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.

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中文翻译：

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ChemFlow_py：用于对接和重新评分的灵活工具包

准确的虚拟筛选工具的设计是药物发现中的一个公开挑战。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，分子对接是一种成熟的技术，效率高，但精度通常较低。此外，众所周知，对接性能是目标依赖性的，这使得对接程序和相应评分函数的选择在接近新的蛋白质目标时至关重要。为了比较不同对接协议的性能，我们开发了 ChemFlow_py，这是一种执行对接和重新评分的自动化工具。使用从 DUD-E 中提取的四种蛋白质系统，每个目标具有 100 种已知活性化合物和 3000 个诱饵，我们比较了包括共识评分在内的几种重新评分策略的性能。我们发现，平均对接结果可以通过共识排名来改善，这强调了当给定目标很少或没有化学信息时共识评分的相关性。ChemFlow_py 是一个免费工具包，用于优化虚拟高通量筛选 (vHTS) 的性能。该软件可在 https://github.com/IFMlab/ChemFlow_py 上公开获取。

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