Literature DB >> 30481020

Comparative Assessment of Scoring Functions: The CASF-2016 Update.

Minyi Su1,2, Qifan Yang1,2, Yu Du1,2, Guoqin Feng1,2, Zhihai Liu1, Yan Li1, Renxiao Wang1,2,3.   

Abstract

In structure-based drug design, scoring functions are often employed to evaluate protein-ligand interactions. A variety of scoring functions have been developed so far, and thus, some objective benchmarks are desired for assessing their strength and weakness. The comparative assessment of scoring functions (CASF) benchmark developed by us provides an answer to this demand. CASF is designed as a "scoring benchmark", where the scoring process is decoupled from the docking process to depict the performance of scoring function more precisely. Here, we describe the latest update of this benchmark, i.e., CASF-2016. Each scoring function is still evaluated by four metrics, including "scoring power", "ranking power", "docking power", and "screening power". Nevertheless, the evaluation methods have been improved considerably in several aspects. A new test set is compiled, which consists of 285 protein-ligand complexes with high-quality crystal structures and reliable binding constants. A panel of 25 scoring functions are tested on CASF-2016 as a demonstration. Our results reveal that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power. Scoring power is somewhat correlated with ranking power, so are docking power and screening power. The results obtained on CASF-2016 may provide valuable guidance for the end users to make smart choices among available scoring functions. Moreover, CASF is created as an open-access benchmark so that other researchers can utilize it to test a wider range of scoring functions. The complete CASF-2016 benchmark will be released on the PDBbind-CN web server ( http://www.pdbbind-cn.org/casf.asp/ ) once this article is published.

Entities:  

Year:  2018        PMID: 30481020     DOI: 10.1021/acs.jcim.8b00545

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  63 in total

1.  Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Authors:  Jianing Lu; Xuben Hou; Cheng Wang; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2019-10-31       Impact factor: 4.956

2.  Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Authors:  Yuwei Yang; Jianing Lu; Chao Yang; Yingkai Zhang
Journal:  J Comput Aided Mol Des       Date:  2019-11-15       Impact factor: 3.686

3.  AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.

Authors:  Duc Duy Nguyen; Guo-Wei Wei
Journal:  J Chem Inf Model       Date:  2019-07-01       Impact factor: 4.956

4.  Are 2D fingerprints still valuable for drug discovery?

Authors:  Kaifu Gao; Duc Duy Nguyen; Vishnu Sresht; Alan M Mathiowetz; Meihua Tu; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-04-29       Impact factor: 3.676

5.  Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Authors:  Florian Leidner; Nese Kurt Yilmaz; Celia A Schiffer
Journal:  J Chem Inf Model       Date:  2019-08-19       Impact factor: 4.956

6.  Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

Authors:  Hongjian Li; Gang Lu; Kam-Heung Sze; Xianwei Su; Wai-Yee Chan; Kwong-Sak Leung
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

7.  Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics.

Authors:  Richard D Smith; Heather A Carlson
Journal:  J Chem Inf Model       Date:  2021-02-18       Impact factor: 4.956

8.  Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach.

Authors:  Suman Kumar Mandal; Parthapratim Munshi
Journal:  Molecules       Date:  2021-04-29       Impact factor: 4.411

9.  GNINA 1.0: molecular docking with deep learning.

Authors:  Andrew T McNutt; Paul Francoeur; Rishal Aggarwal; Tomohide Masuda; Rocco Meli; Matthew Ragoza; Jocelyn Sunseri; David Ryan Koes
Journal:  J Cheminform       Date:  2021-06-09       Impact factor: 5.514

10.  Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning.

Authors:  Duc Duy Nguyen; Kaifu Gao; Jiahui Chen; Rui Wang; Guo-Wei Wei
Journal:  Chem Sci       Date:  2020-09-30       Impact factor: 9.825

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