Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
J Comput Aided Mol Des. 2023 Nov;37(11):565-572. doi: 10.1007/s10822-023-00527-z. Epub 2023 Aug 24.
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
准确的虚拟筛选工具的设计是药物发现中的一个开放性挑战。已经在不同的近似水平上开发了几种基于结构的方法。其中，分子对接是一种效率高但准确性通常较低的成熟技术。此外，对接性能已知是靶标依赖性的，这使得在接近新的蛋白质靶标时，对接程序和相应的评分函数的选择至关重要。为了比较不同对接方案的性能，我们开发了 ChemFlow_py，这是一种用于执行对接和重评分的自动化工具。使用从 DUD-E 中提取的四个蛋白质系统，每个目标有 100 个已知活性化合物和 3000 个诱饵，我们比较了包括共识评分在内的几种重评分策略的性能。我们发现，通过共识排序可以提高平均对接结果，这表明在给定靶标几乎没有或没有化学信息时，共识评分很重要。ChemFlow_py 是一个用于优化虚拟高通量筛选（vHTS）性能的免费工具包。该软件可在 https://github.com/IFMlab/ChemFlow_py 上获得。
