Structure-Based Drug Design. Curr. Pharm. Des., 2013, 19, 2326-2333.
(92) Li, G. B.; Yang, L. L.; Wang, W. J.; Li, L. L.; Yang, S. Y. ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein-Ligand Interactions. J. Chem. Inf. Model. 2013, 53, 592-600.
(93) Jalencas, X.; Mestres, J. Chemoisosterism in the Proteome. J. Chem. Inf. Model. 2013, 53, 279-292.
(94) Pacholczyk, M.; Borys, D.; Kimmel, M. Finite absorbing Markov chain as a model of small-ligand binding process. IWBBIO Proceedings, 2013, 747.
(95) Schneider, N.; Lange, G.; Hindle, S.; Klein, R.; Rarey, M.  A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J. Comput. Aided. Mol. Des. 2013, 27, 15-29.
(96) Greenidge, P. A.; Kramer, C.; Mozziconacci, J.C.; Wolf, R. M. MM/GBSA Binding Energy Prediction on the PDBbind Data Set: Successes, Failures, and Directions for Further Improvement. J. Chem. Inf. Model. 2013, 53, 201-209.
(97) Jalencas, X.; Mestres, J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol. Inform., 2013, 32, 976-990.
(98) Dhanik, A.; McMurray, J. S.; Kavraki, L. E. DINC: A new AutoDock-based protocol for docking large ligands. BMC Struct. Biol., 2013, 13.
(99) Koppisetty, C. A.; Frank, M.; Kemp, G. J.; Nyholm, P. G. Computation of Binding Energies Including Their Enthalpy and Entropy Components for Protein-Ligand Complexes Using Support Vector Machines. J. Chem. Inf. Model. 2013, 53, 2559-2570.
(100) Weber, J.; Achenbach, J.; Moser, D.; Proschak, E. VAMMPIRE: A Matched Molecular Pairs Database for Structure-Based Drug Design and Optimization. J. Med. Chem. 2013, 56, 5203-5207.
(101) Garcia-Sosa, A. T. Hydration Properties of Ligands and Drugs in Protein Binding Sites: Tightly-Bound, Bridging Water Molecules and Their Effects and Consequences on Molecular Design Strategies. J. Chem. Inf. Model. 2013, 53, 1388-1405.
(102) Houston, D. R.; Walkinshaw, M. D. Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context. J. Chem. Inf. Model. 2013, 53, 384-390.
(103) Wang, K.; Gao, J.; Shen, S.; Tuszynski, J. A.; Ruan, J.; Hu, G. An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function. Biomed. Res. Int. 2013, 2013, 409658
(104) Hou, X.; Du, J.; Zhang, J.; Du, L.; Fang, H.; Li, M. How to Improve Docking Accuracy of AutoDock4.2: A Case Study Using Different Electrostatic Potentials. J. Chem. Inf. Model. 2013, 53, 188-200.
(105) Yang, J.; Roy, A.; Zhang, Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res. 2013, 41, D1096-D1103.
2012 
(106) Higueruelo, A. P.; Schreyer, A.; Bickerton, G. R. J.; Blundell, T. L.; Pitt, W. R. What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics? Plos One 2012, 7, e51472.
(107) Cross, S.; Baroni, M.; Goracci, L.; Cruciani, G. GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation. J. Chem. Inf. Model. 2012, 52, 2587-2598.
(108) Garcia-Sosa, A. T.; Oja, M.; Hetenyi, C.; Maran, U. DrugLogit: Logistic Discrimination between Drugs and Nondrugs Including Disease-Specificity by Assigning Probabilities Based on Molecular Properties. J. Chem. Inf. Model. 2012, 52, 2165-2180.
(109) Thompson, A. D.; Dugan, A.; Gestwicki, J. E.; Mapp, A. K. Fine-Tuning Multiprotein Complexes Using Small Molecules. ACS Chem. Biol. 2012, 7, 1311-1320.
(110) Kumar, A.; Zhang, K. Y. J. Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge. J. Comput. Aided Mol. Des. 2012, 26, 603-616.
(111) Benson, M. L.; Faver, J. C.; Ucisik, M. N.; Dashti, D. S.; Zheng, Z.; Merz, K. M. Jr. Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores. J. Comput. Aided Mol. Des. 2012, 26, 647-659.
(112) Joung, J. Y.; Nam, K.-Y.; Cho, K.-H.; No, K. T. Ligand Aligning Method for Molecular Docking: Alignment of Property-Weighted Vectors. J. Chem. Inf. Model. 2012, 52, 984-995.
(113) Perola, E.; Herman, L.; Weiss, J. Development of a Rule-Based Method for the Assessment of Protein Druggability. J. Chem. Inf. Model. 2012, 52, 1027-1038.
(114) Wilson, G. L.; Lill, M. A. Towards a Realistic Representation in Surface-Based Pseudoreceptor Modeling: a PDB-Wide Analysis of Binding Pockets. Mol. Inform., 2012, 31, 259-271.
(115) Yan, Z.; Wang, J. Specificity quantification of biomolecular recognition and its implication for drug discovery. Sci Rep. 2012, 2:309
(116) Hsu, K.-C.; Chen, Y.-F.; Yang, J.-M. GemAffinity: a scoring function for predicting binding affinity and virtual screening. Int J Data Min Bioinform., 2012, 6, 27-41.
(117) Malisi, C.; Schumann, M.; Toussaint, N. C.; Kagayama, J.; Kohlbacher, O.; Hocker, B. Binding Pocket Optimization by Computational Protein Design. Plos One 2012, 7, e52505.
(118) Dhanik, A.; McMurray, J. S.; Kavraki, L. E. Binding Modes of Peptidomimetics Designed to Inhibit STAT3. Plos One 2012, 7, e51603.
(119) Garcia-Sosa, A. T.; Oja, M.; Hetenyi, C.; Maran, U. Disease-Specific Differentiation Between Drugs and Non-Drugs Using Principle Component Analysis of Their Molecular Descriptor Space. Mol. Inform., 2012, 31, 369-383.
(120) Koes, D. R.; Camacho, C. J. Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinformatics 2012, 28, 784-791.
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