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高通量虚拟筛选共识对接综合调查。

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening.

机构信息

Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández, Av. de la Universidad s/n, 03202 Elche, Spain.

出版信息

Molecules. 2022 Dec 25;28(1):175. doi: 10.3390/molecules28010175.

Abstract

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

摘要

3D 技术在蛋白质结构测定方面的快速发展,以及众多计算方法和策略的发展,使得在计算机药物设计中鉴定高活性化合物成为可能。分子对接是一种广泛应用于高通量虚拟筛选的方法,用于筛选针对蛋白质的潜在配体。目前有许多不同的对接程序,它们在用于预测配体结合模式和亲和力的算法和方法上有所不同。所有程序都严重依赖评分函数来准确预测配体结合亲和力,尽管性能存在差异,但这些对接程序都没有比其他程序更有优势。为了解决这个问题,共识评分方法通过平均不同对接程序获得的单个分子的排名或分数来提高虚拟筛选的结果。共识对接在高通量虚拟筛选中的成功应用凸显了优化分子对接方法预测能力的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3849/9821981/6f8055ff0582/molecules-28-00175-g001.jpg

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