通过人工智能和物理学挖掘强效抑制剂:基于连接体和基于结构的药物设计的统一方法
Jie Li1, Oufan Zhang1, Kunyang Sun1
1Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States.
Journal of chemical information and modeling
|June 6, 2024
概括
我们开发了iMiner,这是一种用于药物发现的机器学习算法. 它通过结合深度强化学习和分子对接来产生新型药物分子,加速潜在治疗方法的识别.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 药物发现是一个漫长而昂贵的过程.
- 计算机辅助方法对于加速药物发现至关重要.
- 开发具有理想性质的新型分子是具有挑战性的.
研究的目的:
- 开发一种机器学习算法,用于生成新型药物分子.
- 将深度强化学习与分子对接相结合,以实现高效的药物设计.
- 为了创建一个可适应各种点蛋白的多功能平台.
主要方法:
- 开发了iMiner算法,集成了深度强化学习和AutoDock Vina.
- 用于化学新性和目标相互作用的各种奖励功能.
- 集成了一个用于过化合物的工作流程 (PAINS,Lipinski违规等. ) 的情况.
- 包括交叉验证和分子动力学模拟的选项.
主要成果:
- iMiner产生了具有形状和兼容性限制的新型抑制分子.
- 该算法成功地结合了化学相似性,基于碎片的生长和残留物相互作用.
- 工作流程有效地过不良化合物,并评估姿势稳定性.
- 这种方法是蛋白质结构依赖的,允许广泛适用.
结论:
- iMiner为快速发现药物提供了一种强大的,可适应的方法.
- 该算法加速了新型小分子疗法的识别.
- 这种方法可以应用于任何用于抑制剂发展的标蛋白.
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