Application of the PDBbind-CN Database

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. (PDBbind refined set)
(94)Ozawa, S.-I.; Takahashi, M.; Yamaotsu, N.; Hirono, S. Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping. J. Mol. Graph. Model. 2019, 89, 102-108. (PDBbind 2013 core set)
(95)Ropp, P. J.; Spiegel, J. O.; Walker, J. L.; Green, H.; Morales, G. A.; Milliken, K. A.; Ringe, J. J.; Durrant, J. D. Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening. J. Cheminform. 2019, 11, 34. (PDBbind refined set)
(96)Chen, D. L.; Li, Y. B.; Guo, W.; Li, Y. D.; Savidge, T.; Li, X.; Fan, X. The shielding effect of metal complexes on the binding affinities of ligands to metalloproteins. Phys. Chem. Chem. Phys. 2019, 21, 205-216.
(97)Da, C.; Zhang, D.; Stashko, M.; Vasileiadi, E.; Parker, R. E.; Minson, K. A.; Huey, M. G.; Huelse, J. M.; Hunter, D.; Gilbert, T. S. K.; Norris-Drouin, J.; Miley, M.; Herring, L. E.; Graves, L. M.; DeRyckere, D.; Earp, H. S.; Graham, D. K.; Frye, S. V.; Wang, X. Data-driven construction of antitumor agents with controlled polypharmacology. J. Am. Chem. Soc. 2019, 141, 15700-15709. (for cross search)
(98)Zhang, H.; Liao, L.; Cai, Y.; Hu, Y.; Wang, H. IVS2vec: a tool of inverse virtual screening based on word2vec and deep learning techniques. Methods 2019, 166, 57-65. (PDBbind 2017)
(99)da Silva, A. D.; Bitencourt-Ferreira, G.; de Azevedo, W. F. Taba: a tool to analyze the binding affinity. J. Comput. Chem. 2019, 41, 69-73. (binding data)
(100) Chen, P.; Ke, Y.; Lu, Y.; Du, Y.; Li, J.; Yan, H.; Zhao, H.; Zhou, Y.; Yang, Y. DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. J. Cheminform. 2019, 11: 52. (PDBbind 2016 refined set)
(101) Han, M.; Sun, D.; Rational creation and systematic analysis of cervical cancer kinase-inhibitor binding profile. J. Comput.-Aided Mol. Des. 2019, 33, 689-698. (binding data for Kinase inhibitors)
(102) Gilberg, E.; Bajorath, J. Recent progress in structure-based evaluation of compound promiscuity. ACS Omega 2019, 4, 2758-2765. (binding data)
(103) Gilberg, E.; Gutschow, M.; Bajorath, J. Promiscuous ligands from experimentally determined structures, binding conformations, and protein family-dependent interaction hotspots. ACS Omega 2019, 4, 1729-1737. (binding data)
2018 
(104) Miljkovic, F.; Bajorath, J. Computational analysis of kinase inhibitors identifies promiscuity cliffs across the human kinome. ACS Omega 2018, 3, 17295-17308. (for kinase complexes)
(105) Feinberg, E. N.; Sur, D.; Wu, Z.; Husic, B. E.; Mai, H.; Li, Y.; Sun, S.; Yang, J.; Ramsundar, B.; Pande, V. S. PotentialNet for molecular property prediction. ACS Cent. Sci. 2018, 4, 1520-1530. (PDBbind 2007)
(106) Stepniewska-Dziubinska, M. M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 2018, 34, 3666-3674. (PDBbind 2016)
(107) Chen, F.; Sun, H.; Wang, J.; Zhu, F.; Liu, H.; Wang, Z.; Lei, T.; Li, Y.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA 2018, 24, 1183-1194. (PDBbind for RNA complex)
(108) Han, K.; Zhang, L.; Wang, M.; Zhang, R.; Wang, C.; Zhang, C. Prediction methods of Herbal compounds in Chinese medicinal herbs. Molecules 2018, 23, 2303. (review, citation of PDBbind project)
(109) Perez, A.; Martinez-Rosell G.; De Fabritiis, G. Simulations meet machine learning in structural biology. Curr. Opin. Struct. Biol. 2018, 49, 139-144. (PDBbind)
(110) Zheng, Z.; Pei, J.; Bansal, N.; Liu, H.; Song, L. F.; Merz, K. M. Generation of pairwise potentials using multidimensional data mining. J. Chem. Theory Comput. 2018, 14, 5045-5067. (PDBbind 2015)
(111) Yamaotsu, N.; Hirono, S. In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery. J. Comput.-Aided Mol. Des. 2018, 32, 1229-1245.
(112) Zhao, R.; Cang, Z.; Tong, Y.; Wei, G. Protein pocket detection via convex hull surface evolution and associated Reeb graph. Bioinformatics 2018, 34, i830-i837. (PDBbind refined set 2007, 2013, 2015, 2016)
(113) de Avila, M. B.; de Azevedo, W. F. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem. Biol. Drug Des. 2018, 92, 1468-1474. (binding data)
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