Literature DB >> 20681607

Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions.

Mengzhu Xue1, Mingyue Zheng, Bing Xiong, Yanlian Li, Hualiang Jiang, Jingkang Shen.   

Abstract

Protein kinases are attractive targets for therapeutic interventions in many diseases. Due to their importance in drug discovery, a kinase family-specific potential of mean force (PMF) scoring function, kinase-PMF, was developed to assess the binding of ATP-competitive kinase inhibitors. It is hypothesized that target-specific PMF scoring functions may achieve increased performance in scoring along with the growth of the PDB database. The kinase-PMF inherits the functions and atom types in PMF04 and uses a kinase data set of 872 complexes to derive the potentials. The performance of kinase-PMF was evaluated with an external test set containing 128 kinase crystal structures. We compared it with eight scoring functions commonly used in computer-aided drug design, either in terms of the retrieval rate of retrieving "right" conformations or a virtual screening study. The evaluation results clearly demonstrate that a target-specific scoring function is a promising way to improve prediction power in structure-based drug design compared with other general scoring functions. To provide this rescoring service for researchers, a publicly accessible Web site was established at http://202.127.30.184:8080/scoring/index.jsp .

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Year:  2010        PMID: 20681607     DOI: 10.1021/ci100182c

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


  6 in total

1.  A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

Authors:  Yu Wang; Yanzhi Guo; Qifan Kuang; Xuemei Pu; Yue Ji; Zhihang Zhang; Menglong Li
Journal:  J Comput Aided Mol Des       Date:  2014-12-20       Impact factor: 3.686

Review 2.  Receptor-ligand molecular docking.

Authors:  Isabella A Guedes; Camila S de Magalhães; Laurent E Dardenne
Journal:  Biophys Rev       Date:  2013-12-21

Review 3.  Computational drug discovery.

Authors:  Si-Sheng Ou-Yang; Jun-Yan Lu; Xiang-Qian Kong; Zhong-Jie Liang; Cheng Luo; Hualiang Jiang
Journal:  Acta Pharmacol Sin       Date:  2012-08-27       Impact factor: 6.150

Review 4.  Structure-based virtual screening for drug discovery: a problem-centric review.

Authors:  Tiejun Cheng; Qingliang Li; Zhigang Zhou; Yanli Wang; Stephen H Bryant
Journal:  AAPS J       Date:  2012-01-27       Impact factor: 4.009

5.  Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.

Authors:  Jie Liu; Minyi Su; Zhihai Liu; Jie Li; Yan Li; Renxiao Wang
Journal:  BMC Bioinformatics       Date:  2017-07-18       Impact factor: 3.169

6.  Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.

Authors:  Dingyan Wang; Chen Cui; Xiaoyu Ding; Zhaoping Xiong; Mingyue Zheng; Xiaomin Luo; Hualiang Jiang; Kaixian Chen
Journal:  Front Pharmacol       Date:  2019-08-22       Impact factor: 5.810

  6 in total

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