Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, Strasbourg Cedex 67083, France.
Department of Physics, Auburn University, Auburn, Alabama 36849, United States.
J Chem Inf Model. 2023 Jan 23;63(2):407-411. doi: 10.1021/acs.jcim.2c00919. Epub 2023 Jan 5.
The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.
准确预测蛋白质-配体结合亲和力是合理设计新药物实体的一个基本问题。目前的计算方法要么过于昂贵，要么不够准确，无法有效地用于虚拟高通量筛选活动。此外，最复杂的方法，例如基于分子动力学的构象采样的那些方法，需要进行大量的预处理和后处理才能提供最终的排序，这阻碍了非专业用户的直接应用。我们提出了一个名为 ChemFlow 的新计算平台，以弥合 2D 化学库和估计的蛋白质-配体结合亲和力之间的差距。该软件旨在准备以 SMILES 或 SDF 格式提供的化合物库，将它们对接入蛋白质结合位点，并通过简化的自由能计算重新对构象进行评分。使用包含 626 个蛋白质-配体复合物的数据集和 GPU 计算，我们证明了 ChemFlow 在中型计算机集群上每天可以处理 1000 个配体，以低于 2 kcal/mol 的 RMSE 提供相对结合自由能。该软件可在 https://github.com/IFMlab/ChemFlow 上公开获取。
