最佳分子设计:生成式主动学习 结合再发明与精确的结合性自由能量排名模拟
Hannes H Loeffler1, Shunzhou Wan2, Marco Klähn1
1Molecular AI, Discovery Sciences, R&D, AstraZeneca, Mölndal 431 83, Sweden.
Journal of chemical theory and computation
|September 3, 2024
概括
本研究介绍了一种生成主动学习 (GAL) 协议,将AI和物理模拟结合起来,以发现新的药物配体. 该GAL协议有效地识别了高得分,多样化的分子用于药物发现目标.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
- 分子建模和模拟分子模型
背景情况:
- 积极学习 (AL) 通过智能选择数据点来加速分子发现,模仿代设计-制造-测试-分析循环.
- 传统的药物发现往往是漫长且资源密集的,需要更高效的计算方法.
- 生成型人工智能和基于物理的模拟为探索广的化学空间提供了互补的优势.
研究的目的:
- 开发和部署一个生成式主动学习 (GAL) 协议,用于发现新型分子配体.
- 将生成分子AI (REINVENT) 与基于物理的模拟 (ESMACS) 结合起来,以增强连接体的发现.
- 评估大规模计算基础设施 (Frontier) 上的GAL协议的效率和有效性.
主要方法:
- 实施一个GAL协议,集成REINVENT用于分子发电和ESMACS用于具有约束力的自由能量计算.
- 应用GAL协议来识别两个目标蛋白的配体:3CLpro和TNKS2.
- 系统地改变批量大小,用于免费能源评估,以优化协议效率.
主要成果:
- 与基线方法相比,GAL协议成功地确定了3CLpro和TNKS2目标的高得分分子.
- 发现的配体表现出显著的化学多样性,并占据了不同的化学空间.
- 分析为GAL协议中的不同场景提供了最佳批量大小建议.
结论:
- 结合人工智能和基于物理的方法,为药物发现中有效的化学空间采样提供了一个强大的策略.
- 该协议展示了前所未有的规模和效率,与现代数据驱动药物发现相关.
- 这种方法加快了针对特定设计任务优化化合物的识别.
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