Applications of the CASF benchmark

CASF benchmark: Comparative Assessment of Scoring Functions
1. Notice: You should register and login before downloading the CASF-2013 data package. (size: 636MB, updated on 2016-11-08)

CASF-2013 references:
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.

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.
2. Notice: You should register and login before downloading the CASF-2007 data package. (size: 92MB)

CASF-2007 references:
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 Citations of Our CASF benchmark paper
(1) Fang, Y. et al. GeauxDock: accelerating structure-based virtual screening with heterogeneous computing, PLOS One 11, e0158898 (2016).
(2) Pires, D. E. V. & Ascher, D. B. CSM-Lig: a web server for assessing and comparing protein-small molecule affinities, Nucleic Acids Res. 44, W557-W561 (2016).
(3) 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. 138, 5722-5728 (2016).
(4) Liu, X. et al. PBSA_E: a PBSA-based free energy estimator for protein-ligand binding affinity, J. Chem. Inf. Model. 56, 854-861 (2016).
(5) Yan, Z. & Wang, J. Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks, J. Comput.-Aided Mol. Des. 30, 219-227 (2016).
(6) Quiroga, R. & Villarreal, M. A. Vinardo: a scoring function based on Autodock Vina improves scoring, docking and virtual screening, PLOS One 11, e0155183 (2016).
(7) Khamis, M. A. & Gomaa, W. Comparative assessment of machine-learning scoring functions on PDBbind 2013, Eng. Appl. of Artif. Intell. 45, 136-151 (2015).
(8) Yan, Z. & Wang, J. Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect, Proteins-Struct. Funct.Bioinf. 83, 1632-1642 (2015).
(9) 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 20, 10947-10962 (2015).
(10) Alhossary, A.; Handoko, S. D.; Mu, Y.; Kwoh, C.-K. Fast, accurate, and reliable molecular docking with QuickVina 2, Bioinformatics, 31, 2214-2216 (2015).
(11) 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, 34, 115-126 (2015).
(12) 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. 29, 349-360 (2015).
(13) Ashtawy, H. M.; Mahapatra, N. R. Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins, BMC Bioinformatics 16, (S6):S3 (2015)
(14) Gabel, J.; Desaphy, J.; Rognan, D. Beware of machine learning-based scoring functions-on the danger of developing black boxes. J. Chem. Inf. Model. 54, 2807-2815 (2014).
(15) 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. 53, 118-127 (2014).
(16) 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, 15, 291 (2014).
(17) Hu, B.; Lill, M. A. PharmDock: a pharmacophore-based docking program. J. Cheminformatics. 6, 14-27 (2014).
(18) Li, H.; Leung, K.-S.; Ballester, P. J.; Wong, M.-H. iStar: a web platform for large-scale protein-ligand docking. PLOS One, 9, e85678 (2014)
(19) 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. 54, 944-955 (2014).
(20) Shin, W.-H.; Kim, J.-W.; Kim, D.-S.; Seok, C. GalaxyDock2: Protein-Ligand Docking Using Beta-Complex and Global Optimization. J. Comput. Chem. 34, 2647-2656 (2013).
(21) Zilian, D. & Sotriffer, C. A. SFCscoreRF: A random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J. Chem. Inf. Model. 53, 1923-1933 (2013).
(22) 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. 53, 2181-2195 (2013).
(23) 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. 53, 592-600 (2013).
(24) 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. 27, 15-29 (2013).
(25) Yan, Z. & Wang, J. Specificity quantification of biomolecular recognition and its implication for drug discovery. Sci. Rep. 2, 309 (2012).
(26) 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. 26, 185-197 (2012).
(27) 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. 52, 16-28 (2012).
(28) 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. 51, 2528-2537 (2011).
(29) Neudert, G.; Klebe, G. DSX: A Knowledge-Based Scoring Function for the Assessment of Protein-Ligand Complexes. J. Chem. Inf. Model. 51, 2731-2745 (2011).
(30) 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. 51, 1423-1430 (2011).
(31) Plewczynski, D.; Lazniewski, M.; von Grotthuss, M. VoteDock: Consensus Docking Method for Prediction of Protein-Ligand Interactions, J. Chem. Inf. Model. 32, 568-581 (2011).
(32) 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. 51, 214-228 (2011).
(33) Kramer, C. & Gedeck, P. Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets. J. Chem. Inf. Model. 50, 1961-1969 (2010).
(34) Ballester, P. J. & Mitchell, J. B. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 26, 1169-1175 (2010).

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