机器学习引导的AQFEP:用于虚拟选的快速有效的绝对自由能量扰动解决方案
Jordan E Crivelli-Decker1, Zane Beckwith1, Gary Tom1,2,3
1SandboxAQ, Palo Alto, California 94301, United States.
Journal of chemical theory and computation
|August 15, 2024
概括
这项研究引入了在药物发现中进行虚拟选的高效工作流程,将主动学习与快速绝对自由能量扰动方法相结合,用于在没有参考分子的情况下准确地预测结合亲和力.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 分子建模分子建模
背景情况:
- 基于结构的方法在现代药物发现中至关重要.
- 虚拟查 (VS) 快速探索潜在药物候选者的化学空间.
- 像相对自由能量扰动 (RFEP) 这样的当前方法是准确的,但在计算上昂贵,需要参考分子.
- 绝对自由能量扰动 (AFEP) 提供了理论准确性,用于击中识别,但缺乏VS的吞吐量.
研究的目的:
- 开发一个集成的工作流程,以有效地对大型化学图书馆进行虚拟选.
- 将主动学习与基于物理的快速评分功能相结合,以改善药物发现.
- 为了能够在不需要参考分子的情况下准确地预测结合亲和力.
主要方法:
- 开发了一个集成的工作流程,将主动学习与快速的绝对自由能量扰动 (AFEP) 方法结合起来.
- 使用基于物理的评分功能,以提高结合亲和力预测的准确性.
- 应用工作流来选大型和多样化的化学库.
主要成果:
- 验证了工作流在排名结构相关联联体中的表现.
- 在虚拟选中证明了显著的命中率丰富.
- 通过积极学习展示了有效的化学空间探索.
- 报告了迄今为止最大的自由能源模拟集合.
结论:
- 集成的工作流提供了一种高效准确的方法,用于在药物发现中进行虚拟查.
- 该方法克服了用于高通量选的传统RFEP和AFEP方法的局限性.
- 这种方法通过将计算效率与预测能力相结合,可以更快地识别有前途的候选药物.
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