FEP-SPell-ABFE:用于药物发现的开源自动炼金术绝对绑定自由能计算工作流。

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Pengfei Li, Tingting Pu, Ye Mei
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引用次数: 0

摘要

以绝对结合自由能(ABFE)衡量的药物分子与靶标之间的结合亲和力是药物开发先导发现阶段的关键因素。最近的研究强调了计算机ABFE预测的潜力,通过允许对有希望的候选药物进行排名和优先排序,直接帮助药物开发。这项工作引入了一个名为FEP-SPell-ABFE的开源Python工作流,旨在以最小的用户参与自动化ABFE计算。工作流只需要三个关键输入:PDB格式的受体蛋白结构,SDF格式的候选配体,以及管理工作流和分子动力学模拟参数的配置文件(config.yaml)。它以逗号分隔值(CSV)格式生成配体的排序表以及它们的结合自由能。工作流利用SLURM(用于资源管理的简单Linux实用程序)来自动化任务执行和跨模块的资源分配。提供了一个使用示例和几个用于验证的基准系统。fep -拼写- abfe工作流,以及一个实际的例子,可以在GitHub上公开访问https://github.com/freeenergylab/FEP-SPell-ABFE,在MIT许可下分发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery.

The binding affinity between a drug molecule and its target, measured by the absolute binding free energy (ABFE), is a crucial factor in the lead discovery phase of drug development. Recent research has highlighted the potential of in silico ABFE predictions to directly aid drug development by allowing for the ranking and prioritization of promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed to automate ABFE calculations with minimal user involvement. The workflow requires only three key inputs: a receptor protein structure in PDB format, candidate ligands in SDF format, and a configuration file (config.yaml) that governs both the workflow and molecular dynamics simulation parameters. It produces a ranked list of ligands along with their binding free energies in the comma-separated values (CSV) format. The workflow leverages SLURM (Simple Linux Utility for Resource Management) for automating task execution and resource allocation across the modules. A usage example and several benchmark systems for validation are provided. The FEP-SPell-ABFE workflow, along with a practical example, is publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under the MIT License.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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