FEP协议构建器:使用主动学习优化自由能量扰乱协议.
César de Oliveira1, Karl Leswing2, Shulu Feng2
1Schrodinger, Inc., 9868 Scranton Road, Suite 3200, San Diego, California 92121, United States.
使用FEP协议构建器 (FEP-PB) 的自动化自由能量扰动 (FEP) 协议优化快速生成准确的结合亲和预测. 这加快了药物发现的速度,以满足以前由手动FEP协议开发所限制的具有挑战性的目标.
科学领域:
- 计算化学是一种计算化学.
- 分子建模分子建模
- 药物发现 药物发现
背景情况:
- 自由能量扰动 (FEP) 的计算能够高准确度地预测结合亲和力 (∼1 kcal mol−1).
- 在力场,采样算法和并行计算方面的进步推动了FEP方法的改进.
- 默认的FEP设置与某些目标系统发生冲突,需要手动优化协议.
研究的目的:
- 开发一个自动化工作流程,FEP协议构建器 (FEP-PB),用于快速生成FEP协议.
- 解决手动FEP协议优化的局限性,包括时间,计算成本和潜在的过拟合.
- 提高FEP计算对更广泛的药物发现目标的适用性.
主要方法:
- FEP协议构建器 (FEP-PB) 使用主动学习工作流来搜索协议参数空间.
- 代搜索优化参数,以开发准确的FEP协议.
- 在MCL1和p97等具有挑战性的系统上进行验证,包括真实世界的药物发现环境.
主要成果:
- 对于以前具有挑战性的系统,FEP-PB迅速生成了准确的FEP协议 (MCL1,p97).
- 自动化方法需要有限的人工干预,并优于p97系统的专家手动优化.
- 积极学习提供了关于FEP绩效特定参数的重要性的见解.
结论:
- FEP-PB是一个强大的工具,可以快速开发准确的FEP协议.
- 该工作流提高了药物发现中FEP计算的效率和成功率.
- FEP-PB将FEP技术的实用性扩展到更复杂和更具挑战性的生物目标.
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