Comparative evaluation of 11 scoring functions for molecular docking.

By Renxiao Wang, Yipin Lu, Shaomeng Wang. 2003

851 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, neutral): “…[34] GlideScore [37] DrugScore [62] GoldScore [35] ChemScore [60] PMF_Score [83] ICM [46] X_Score [66] MotifScore [84] LigandFit [47] F_Score [73] RF_Score [85] Molegro Virtual Docker [48] Fresno [75] PESD_SVM [86] SYBYL_G-Score [73] SCORE [76] PoseScore [87] SYBYL_D-Score [73] LUDI [77]…”
PotentialNet for Molecular Property Prediction (2018, neutral): “We term this train-test split “PDBBind 2007, Reﬁned Train, Core Test” below, and compare performance with RF-score 25 , X-Score 41,42 , and the networks (7)-(9) described in this work.”
Virtual ligand screening: strategies, perspectives and limitations (2006, background): “118 Wang, R. and Wang, S. (2001) How does consensus scoring work for virtual library”
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review (2012, background): “The performance of various scoring functions has been investigated by several comparative studies (73 – 77), with respect to the ability of reproducing known binding pose, predicting binding af ﬁ nity and rank-ordering a compound library.”
Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark (2018, background): “A pioneering work was published by Wang et al. 61 , who conducted a comparative study of 11 scoring functions on a set of 100 protein–ligand complexes.”
Key Topics in Molecular Docking for Drug Design (2019, methodology): “Moreover, it is widely accepted that the conformational sampling is not the major bottleneck in the docking process [109,110] therefore, a greater fraction of the developed methods generate the docking poses using a single algorithm and subsequently use a different set of SFs to re-assess them (Tabl”
An interaction-motif-based scoring function for protein-ligand docking (2010, methodology): “The sulfur atom of cysteines, assigned as S3N, was also commonly observed a The data were taken from Wang et al. [9] and Velec et al. [20], except for the MotifScore results, which were computed in this work using the same dataset. b Scoring functions are ranked by their success rates at rmsd % 2.0…”
Comparing protein–ligand docking programs is difficult (2005, methodology): “45,46 In this situation, it is essential to perform local optimization of the docking solution with respect to each of the scoring functions.”