Applications of the CASF benchmark

The CASF Benchmark Package for Download
CASF-2016 Notice: You should register and login before downloading the CASF-2016 package. (size: 1.46GB)

Su M.Y.; Yang Q.F.; Du Y.; Feng G.Q.; Liu Z.H.; Li Y.; Wang R.X.*Comparative Assessment of Scoring Functions: The CASF-2016 Update. J. Chem. Inf. Model., 2019. DOI: 10.1021/acs.jcim.8b00545.
CASF-2013 Notice: You should register and login before downloading the CASF-2013 package. (size: 636MB)

(1) Li, Y.; Liu, Z. H.; Han, L.; Li, J.; Liu, J.; Zhao, Z. X.; Li, C. K.; Wang, R. X.* "Comparative Assessment of Scoring Functions on an Updated Benchmark: I. Compilation of the Test Set", J. Chem. Inf. Model., 2014, doi: 10.1021/ci500080q.

(2) Li, Y.; Han, L.; Liu, Z. H.; Wang, R. X.*, "Comparative Assessment of Scoring Functions on an Updated Benchmark: II. Evaluation Methods and General Results", J. Chem. Inf. Model., 2014, doi: 10.1021/ci500081m.
CASF-2007 Notice: You should register and login before downloading the CASF-2007 package. (size: 92MB)

Cheng T.J.; Li X.; Li Y.; Liu Z.H.; Wang R.X."Comparative assessment of scoring functions on a diverse test set", J. Chem. Inf. Model., 2009, 49(4):1079-1093.
Selected applications of the CASF benchmark published by other researchers
(1) Zhang, X. J.; Shen, C.; Guo, X. Y.; Wang, Z.; Weng, G. Q.; Ye, Q.; Wang, G. A.; He, Q. J.; Yang, B.; Cao, D. S.; Hou, T. ASFP(Artifical Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions. J. Cheminform. 2021, 13:6. (CASF-2016, evaluation)
(2) Wong, K. M.; Tai, H. K.; Siu, S. W. I. GWOVina: a grey wolf optimization approach to rigid and flexible receptor docking. Chem. Biol. Drug Des. 2021, 97, 97-110. (CASF-2013, docking power evaluation)
(3) Bao, J. X.; He, X.; Zhang, J. Z. H. Development of a new scoring function for virtual screening: APBScore. J. Chem. Inf. Model. 2020, 60, 6355-6365. (CASF-2016 core set, evaluation)
(4) 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. (CASF-2016 core set)
(5) Acharya, A. et al Supercomputer-based ensemble docking drug discovery pipeline with application to Covid-19. J. Chem. Inf. Model. 2020, 60, 5832-5852. (conclusion, RF scoring function)
(6) Wang, E.; Liu, H.; Wang, J.; Weng, G.; Sun, H.; Wang, Z.; Kang, Y.; Hou, T. Development and evaluation of MM/GBSA based on a variable dielectric GB model for predicting protein-ligand binding affinities. J. Chem. Inf. Model. 2020, 60, 5353-5365. (use CASF-2013 core set for training and test)
(7) Mirza, M. U.; Ahmad, S.; Abdullah, I.; Froeyen, M. Identification of novel human USP2 inhibitor and its putative role in treatment of COVID-19 by inhibiting SARS-CoV-2 papain-like (PLpro) protease. Comput. Biol. Chem. 2020, 89:107376. (conclusion, AutoDock Vina)
(8) 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, 8424. (CASF-2016, evaluation)
(9) 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, 9548. (CASF-2013, CASF-2016, evaluation)
(10) Liu, H.; Deng, J. P.; Luo, Z.; Lin, Y. W.; Merz, K. M.; Zheng, Z. Receptor-Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method. J. Chem. Theory Comput. 2020, 16, 6645-6655. (CASF-2016, evaluation)
(11) Zheng Z.; Borbulevych, O. Y.; Liu, H.; Deng, J. P.; 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. (CASF-2016, evaluation)
(12) 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. (CASF-2016 core set, evaluation)
(13) Morrone, J. A.; Weber, J. K.; Huynh, T.; Luo. H.; Cornell, W. D. Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach. J. Chem. Inf. Model. 2020, 60, 4170-4179. (CASF-2013, binding mode prediction)
(14) Wei, L.; Wen, W.; Rao, L.; Huang, Y.; Lei, M.; Liu, K.; Hu, S.; Song, R.; Ren, Y. Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation. J. Chem. Inf. Model. 2020, 60, 4388-4402.(conclusion, X-Score)
(15) Smith, S. T.; Meiler, J. Assessing multiple score functions in Rosetta for drug discovery. PLOS One 2020, 15:e0240450. (CASF-2016, evaluation)
(16) Imrie, F.; Bradley, A. R.; van der Schaar, M.; Deane, C. M. Deep generative models for 3D linker design. J. Chem. Inf. Model. 2020, 60, 1983-1995. (use CASF-2016 core set for test)
(17) 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. (use CASF-2016 core set for test)
(18) 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. (CASF-2016)
(19) 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. (CASF-2016 core set, evaluation)
(20) 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. (CASF-2016, docking power)
(21) 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. (CASF-2016, scoring power)
(22) 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. (CASF-2007, CASF-2013, CASF-2016)
(23) 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. (CASF-2007, CASF-2013, CASF-2016)
(24) 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 Design 2020, 96, 973-983. (CASF-2016, evaluation)
(25) Ropon-Palacios, G.; Chenet-Zuta, M. E.; Olivos-Ramirez, G. E.; Otazu, K.; Acurio-Saavedra, J.; Camps, I. Potential novel inhibitors against emerging zoonotic pathogen Nipah Virus: a virtual screening and molecular dynamics approach. J. Biomol. Struct. Dynamics 2020, 38, 3225-3234. (conclusion, X-Score)
(26) Li, C.; Sun, J.; Palade, V. Diversity-guided Lamarckian random drift particle swarm optimization for flexible ligand docking. BMC Bioinformatics 2020, 21:286. (CASF-2016, evaluation)
(27) Yang, Y.; Zheng, S.; Su, S.; Zhao, C.; Xu, J.; Chen, H. SyntaLinker: automatic fragment linking with deep conditional transformer neural networks. Chem. Sci. 2020, 11, 8312-8322. (use CASF-2016 for test set)
(28) 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. (CASF-2013 core set)
(29) 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 (CASF-2013 for test in drug repurposing).
(30) 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. (CASF-2016, evaluation)
(31) Li, H.; Peng, J.; Sidorov, P.; Leung, Y.; Leung, K.-S. Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics 2019, 35, 3989-3995. (CASF-2007)
(32) Kang, N.; Wang, X. L.; Zhao, Y. Discovery of small molecule agonists targeting neuropeptide Y4 receptor using homology modeling and virtual screening. Chem. Biol. Drug Des. 2019, 94, 2064-2072. (conclusion, scoring function GlideScore)
(33) Ropon-Palacios, G.; Chenet-Zuta, M. E.; Olivos-Ramirez, G. E.; Otazu, K.; Acurio-Saavedra, J.; Camps, I. Potential novel inhibitors against emerging zoonotic pathogen Nipah Virus: a virtual screening and molecular dynamics approach. J. Biomol. Struct. Dynamics 2019, DOI: 10.1080/07391102.2019.1655480. (conclusion, docking decoys)
(34) Kairys, V.; Baranauskiene, L.; Kazlauskiene, M.; Matulis, D.; Kazlaukas, E. Binding affinity in drug design: experimental and computational techniques. Expr. Opin. Drug Discov. 2019, 14, 755-768. (review, citation of conlcusion)
(35) Nguyen, D. D.; Wei, G. W. AGL-Score: algebraic graph learning score for protein-ligand binding scoring, ranking, docking and screening. J. Chem. Inf. Model. 2019, 59, 3291-3304. (CASF-2013, data set, method)
(36) 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. (CASF-2013 core set, evaluation method)
(37) 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 (CASF-2013)
(38) Bresso, E.; Fernandez, D.; Amora, D. X.; Noel, P.; Petitot, A.-S.; de Sa, M. E. L.; Albuquerque, E. V. S.; Danchin, E. G. J.; Maigret, B.; Martins, N. F. A chemosensory GPCR as a potential target to control the root-knot nematode meloidoyne incognita parasitism in plants. Molecules 2019, 24, 3798. (conclusion, scoring function ChemPLP)
(39) 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. (CASF-2013 core set)
(40) 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. (CASF-2013 benchmark)
(41) Pei, J.; Zheng, Z.; Kim, H.; Song, L. F.; Walworth, S.; Merz, M. R.; Merz, K. M. Random forest refinement of pairwise potentials for protein-ligand decoy detection. J. Chem. Inf. Model. 2019, 59, 3305-3315. (CASF-2013 core set)
(42) 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. (CASF-2013 core set)
(43) Dittrich, J.; Schmidt, D.; Pfleger, C.; Gohlker, H. Converging a knowledge-based scoring function: DrugScore2018. J. Chem. Inf. Model. 2019, 59, 509-521. (CASF-2013 benchmark)
(44) Khan, R.; Zeb, A.; Choi, K.; Lee, G.; Lee, K. W.; Lee, S.-W. Biochemical and structural insights concerning triclosan resistance in a novel YX7K type enoyl-acyl carrier protein reductase from soil metagenome. Sci. Rep. 2019, 9, 15401. (conclusion, scoring function ChemPLP)
(45) 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. (CASF-2013 core set and conclusion)
(46) Zeb, A.; Kim, D.; Alam, S. I.; Son, M.; Kumar, R.; Rampogu, S.; Parameswaran, S.; Shelake, R. M.; Rana, R. M.; Parate, S.; Kim, J. Y.; Lee, K. W. Computational simulations identify pyrrolidine-2,3-dione derivatives as novel inhibitors of Cdk5/p25 complex to attenuate Alzheimer’s pathology. J. Clin. Med. 2019, 8, 746. (conclusion, scoring function: GoldScore, ASP)
(47) Nguyen, D. D.; Wei, G. W. DG-GL: differential geometry-based geometric learning of molecular datasets. Int. J. Numer. Method Biomed. Eng. 2019, 35, e3179. (CASF-2013 core set)
(48) Rehman, S. U.; Ali, T.; Alam, S. I.; Ullah, R.; Zeb, A.; Lee, K. W.; Rutten, B. P. F.; Kim, M. O. Ferulic acid rescues LPS-induced neurotoxicity via modulation of the TLR4 receptor in the mouse hippocampus. Mol. Neurobiol. 2019, 56, 2774-2790. (conclusion, scoring function: ChemPLP, ASP)
(49) 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. (CASF-2013 core set)
(50) Zeb, A.; Park, C.; Rampogu, S.; Son, M.; Lee, G.; Lee, K. W. Structure-based drug designing recommends HDAC6 inhibitors to attenuate microtubule-associated tau-pathogenesis. ACS Chem. Neurosci. 2019, 10, 1326. (conclusion, scoring function: ChemPLP, ASP)
(53) Wang, Z. H.; Liang, H.; Cao, H. J.; Zhang, B. J.; Li, J.; Wang, W. Q.; Qin, S. S.; Wang, Y. F.; Xuan, L. J.; Lai, L. H.; Shui, W. Q. Efficient ligand discovery from natural herbs by integrating virtual screening, affinity mass spectrometry and targeted metabolomics. Analyst 2019, 144, 2881-2890. (conclusion, scoring function: GlidScore)
(54) Bojarova, P.; Kulik, N.; Hovorkova, M.; Slamova, K.; Pelantova, H.; Kren, V. The beta-N-acetylhexosaminidase in the synthesis of bioactive glycans: protein and reaction engineering. Molecules 2019, 234, 599. (conclusion, scoring function: GlideScore)
(51) 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. (CASF-2013 core set for test)
(52) Margiotta, E.; Deganutti, G.; Moro, S. Could the presence of sodium ion influence the accuracy and precision of the ligand-posing in the human A2A adenosine receptor orthosteric binding site using a molecular docking approach? Insights from Dockbench. J. Comput.-Aided Mol. Des. 2018, 32, 1337-1346. (conclusion, scoring function: ASP)
(55) Araki, M.; Iwata, H.; Ma, B.; Fujita, A.; Terayama, K.; Sagae, Y.; Ono, F.; Tsuda, K.; Kamiya, N.; Okuno, Y. Improving the accuracy of protein-ligand binding mode prediction using a molecular dynamics-based pocket generation approach. J. Comput. Chem. 2018, 39, 2679-2689. (conclusion, scoring function: ASP)
(56) Tai, H. K.; Jusoh, S. A.; Siu, S. W. I. Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening. J. Cheminform. 2018, 10:62 (CASF-2013 core set)
(57) 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 CASF project)
(58) 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. (CASF-2013 core set)
(59) Gaillard, T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J. Chem. Inf. Model. 2018, 58, 1697-1706. (core set and evaluation)
(60) Jedwabny, W.; Lodola, A.; Dyguda-Kazimierowicz, E. Theoretical Model of EphA2-Ephrin A1 Inhibition. Molecules 2018, 23, DOI: 10.3390/molecules23071688. (citation of conclusion)
(61) Jimenez, J.; Skalic, M.; Martinez-Rosell, G.; De Fabritiis, G. KDEEP: Protein−Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. J. Chem. Inf. Model. 2018, 58, 287-296. (SF model training and test on core set)
(62) Guedes, I. A.; Pereira, F. S. S.; Dardenne, L. E. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front. Pharmacol. 2018, 9:1089. (review, training/test set)
(63) Pantsar, T.; Poso, A. Binding Affinity via Docking: Fact and Fiction. Molecules 2018, 23, 8:1899. (citation of opinion)
(64) Okada-Junior, C. Y.; Monteiro, G. C.; Aguiar, A. C. C.; Batista, V. S.; de Sonza, J. O.; Souza, G. E.; Bueno, R. V.; Oliva, G.; Nascimento-Junior, N. M.; Guido, R. V. C.; Bolzani, V. S. Phthalimide Derivatives with Bioactivity against Plasmodium falciparum: Synthesis, Evaluation, and Computational Studies Involving bc1 Cytochrome Inhibition. ACS Omega 2018, 3, 9424-9430. (Conclusion, scoring function ChemPLP)
(65) Zheng, M.; Zhao, J.; Cui, C.; Fu, Z.; Li, X.; Liu, X.; Ding, X.; Tan, X.; Li, F.; Luo, X.; Chen, K.; Jiang, H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med. Res. Rev. 2018, 38, 914-950. (review citation)
(66) Ban T.; Ohue, M.; Akiyama, Y. Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem. Comput. Biol. Chem. 2018, 73, 139-146. (citation of conclusion, GlideScore)
(67) Jasper, J. B.; Humbeck, L.; Brinkjost, T.; Koch, O. A novel interaction fingerprint derived from per atom score contributions: exhaustive evaluation of interaction fingerprint performance in docking based virtual screening. J. Cheminform. 2018, 10: 15. (citation of conclusion, ChemPLP)
(68) Li, D.-D.; Meng, X.-F.; Wang, Q.; Yu, P.; Zhao, L.-G.; Zhang, Z.-P.; Wang, Z.-Z.; Xiao, W. Consensus scoring model for the molecular docking study of mTOR kinase inhibitor. J. Mol. Graph. Model. 2018, 79, 81-87. (citation of conclusion, docking power)
(69) Kumar, S. P. PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions. J. Mol. Graph. Model. 2018, 79, 194-212. (citation of conclusion, docking power)
(70) Lin, H.; Siu, S. W. I. A Hybrid Cuckoo Search and Differential Evolution Approach to Protein–Ligand Docking. Int. J. Mol. Sci. 2018, 19, 3181. (PDBbind v2012)
(71) Cang, Z.; We, G.-W. Integration of element specific persistent homology and machine learning for protein‐ligand binding affinity prediction. Int. J. Numeric. Methods Biomed. Engine. 2018, 34, e2914. (PDBbind v2007, training and test)
(72) Cang, Z.; Mu, L.; Wei, G.-W. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput. Biol. 2018, 14, e1005929. (PDBbind core set)
(73) Ashtawy, H. M.; Mahapatra, N. R. Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment. J. Chem. Inf. Model. 2018, 58, 119-133. (SF development and validation)
(74) Ashtawy, H. M.; Mahapatra, N. R. Descriptor Data Bank (DDB): a cloud platform for multiperspective modeling of protein-ligand interactions. J. Chem. Inf. Model. 2018, 58, 134-147. (core set)
(75) Zafar, A.; Sari, S.; Leung, E.; Pilkington, L. I.; van Rensburg, M.; Barker, D.; Reynisson, J. GPCR Modulation of Thieno[2,3-b]pyridine Anti-Proliferative Agents. Molecules. 2017, 22, 2254. (citation of conclusion)
(76) Suslov, E.; Zarubaev, V. V.; Slita, A. V.; Ponomarev, K.; Korchagina, D.; Ayine-Tora, D. M.; Reynisson, J.; Volcho, K.; Salakhutdinov, N. Anti-influenza activity of diazaadamantanes combined with monoterpene moieties. Bioorg. Med. Chem. Lett. 2017, 27, 4531-4535. (citation of conclusion)
(77) Kadukova, M.; Grudinin, S. Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization. J. Comput.-Aided Mol. Des. 2017, 31, 943-958. (SF development and validation)
(78) Cheron, N.; Shakhnovich, E. I. Effects of sampling on BACE-1 ligands binding free energy predictions via MM-PBSA calculations. J. Comput. Chem. 2017, 38, 1941-1951. (data set)
(79) Yan, Z.; Wang, J. SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity. Nucleic Acids Res. 2017, 45, e110. (citation of conclusion)
(80) Pedregal, J. R.; Sciortino, G.; Guasp, J.; Municoy, M.; Marechal, J. D. GaudiMM: a modular multi-objective platform for molecular modeling. J. Comput. Chem. 2017, 38, 2118-2126. (citation of conclusion)
(81) Baek, M.; Shin, W.-H.; Chung, H. W.; Seok, C. GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking. J. Comput.-Aided Mol. Des. 2017, 31, 653-666. (new SF development and validation).
(82) Li, G. B.; Yu, Z. J.; Liu, S.; Huang, L. Y.; Yang, L. L.; Lohans, C. T.; Yang, S. Y. IFPTarget: a customized virtual target identification method based on protein-ligand interaction fingerprint analyses. J. Chem. Inf. Model. 2017, 57, 1640-1651. (validation)
(83) Nguyen, D. D.; Xiao, T.; Wang, M.; Wei, G. W. Rigidity strengthening: a mechanism for protein-ligand binding. J. Chem. Inf. Model. 2017, 57, 1715-1721. (validation)
(84) Wang, B.; Zhao, Z.; Nguyen, D. D.; Wei, G. W. Feature functional theory-binding predictor (FFT-BP) for the blind prediction of binding free energies. Theoret. Chem. Acc. 2017, 136, 55. (validation)
(85) Li, Y.; Yang. J. Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein-ligand interactions. J. Chem. Inf. Model. 2017, 57, 1007-1012. (data set)
(86) Yu, Z.; Li, P.; Jr., K. M. M. Using ligand-induced protein chemical shift perturbations to determine protein-ligand structures. Biochemistry 2017, 56, 2349-2362. (data set)
(87) Sundriyal, S.; Moniot, S.; Mahmud, Z.; Yao, S.; Di Fruscia, P.; Reynolds, C. R.; Dexter, D. T.; Sternberg, M. J. E.; Lam, E. W. F.; Steegborn, C.; Fuchter, M. J. Thienopyrimidinone based sirtuin-2 (SIRT-2)-selective inhibitors bind in the ligand induced selectivity pocket. J. Med. Chem. 2017, 60, 1928-1945. (citation of conclusion)
(88) Wang, Y.; Li, L.; Zhang, B.; Xing, J.; Chen, S.; Wan, W.; Song, Y.; Jiang, H.; Jiang, H.; Luo, C.; Zheng, M. Discovery of novel disruptor of silencing telomeric 1-like (DOT1L) inhibitors using a target-specific scoring function for the (S)-adenosyl-L-methionine (SAM)-dependent methyltransferase family. J. Med. Chem. 2017, 60, 2026-2036. (citation of conclusion)
(89) Debroise, T.; Shakhnovich, E. I.; Cheron, N. A hybrid knowledge-based and empirical scoring function for protein-ligand interaction: SMoG2016. J. Chem. Inf. Model. 2017, 57, 584-593. (validation of new scoring function)
(90) Wang, C.; Zhang, Y.; Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem. 2017, 38, 169-177. (new scoring function development and validation)
(91) Ciordia, M.; Perez-Benito, L.; Delgado, F.; Trabanco, A. A.; Tresadern, G. Application of free energy perturbation for the design of BACE1 inhibitors. J. Chem. Inf. Model. 2016, 56, 1856-1871. (citation of conclusion)
(92) Bjerrum, E. J. Machine learning optimization of cross docking accuracy. Comput. Biol. Chem. 2016, 62, 133-144. (citation of conclusion)
(93) Pires, D. E. V.; Ascher, D. B. CSM-lig: a web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Res. 2016, 44, W557-W561. (data set)
(94) Quiroga, R.; Villarreal, M. A. Vinardo: a scoring function based on Autodock Vina improves scoring, docking and virtual screening. PLoS ONE, 2016, e0155183 (new scoring function development and validation)
(95) Liu, X.; Liu, J.; Zhu, T.; Zhang, L.; He, X.; Zhang, J. Z. H. PBSA_E: a PBSA-based free energy estimator for protein-ligand binding affinity. J. Chem. Inf. Model. 2016, 56, 854-861. (validation)
(96) 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. (new scoring function validation)
(97) Yan, Z.; 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. (validation)
(98) Hauser, A. S.; Windshugel, B. LEADS-PEP: a benchmark data set for assessment of peptide docking performance. J. Chem. Inf. Model. 2016, 56, 188-200. (citation of conclusion)
(99) Shin, W. H.; Christoffer, C. W.; Wang, J.; Kihara, D. PL-PatchSurfer2: improved local surface matching-based virtual screening method that is tolerant to target and ligand structure variation. J. Chem. Inf. Model. 2016, 56, 1676-1691. (data set)
(100) Fang, Y.; Ding, Y.; Feinstein, W. P.; Koppelman, D. M.; Moreno, J.; Jarrell, M.; Ramanujam, J.; Brylinski, M. GeauxDock: accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE 2016, e0158898. (validation)
(101) Xu, D.; Meroueh, S. O. Effect of binding pose and modeled structures on SVMGen and GlideScore enrichment of chemical libraries. J. Chem. Inf. Model. 2016, 56, 1139-1151. (data set)
(102) Chen, A. S. Y.; Westwood, N. J.; Brear, P.; Rogers, G. W.; Mavridis, L.; Mitchell, J. B. O. Mol. Inform. 2016, 35, 125-135. (data set)
(103) Ain, Q. U.; Aleksandrova, A.; Roessler, F. D.; Ballester, P. J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. WIREs Comput. Mol. Sci. 2015, 5, 405-424.
(104) Khamis, M. A.; Gomaa, W. Comparative assessment of machine-learning scoring functions on PDBbind 2013. Eng. Appl. Art. Intell. 2015, 45, 136-151. (validation)
(105) Alhossary, A.; Handoko, S. D.; Mu, Y.; Kwoh, C. K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 2015, 31, 2214-2216. (data set)
(106) Li, H.; 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. (validation)
(107) 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. (validation)
(108) Yan, Z.; Wang, J. Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect. Proteins 2015, 83, 1632-1642. (validation)
(109) Houston, D. R.; Yen, L.; Pettit, S.; Walkinshaw, M. D. Structure- and ligand-based virtual screening identifies new scaffolds for inhibitors of the oncoprotein MDM2. PLoS ONE 2015, e0121424. (citation of conclusion)
(110) Khamis, M. A.; Gomaa, W.; Ahmed, W. F. Machine learning in computational docking. Art. Intell. Med. 2015, 63, 135-152. (review citation).
(111) Danishuddin, M.; Khan, A. U. Structure based virtual screening to discover putative drug candidates: necessary considerations and successful case studies. Methods 2015, 71, 135-145. (review citation)
(112) 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:S3. (validation)
(113) 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. (citation of conclusion)
(114) Wang, Y.; Guo, Y.; Kiang, 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.
(115) Li, H.; Leung, K. S.; Wong, M. H.; Ballester, P. J. Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol. Inform. 2015, 34, 115-126. (validation)
(116) Zheng, Z.; Wang, T.; Li, P.; Jr., K. M. M. KECSA-movable type implicit solvation model (KMTISM). J. Chem. Theory Comput. 2015, 11, 667-682. (data set)
(117) Yi, X.; Zhang, Y.; Wang, P.; Qi, J.; Hu, M.; Zhong, G. Ligands binding and molecular simulation: the potential investigation of a biosensor based on an insert odorant binding protein. Int. J. Biol. Sci. 2015, 11, 75-87.
(118) Yan, C.; Zou, X. Predicting peptide binding sites on protein surfaces by clustering chemical interactions. J. Comput. Chem. 2015, 36, 49-61.
(119) 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.
(120) 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.
(121) 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. J. Mol. Graph. Model. 2014, 53, 118-127.
(122) Li, H.; 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, 15:291.
(123) Sun, H.; 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.
(124) Campbell, A. J.; Lamb, M. L.; Joseph-McCarthy, D. Ensemble-based docking using biased molecular dynamics. J. Chem. Inf. Model. 2014, 54, 2127-2138.
(125) Hu, B.; Lill, M. A. PharmDock: a pharmacophore-based docking program. J. Cheminformatics 2014, 6:14.
(126) Gupta, S. D.; Snigdha, D.; Mazaira, G. I.; Galigniana, M. D.; Subrahmanyam, C. V. S.; Gowrishankar, N. L.; Raghavendra, N. M. Molecular docking study, synthesis and biological evaluation of Schiff bases as Hsp90 inhibitors. Biomed. Pharma. 2014, 68, 369-376.
(127) Chen, Y.-F.; Shiau, A.-L.; Wang, S.-H.; Yang, J.-S.; Chang, S.-J.; Wu, C.-L.; Wu, T.-S. Zhankuic Acid A isolated from Taiwanofungus camphorates is a novel selective TLR4/MD-2 antagonist with anti-inflammatory properties. J. Immunology 2014, 192, 62778-62786.
(128) 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.
(129) Li, H.; Leung, K.-S.; Ballester, P. J.; Wong, M.-H. istar: a web platform for large-scale protein-ligand docking. PLOS One 2014, 9, e85678
(130) Sun, X.-Q.; Chen, L.; Lo, Y.-Z.; Li, W.-H.; Liu, G.-X.; Tu, Y.-Q.; Tang, Y. Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors. Acta Pharm. Sinica 2014, 35, 301-310.
(131) Shin, W.-H.; Kim, J.-W.; Kim, D.-S.; Seok, C. GalaxyDock2: Protein-Ligand Docking Using Beta-Complex and Global Optimization. J. Comput. Chem. 2013, 34, 2647-2656.
(132) Liu, Q.; Kwoh, C. K.; Li, J. Binding Affinity Prediction for Protein-Ligand Complexes Based on β Contacts and B Factor. J. Chem. Inf. Model. 2013, 53, 3076-3085.
(133) 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.
(134) Zilian, D.; Sotriffer, C. A. SFCscoreRF: A random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J. Chem. Inf. Model. 2013, 53, 1923-1933.
(135) Wang, S.-H.; Wu, Y.-T.; Kuo, S.-C.; Yu, J. HotLig: A molecular surface-directed approach to scoring protein-ligand interactions. J. Chem. Inf. Model. 2013, 53, 2181-2195.
(136) 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.
(137) 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.
(138) Yan, Z.; Wang, J. Specificity quantification of biomolecular recognition and its implication for drug discovery. SCI. Rep. 2012, 2, 309.
(139) Korb, O.; Ten Brink, T.; Victor Paul Raj, F. R.; Keil, M.; Exner, T. E. Are predefined decoy sets of ligand poses able to quantify scoring function accuracy? J. Comput.-Aided. Mol. Des. 2012, 26, 185-197.
(140) Hsieh, J.; Yin, S.; Wang, X. S.; Liu, S.; Dokholyan, N. V.; Tropsha, A. Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge-Based Pose Scoring and Physical Force Field-Based Hit Scoring Functions Improves the Accuracy of Structure-Based Virtual Screening. J. Chem. Inf. Model. 2012, 52, 16-28.
(141) Wang, J.-C.; Lin, J.-H.; Chen, C.-M.; Perryman, A. L.; Olson, A. J. Robust Scoring Functions for Protein-Ligand Interactions with Quantum Chemical Charge Models. J. Chem. Inf. Model. 2011, 51, 2528-2537.
(142) Neudert, G.; Klebe, G. DSX: A Knowledge-Based Scoring Function for the Assessment of Protein-Ligand Complexes. J. Chem. Inf. Model. 2011, 51, 2731-2745.
(143) Hsieh, J.-H.; Yin, S.; Liu, S.; Sedykh, A.; Dokholyan, N. V.; Tropsha, A. Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets. J. Chem. Inf. Model. 2011, 51, 2027-2035.
(144) Osolodkin, D. I.; Palyulin, V. A.; Zefirov, N. S. Structure-Based Virtual Screening of Glycogen Synthase Kinase 3 beta Inhibitors: Analysis of Scoring Functions Applied to Large True Actives and Decoy Sets. Chem. Biol. Drug Des. 2011, 78, 378-390.
(145) Ballester, P. J.; Mitchell, J. B. Comments on "Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets": Significance for the Validation of Scoring Functions. J. Chem. Inf. Model. 2011, 51, 1739-1741.
(146) Spitzmueller, A.; Velec, H. F. G.; Klebe, G. MiniMuDS: A New Optimizer using Knowledge-Based Potentials Improves Scoring of Docking Solutions. J. Chem. Inf. Model. 2011, 51, 1423-1430.
(147) Kramer, C.; Gedeck, P. Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors. J. Chem. Inf. Model. 2011, 51, 707-720.
(148) Plewczynski, D.; Lazniewski, M.; von Grotthuss, M. VoteDock: Consensus Docking Method for Prediction of Protein-Ligand Interactions, J. Chem. Inf. Model. 2011, 32, 568-581.
(149) Plewczynski, D.; Lazniewski, M.; Augustyniak, R.; Ginalski, K. Can We Trust Docking Results? Evaluation of Seven Commonly Used Programs on PDBbind Database. J. Comput. Chem. 2011, 32, 742-755.
(150) Tang, Y. T.; Marshall, G. R. PHOENIX: A Scoring Function for Affinity Prediction Derived Using High-Resolution Crystal Structures and Calorimetry Measurements. J. Chem. Inf. Model. 2011, 51, 214-228.
(151) Shen, Q.; Xiong, B.; Zheng, M.; Luo, X.; Luo, C.; Liu, X.; Du, Y.; Li, J.; Zhu, W.; Shen, J.; Jiang, H. Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched? J. Chem. Inf. Model. 2011, 51, 386-397.
(152) Kramer, C.; Gedeck, P. Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets. J. Chem. Inf. Model. 2010, 50, 1961-1969.
(153) Sandor, M.; Kiss, R.; Keseru, G. M. Virtual Fragment Docking by Glide: a Validation Study on 190 Protein-Fragment Complexes. J. Chem. Inf. Model. 2010, 50, 1165-1172.
(154) Pencheva, T.; Soumana, O. S.; Pajeva, I.; Miteva, M. A. Post-docking virtual screening of diverse binding pockets: Comparative study using DOCK, AMMOS, X-Score and FRED scoring functions. Eur. J. Med. Chem. 2010, 45, 2622-2628.
(155) Ballester, P. J.; Mitchell, J. B. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 2010, 26, 1169-1175.
(156) Das, S. Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures. J. Chem. Inf. Model. 2010, 50, 298-308.

This site has been visited times since Nov 2007.

Copyright ©2007-2021    上海盈赛思信息科技有限公司    网站备案号:沪ICP备2021015625号-3    沪公网安备:正在申请中

Technical Support(技术支持):