Applications of the PDBbind-CN database
Selected Applications of the PDBbind Database 2021 (1) Maguire, J. B.; Haddox, H. K.; Strickland, D.; Halabiya, S. F.; Coventry, B.; Griffin, J. R.; Pulavarti, S.; Cummins, M.; Thieker, D. F.; Klavins, E.; Szyperski, T.; DiMaio, F.; Baker, D.; Kuhlman, B., Perturbing the energy landscape for improved packing during computational protein design. Proteins 2021, 89, 436-449. (PDBbind) 2020 (2) Zhu, F.; Zhang, X.; Allen, J. E.; Jones, D.; Lightstone, F. C., Binding Affinity Prediction by Pairwise Function Based on Neural Network. J Chem Inf Model 2020, 60, 2766-2772. ( PDBbind 2018 refined set) (3) Zheng, Z.; Borbulevych, O. Y.; Liu, H.; Deng, J.; Martin, R. I.; Westerhoff, L. M., MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment. J Chem Inf Model 2020, 60, 5437-5456. ( PDBbind 2019) (4) Zhang, H.; Saravanan, K. M.; Lin, J.; Liao, L.; Ng, J. T.; Zhou, J.; Wei, Y., DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020, 8, e8864. ( PDBbind 2017) (5) Yuan, J. H.; Han, S. B.; Richter, S.; Wade, R. C.; Kokh, D. B., Druggability Assessment in TRAPP Using Machine Learning Approaches. J Chem Inf Model 2020, 60, 1685-1699. ( PDBbind 2017 refined set) (6) Yoshidome, T.; Ikeguchi, M.; Ohta, M., Comprehensive 3D-RISM analysis of the hydration of small molecule binding sites in ligand-free protein structures. J Comput Chem 2020, 41, 2406-2419. ( PDBbind 2017 refined set) (7) Yang, J.; Shen, C.; Huang, N., Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets. Front Pharmacol 2020, 11, 69. ( PDBbind 2015) (8) Yang, J.; Kwon, S.; Bae, S. H.; Park, K. M.; Yoon, C.; Lee, J. H.; Seok, C., GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds. J Chem Inf Model 2020, 60, 3246-3254. ( PDBbind 2018) (9) Xie, L.; Xu, L.; Kong, R.; Chang, S.; Xu, X., Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning. Frontiers in Pharmacology 2020, 11. (PDBbind) (10)Xie, L.; Xu, L.; Chang, S.; Xu, X.; Meng, L., Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding. Chem Biol Drug Des 2020, 96, 973-983. (PDBbind) (11)Willow, S. Y.; Xie, B.; Lawrence, J.; Eisenberg, R. S.; Minh, D. D. L., On the polarization of ligands by proteins. Phys Chem Chem Phys 2020, 22, 12044-12057. (PDBbind 2016 core set) (12)Wierbowski, S. D.; Wingert, B. M.; Zheng, J.; Camacho, C. J., Cross-docking benchmark for automated pose and ranking prediction of ligand binding. Protein Sci 2020, 29, 298-305. (binding data) (13)Wang, Y.; Hu, J.; Lai, J.; Li, Y.; Jin, H.; Zhang, L.; Zhang, L. R.; Liu, Z. M., TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network. J Chem Inf Model 2020, 60, 2754-2765. (PDBbind 2018 General Set) (14)Wang, B.; Ng, H. L., Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4. J Comput Aided Mol Des 2020, 34, 201-217. (PDBbind 2017) (15)Varela-Rial, A.; Majewski, M.; Cuzzolin, A.; Martinez-Rosell, G.; De Fabritiis, G., SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J Chem Inf Model 2020, 60, 2673-2677. (PDBbind 2018 refined set) (16)Su, M.; Feng, G.; Liu, Z.; Li, Y.; Wang, R., Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? J Chem Inf Model 2020, 60, 1122-1136. (PDBbind 2016 refined set) (17)Sriramulu, D. K.; Wu, S.; Lee, S.-G., Effect of ligand torsion number on the AutoDock mediated prediction of protein-ligand binding affinity. Journal of Industrial and Engineering Chemistry 2020, 83, 359-365. (PDBbind) (18)Sriramulu, D. K.; Lee, S. G., Combinatorial Effect of Ligand and Ligand-Binding Site Hydrophobicities on Binding Affinity. J Chem Inf Model 2020, 60, 1678-1684. (PDBbind) (19)Soni, A.; Bhat, R.; Jayaram, B., Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. J Comput Aided Mol Des 2020, 34, 817-830. (PDBbind 2007 2013 2016 2017) (20)Šlachtová, V.; Šebela, M.; Torfs, E.; Oorts, L.; Cappoen, D.; Berka, K.; Bazgier, V.; Brulíková, L., Novel thiazolidinedione-hydroxamates as inhibitors of Mycobacterium tuberculosis virulence factor Zmp1. European Journal of Medicinal Chemistry 2020, 185, 111812. (binding data) (21)Singh, N.; Decroly, E.; Khatib, A. M.; Villoutreix, B. O., Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages. Eur J Pharm Sci 2020, 153, 105495. (PDBbind) (22)Pinzi, L.; Rastelli, G., Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank. J Chem Inf Model 2020, 60, 372-390. (PDBbind) (23)Penner, P.; Martiny, V.; Gohier, A.; Gastreich, M.; Ducrot, P.; Brown, D.; Rarey, M., Shape-Based Descriptors for Efficient Structure-Based Fragment Growing. J Chem Inf Model 2020, 60, 6269-6281. (PDBbind 2019) (24)Na, G. S.; Kim, H. W.; Chang, H., Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis. J Chem Inf Model 2020, 60, 1137-1145. (PDBbind) (25)Meli, R.; Biggin, P. C., spyrmsd: symmetry-corrected RMSD calculations in Python. J Cheminform 2020, 12, 49. (PDBbind refined set) (26)Marchand, J. R.; Knehans, T.; Caflisch, A.; Vitalis, A., An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules. J Chem Inf Model 2020, 60, 5188-5202. (PDBbind) (27)Macari, G.; Toti, D.; Pasquadibisceglie, A.; Polticelli, F., DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina. Int J Mol Sci 2020, 21. (PDBbind 2013 2016 2018) (28)Liu, Y.; Grimm, M.; Dai, W. T.; Hou, M. C.; Xiao, Z. X.; Cao, Y., CB-Dock: a web server for cavity detection-guided protein-ligand blind docking. Acta Pharmacol Sin 2020, 41, 138-144. (PDBbind 2018) (29)Li, Y.; Gao, Y.; Holloway, M. K.; Wang, R., Prediction of the Favorable Hydration Sites in a Protein Binding Pocket and Its Application to Scoring Function Formulation. J Chem Inf Model 2020, 60, 4359-4375. (PDBbind 2016 core set) (30)Li, S.; Wan, F.; Shu, H.; Jiang, T.; Zhao, D.; Zeng, J., MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities. Cell Systems 2020, 10, 308-322.e11. (PDBbind 2018) (31)Li, J.; Song, Y.; Li, F.; Zhang, H.; Liu, W., FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm. Comput Biol Chem 2020, 88, 107363. (PDBbind 2012) (32)Li, G.; Su, Y.; Yan, Y. H.; Peng, J. Y.; Dai, Q. Q.; Ning, X. L.; Zhu, C. L.; Fu, C.; McDonough, M. A.; Schofield, C. J.; Huang, C.; Li, G. B., MeLAD: an integrated resource for metalloenzyme-ligand associations. Bioinformatics 2020, 36, 904-909. (PDBbind) (33)Laufkotter, O.; Laufer, S.; Bajorath, J., Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome. Data Brief 2020, 32, 106189. (PDBbind) (34)Kwon, Y.; Shin, W. H.; Ko, J.; Lee, J., AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks. Int J Mol Sci 2020, 21(22), 8424. (PDBbind 2016 2018) (35)Katigbak, J.; Li, H.; Rooklin, D.; Zhang, Y., AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using beta-Clusters. J Chem Inf Model 2020, 60, 1494-1508. (PDBbind 2016 refined set) (36)Karlov, D. S.; Sosnin, S.; Fedorov, M. V.; Popov, P., graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes. ACS Omega 2020, 5, 5150-5159. (PDBbind 2007 2018) (37)Kadukova, M.; Chupin, V.; Grudinin, S., Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J Comput Aided Mol Des 2020, 34, 191-200. (PDBbind) (38)Jimenez-Luna, J.; Cuzzolin, A.; Bolcato, G.; Sturlese, M.; Moro, S., A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules 2020, 25(11), 2487. (PDBbind 2017) (39)Jiang, H.; Fan, M.; Wang, J.; Sarma, A.; Mohanty, S.; Dokholyan, N. V.; Mahdavi, M.; Kandemir, M. T., Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks. J Chem Inf Model 2020, 60, 4594-4602.(PDBbind 2016) (40)Huang, K.; Luo, S.; Cong, Y.; Zhong, S.; Zhang, J. Z. H.; Duan, L., An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity. Nanoscale 2020, 12, 10737-10750. (PDBbind) (41)Holderbach, S.; Adam, L.; Jayaram, B.; Wade, R. C.; Mukherjee, G., RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features. Front Mol Biosci 2020, 7, 601065. (PDBbind 2018 refined set) (42)Hassan-Harrirou, H.; Zhang, C.; Lemmin, T., RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks. J Chem Inf Model 2020, 60, 2791-2802. (PDBbind 2016 2018) (43)Ghanbarpour, A.; Mahmoud, A. H.; Lill, M. A., Instantaneous generation of protein hydration properties from static structures. Communications Chemistry 2020, 3. (PDBbind 2016 refined set) (44)Gao, K.; Nguyen, D. D.; Sresht, V.; Mathiowetz, A. M.; Tu, M.; Wei, G. W., Are 2D fingerprints still valuable for drug discovery? Phys Chem Chem Phys 2020, 22, 8373-8390. (PDBbind 2015 2016) (45)Gao, K.; Nguyen, D. D.; Chen, J.; Wang, R.; Wei, G. W., Repositioning of 8565 Existing Drugs for COVID-19. J Phys Chem Lett 2020, 11, 5373-5382. (PDBbind 2019) (46)Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E., Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods 2020, 17, 184-192. (PDBbind) (47)Francoeur, P. G.; Masuda, T.; Sunseri, J.; Jia, A.; Iovanisci, R. B.; Snyder, I.; Koes, D. R., Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. J Chem Inf Model 2020, 60, 4200-4215. (PDBbind 2016) (48)Flachsenberg, F.; Meyder, A.; Sommer, K.; Penner, P.; Rarey, M., A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses. J Chem Inf Model 2020, 60, 6502-6522. (PDBbind 2016) (49)Fine, J.; Muhoberac, M.; Fraux, G.; Chopra, G., DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening. J Chem Inf Model 2020, 60, 4137-4143. (PDBbind 2016) (50)Fine, J.; Konc, J.; Samudrala, R.; Chopra, G., CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials. J Chem Inf Model 2020, 60, 1509-1527. (PDBbind 2016 core set) (51)Feldmann, C.; Bajorath, J., Biological Activity Profiles of Multitarget Ligands from X-ray Structures. Molecules 2020, 25(4), 794. (PDBbind 2018) (52)Feldmann, C.; Bajorath, J., X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes. Int J Mol Sci 2020, 21(11), 3782. (PDBbind 2019) (53)Durai, P.; Ko, Y. J.; Pan, C. H.; Park, K., Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening. BMC Bioinformatics 2020, 21, 309. (PDBbind) (54)Copoiu, L.; Torres, P. H. M.; Ascher, D. B.; Blundell, T. L.; Malhotra, S., ProCarbDB: a database of carbohydrate-binding proteins. Nucleic Acids Res 2020, 48, D368-D375. (PDBbind) (55)Cinaroglu, S. S.; Timucin, E., Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets. Brief Bioinform 2020, 21, 2112-2125. (PDBbind refined set) (56)Cho, H.; Lee, E. K.; Choi, I. S., Layer-wise relevance propagation of InteractionNet explains protein-ligand interactions at the atom level. Sci Rep 2020, 10, 21155. (PDBbind 2018) (57)Cang, Z.; Wei, G. W., Persistent Cohomology for Data With Multicomponent Heterogeneous Information. SIAM J Math Data Sci 2020, 2, 396-418. (PDBbind 2007 2013 2015 2016) (58)Boyles, F.; Deane, C. M.; Morris, G. M., Learning from the ligand: using ligand-based features to improve binding affinity prediction. Bioinformatics 2020, 36, 758-764. (PDBbind 2007 2013 2016 2018) (59)Bonanni, D.; Lolli, M. L.; Bajorath, J., Computational Method for Structure-Based Analysis of SAR Transfer. J Med Chem 2020, 63, 1388-1396. (PDBbind) (60)Bao, J.; He, X.; Zhang, J. Z. H., Development of a New Scoring Function for Virtual Screening: APBScore. J Chem Inf Model 2020, 60, 6355-6365. (PDBbind 2016 2018) 2019 (61)Yang, Y.; Lu, J.; Yang, C.; Zhang, Y., Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S. J Comput Aided Mol Des 2019, 33, 1095-1105.(PDBbind 2016) (62)Yang, W.; Sun, X.; Zhang, C.; Lai, L., Discovery of novel helix binding sites at protein-protein interfaces. Comput Struct Biotechnol J 2019, 17, 1396-1403. (PDBbind 2014) (63)Wang, F.; Wu, F. X.; Li, C. Z.; Jia, C. Y.; Su, S. W.; Hao, G. F.; Yang, G. F., ACID: a free tool for drug repurposing using consensus inverse docking strategy. J Cheminform 2019, 11, 73. (PDBbind) (64)Vangone, A.; Schaarschmidt, J.; Koukos, P.; Geng, C.; Citro, N.; Trellet, M. E.; Xue, L. C.; Bonvin, A., Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server. Bioinformatics 2019, 35, 1585-1587. (PDBbind 2013 core set) (65)Moman, E.; Grishina, M. A.; Potemkin, V. A., Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019, 33, 943-953. (PDBbind 2018) (66)Lu, J.; Hou, X.; Wang, C.; Zhang, Y., Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions. J Chem Inf Model 2019, 59, 4540-4549. (PDBbind 2016) (67)Li, Y. J.; Rezaei, M. A.; Li, C. L.; Li, X. L. DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction. In 2019 Ieee International Conference on Bioinformatics and Biomedicine, Yoo, I. H.; Bi, J. B.; Hu, X., Eds.; 2019, pp 303-310. (PDBbind 2016 2018) (68)Jimenez-Luna, J.; Perez-Benito, L.; Martinez-Rosell, G.; Sciabola, S.; Torella, R.; Tresadern, G.; De Fabritiis, G., DeltaDelta neural networks for lead optimization of small molecule potency. Chem Sci 2019, 10, 10911-10918. (PDBbind 2016) (69)Ivanov, S. M.; Dimitrov, I.; Doytchinova, I. A., Bridging solvent molecules mediate RNase A - Ligand binding. PLoS One 2019, 14, e0224271. (PDBbind 2018 refined set) (70)Ribeiro, J.; Rios-Vera, C.; Melo, F.; Schuller, A. Calculation of accurate interatomic contact surface areas for the quantitative analysis of non-bonded molecular interactions. Bioinformatics 2019, 35, 3499-3501. (PDBbind 2016) (71)Lim, J.; Ryu, S.; Park, K.; Choe, Y. J.; Ham, J.; Kim, W. Y. Predicting drug-target interaction using a novel graph neural network with 3D structure-embeded graph representation. J. Chem. Inf. Model. 2019, 59, 3981-3988. (PDBbind 2018) (72)Yang, J.;Baek, M.; Seok, C. GalaxyDock3: protein-ligand docking that considers the full ligand conformational flexibility. J. Comput. Chem. 2019, 40, 2739-2748. (PDBbind 2016) (73)Zheng, L.; Fan, J.; Mu, Y.; OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega 2019, 4, 15956-15965. (PDBbind 2016) (74)Berishvili, V. P.; Perkin, V. O.; Voronkov, A. E.; Radchenko, E. V.; Syed, R.; Reddy, C. V. R.; Pillay, V.; Kumar, P.; Choonara, Y. E.; Kamal, A.; Palyulin, V. A. Time-domain analysis of molecular dynamics trajectories using deep neural networks: application to activity ranking of tankyrase inhibitors. J. Chem. Inf. Model. 2019, 59, 3519-3532. (PDBbind 2017 refined set) (75)Wang, J.; Dokholyan, N. V. MedusaDock2.0: efficient and accurate protein-ligand docking with constraints. J. Chem. Inf. Model. 2019, 59, 2509-2515. (PDBbind 2017 refined set) (76)Weng, G.; Wang, E.; Chen, F.; Sun, H.; Wang, Z.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. Phys. Chem. Chem. Phys. 2019, 21, 10135-10145. (PDBbind 2017, peptide) (77)Zhang, H.; Liao, L.; Saravanan, K. M.; Yin, P.; Wei, Y. DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity. PEERJ 2019, 7, e7362. (PDBbind 2018) (78)Wojcikowski, M.; Kukielka, M.; Stepniewska-Dziubinska, M. M.; Siedlecki, P. Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 2019, 35, 1334-1341. (PDBbind 2016) (79)Trisciuzzi, D.; Nicolotti, O.; Miteva, M. A.; Villoutreix, B. O. Analysis of solvent-exposed and buried cocrystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors. Drug Discovery Today 2019, 24, 551-559. (PDBbind 2017) (80)Sunseri, J.; King, J. E.; Francoeur, P. G.; Koes, D. R. Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput.-Aided Mol. Des. 2019, 33, 19-34. (PDBbind 2016 refined set) (81)Nguyen, D. D.; Cang, Z.; Wu, K.; Wang, M.; Cao, Y.; Wei, G. W. Mathematical deep learning for pose and binding affinity prediction and ranking in D3R grand challenges. J. Comput.-Aided Mol. Des. 2019, 33, 71-82. (PDBbind for training set) (82)Macari, G.; Toti, D.; Polticelli, F. Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. J. Comput.-Aided Mol. Des. 2019, 33, 887-903. (83)Preto, J.; Gentile, F.; Assessing and improving the performance of consensus docking strategies using the DockBox package. J. Comput.-Aided Mol. Des. 2019, 33, 817-829. (PDBbind 2013 core set) (84)Jiang, M.; Li, Z.; Bian, Y.; Wei, Z. A novel protein descriptor for the prediction of drug binding sites. BMC Bioinformatics 2019, 20: 478. (PDBbind for test) (85)Devaurs, D.; Antunes, D. A.; Hall-Swan, S.; Mitchell, N.; Moll, M.; Lizee, G.; Kavraki, L. E. Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Bioinformatics 2019, 20: 42. (PDBbind) (86)Torres, P. H. M.; Sodero, A. C. R.; Jofily, P.; Silva, F. P. Key topics in molecular docking for drug design. Int. J. Mol. Sci. 2019, 20, 4574. (review, PDBbind project) (87)Macari, G.; Toti, D.; Del Moro, C.; Polticelli, F. Fragment-based ligand-protein contact statistics: application to docking simulations. Int. J. Mol. Sci. 2019, 20, 2499. (PDBbind 2013 core set) (88)Pei, J.; Zheng, Z.; Merz, K. M. Random forest refinement of the KECSA2 knowledge-based scoring function for protein decoy detection. J. Chem. Inf. Model. 2019, 59, 1919-1929. (PDBbind 2014) (89)Zhu, M.; Song, X.; Chen, P.; Wang, W.; Wang, B. dbHDPLS: a database of human disease-related protein-ligand structures. Comput. Biol. Chem. 2019, 78, 353-358. (90)Dittrich, J.; Schmidt, D.; Pfleger, C.; Gohlker, H. Converging a knowledge-based scoring function: DrugScore2018. J. Chem. Inf. Model. 2019, 59, 509-521. (PDBbind 2016) (91)Wang, X.; Li, Z.; Jiang, M.; Wang, S.; Zhang, S.; Wei, Z. Molecule property prediction based on spatial graph embedding. J. Chem. Inf. Model. 2019, 59, 3817-3828. (92)Cinaroglu, S. S.; Timucin, E. Comparative assessment of seven docking programs on a nonredundant metalloprotein subset of the PDBbind refined. J. Chem. Inf. Model. 2019, 59, 3846-3859. (PDBbind 2017 refined set) (93)Bolcato, G.; Cuzzolin, A.; Bissaro, M.; Moro, S.; Sturlese, M. Can we still trust docking results? An extension of the applicability of DockBench on PDBbind database. Int. J. Mol. Sci. 2019, 20, 3558. 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