基于结构的虚拟选机器学习评分的实用指南
Viet-Khoa Tran-Nguyen1, Muhammad Junaid1, Saw Simeon1
1Centre de Recherche en Cancérologie de Marseille, Marseille, France.
Nature protocols
|October 16, 2023
概括
本研究介绍了一种用于构建和评估基于结构的虚拟选 (SBVS) 的特定目标机器学习评分函数 (SF) 的协议. 这种方法增强了对治疗点的活性分子的发现.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 基于结构的虚拟查 (SBVS) 使用对接来识别治疗点的活性分子.
- 随着生物活性信息的大量化学和蛋白质数据集的可用性显著增加.
- 机器学习 (ML),包括深度学习,有效地利用这些数据集来开发SBVS的评分函数 (SF).
研究的目的:
- 为SBVS提供一个全面和用户友好的协议,用于构建和评估针对特定目标的基于ML的SF.
- 为增强SBVS性能提供关于数据增强,算法选择和SF优化的实际指导.
- 为了促进发现具有较高活动可能性的分子对特定目标的发现.
主要方法:
- 该协议涉及四个关键步骤:在公共基准上评估通用SF,准备特定目标的实验数据,为ML建模分区数据,并生成/评估特定目标的MLSF.
- 使用公共存储库来获取数据,并为ML建模准备培训和测试数据集.
- 使用可访问的软件 (Smina,CNN-Score,RF-Score-VS,DeepCoy) 和用于SF生成和评估的网络资源.
主要成果:
- 该协议使用三个示例目标来演示:乙胆化酶,HMG-CoA减少酶和过氧酶增殖器激活受体-α.
- 所有必要的代码和数据都是公开可用的,使得可复制性和应用.
- 该过程可以在一周内在一台计算机上执行,突出其效率.
结论:
- 针对特定目标的基于ML的SFs代表了SBVS的最先进的状态,通常表现优于通用方法.
- 提出的协议提供了通过增强训练数据和选择适当的ML算法来优化SBVS的实际指导.
- 该协议的成功实施可以显著提高发现新疗法剂的效率和成功率.
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