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虚拟配体筛选:策略、前景与局限性

Virtual ligand screening: strategies, perspectives and limitations.

作者信息

Klebe Gerhard

机构信息

Institute of Pharmaceutical Chemistry, University of Marburg, Marbacher Weg 6, D-35032 Marburg, Germany.

出版信息

Drug Discov Today. 2006 Jul;11(13-14):580-94. doi: 10.1016/j.drudis.2006.05.012.

DOI:10.1016/j.drudis.2006.05.012
PMID:16793526
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7108249/
Abstract

In contrast to high-throughput screening, in virtual ligand screening (VS), compounds are selected using computer programs to predict their binding to a target receptor. A key prerequisite is knowledge about the spatial and energetic criteria responsible for protein-ligand binding. The concepts and prerequisites to perform VS are summarized here, and explanations are sought for the enduring limitations of the technology. Target selection, analysis and preparation are discussed, as well as considerations about the compilation of candidate ligand libraries. The tools and strategies of a VS campaign, and the accuracy of scoring and ranking of the results, are also considered.

摘要

与高通量筛选不同,在虚拟配体筛选(VS)中,使用计算机程序选择化合物以预测它们与目标受体的结合。一个关键前提是了解负责蛋白质-配体结合的空间和能量标准。本文总结了进行虚拟配体筛选的概念和前提条件,并探讨了该技术长期存在的局限性的原因。讨论了靶点选择、分析和制备,以及关于候选配体库编纂的考虑因素。还考虑了虚拟配体筛选活动的工具和策略,以及结果评分和排名的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/4a508de3916d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/ebefe27bccf4/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/9393588d7829/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/06a82602a5ac/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/dbe6d2e90032/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/9f9998cb3613/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/4a508de3916d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/ebefe27bccf4/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/9393588d7829/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/06a82602a5ac/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/dbe6d2e90032/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/9f9998cb3613/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bfb/7108249/4a508de3916d/gr6.jpg

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Modeling water molecules in protein-ligand docking using GOLD.使用GOLD对蛋白质-配体对接中的水分子进行建模。
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DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction.
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J Med Chem. 2025 Jan 9;68(1):307-323. doi: 10.1021/acs.jmedchem.4c01884. Epub 2024 Dec 19.
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High-Affinity Peptides for Target Protein Screened in Ultralarge Virtual Libraries.在超大型虚拟文库中筛选出的针对目标蛋白的高亲和力肽段。
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