将反应方案,反应剂数据库和虚拟图书馆集成到基于片段的设计中,通过强化学习来实现
Susanne Sauer1, Hans Matter1, Gerhard Hessler1
1Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany.
Journal of chemical information and modeling
|September 5, 2023
概括
人工智能 (AI) 通过优化分子线索来增强药物设计. 这项研究改进了基于碎片的强化学习,用于具有改进性质的新型,可合成的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 人工智能驱动的生成模型对于药物设计中的优化至关重要.
- 关键的成功因素包括试剂可用性,新性和多属性优化.
- 有针对性的片段替换策略模仿药物化学方法.
研究的目的:
- 介绍基于片段的强化学习的新变体,以改进药物设计.
- 为了提高合成性和平衡新性与生成分子中的多样性.
- 用可合成的候选药物高效地探索大型化学空间.
主要方法:
- 采用了基于片段的强化学习的演员-关键模型.
- 引入了新的功能:冷碎片和使用试剂作为碎片来源.
- 纳入了分子分裂的反应方案,以提高合成能力.
- 调整网络输出概率以平衡新性和多样性.
- 结合基于片段的优化与虚拟库编码.
主要成果:
- 实现了具有有利配置的高质量分子的设计.
- 一项针对15个药物相关目标的验证研究产生了新的结构.
- 获得的分子与大多数目标的独立验证集相同或相关.
- 在基于碎片的强化学习的价值方面取得了显著的改进.
结论:
- 提出的修改大大提高了药物设计的基于片段的强化学习.
- 这种方法有效地产生了新的,可合成和高质量的候选药物.
- 这种人工智能驱动的方法为探索化学空间和优化分子特性提供了强大的工具.
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