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) Gaillard, T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J. Chem. Inf. Model., 2018, 58: 1697-1706.
(2) Cang, Z.;Mu, L.; Wei, G. W. Representability of Algebraic Topology for Biomolecules in Machine Learning Based Scoring and Virtual Screening. PLoS Comput Bio., 2018, 14(1):e1005929.
(3) Jimenez, J.; Skalic, M.; Martinez-Rosell, G.; De Fabritiis, G. K-DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. J. Chem. Inf. Model., 2018, 58: 287-296.
(4) Fu, DY.; Meiler, J. Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review. J. Chem. Inf. Model., 2018, 58: 225-233.
(5) Ashtawy, H.;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.
(6) 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., 2017, 31: 943-958.
(7) 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.
(8) 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 Fingerprinting Analyses. J. Chem. Inf. Model., 2017, 57: 1640?1651.
(9) 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.
(10) Wang, B.; Zhao, Z. X.; Nguyen, D. D.; Wei, G. W. Feature Functional Theory-binding Predictor (FFT-BP) for the Blind Prediction of Binding Free Energies. Theor Chem Acc., 2017, 136: 55-79.
(11)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.
(12)Wang C.; Zhang, Y.K. Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest. J. Comput. Chem., 2017, 38: 169-177.
(13)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.
(14)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, 11, e0158898.
(15)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.
(16)Duan, L.; Liu, X.; Zhang, J. Z. H. Interaction Entropy: A New Paradigm for High Efficient and Reliable Computation of Protein-ligand Binding Free Energy. J. Am. Chem. Soc., 2016, 138: 5722-5728.
(17)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.
(18)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.
(19)Quiroga, R.; Villarreal, M. A. Vinardo:A Scoring Function Based on Autodock Vina Improves Scoring, Docking and Virtual Screening. PLOS One, 2016,11(5): e0155183.
(20)Khamis, M. A.; Gomaa, W. Comparative Assessment of Machine-learning Scoring Functions On PDBbind 2013. Eng. Appl. of Artif. Intell., 2015, 45: 136-151.
(21)Yan, Z.; Wang, J. Optimizing the Affinity and Specificity of Ligand Binding with the Inclusion of Solvation Effect. Proteins-Struct. Funct.Bioinf., 2015, 83: 1632-1642.
(22)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.
(23)Alhossary, A.; Handoko, S. D.; Mu, Y.; Kwoh, C. K. Fast, Accurate, and Reliable Molecular Docking With QuickVina 2. Bioinformatics., 2015, 31: 2214-2216.
(24)Li, H.; Leung, K.; 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. Informatics., 2015, 34: 115-126.
(25)Wang, Y. et al. 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.
(26)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, (S6):S3.
(27)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.
(28)Lindblom, P. R. et al. 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.
(29)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: 291-302.
(30)Hu, B.; Lill, M. A. PharmDock: A Pharmacophore-Based Docking Program. J. Cheminformatics., 2014, 6: 14-27.
(31)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.
(32)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.
(33)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.
(34)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.
(35)Wang, S. H.; Wu, Y. T.; Kuo, S. C.; Yu, J. HotLig: AMolecular Surface-Directed Approach to Scoring Protein-Ligand Interactions. J. Chem. Inf. Model., 2013, 53: 2181-2195.
(36)Li, G.B.; Yang, L.L.; Wang, W. J.; Li, L. L.; Yang, S. Y. ID-Score: ANew Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein-ligand Interactions . J. Chem. Inf. Model., 2013, 53: 592-600.
(37)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.
(38)Yan, Z.; Wang, J. Specificity Quantification of Biomolecular Recognition and Its Implication for Drug Discovery. Sci. Rep., 2012,2, 309.
(39)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.
(40)Hsieh, J. et al. 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.
(41)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.
(42)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.
(43)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.
(44)Plewczynski, D.; Lazniewski, M.; von Grotthuss, M. VoteDock: Consensus Docking Method for Prediction of Protein-Ligand Interactions. J. Chem. Inf. Model., 2011, 51: 568-581.
(45)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.
(46)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.
(47)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.

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