实践中的机器学习ADME模型:从成功的优化案例研究中获得的四个指南
Alexander S Rich1, Yvonne H Chan2, Benjamin Birnbaum1
1Inductive Bio, Inc., 550 Vanderbilt Ave, #730, Brooklyn, New York 11238, United States.
ACS medicinal chemistry letters
|August 14, 2024
概括
这项研究为使用机器学习 (ML) ADME 模型加速药物发现提供了实际指导方针. 将这些ML模型与化学家的专业知识相结合,可以提高的优化,减少化合物合成.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药理动力学 药理动力学
背景情况:
- 优化吸收,分布,新陈代谢和分泌 (ADME) 特性和药理动力学 (PK) 概况对于识别临床候选药物至关重要.
- 加快ADME/PK评估和减少合成努力是药物发现的关键挑战.
研究的目的:
- 为在小分子优化中使用ML ADME模型提供实用指南.
- 通过指导化合物设计的案例研究来说明ML模型的应用.
主要方法:
- 开发和应用机器学习 (ML) 模型来预测ADME属性.
- 将ML模型预测集成到药物化学决策工作流程中.
- 案例研究展示了ML模型在优化中的实际使用.
主要成果:
- ML ADME 模型可以显著指导复合物设计并加速优化.
- 成功的集成需要用户的信任,程序特定的调整,以及与化学家的专业知识的协同作用.
- 该案例研究表明,通过ML引导设计,对化合物合成的需求减少了.
结论:
- ML ADME模型是加强药物化学活动的宝贵工具.
- 有效实施ML模型需要仔细整合到现有研究流程中.
- 机器学习预测和专家化学知识之间的协同作用对于成功的药物发现计划至关重要.
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