通过使用在贝叶斯对接近似上训练的强化学习的混合深度生成模型来改进药物发现.
Youjin Xiong1, Yiqing Wang2, Yisheng Wang1
1Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China.
Journal of computer-aided molecular design
|August 7, 2023
概括
生成分子设计现在使用对接分数来更快地创建新型药品. 这种方法克服了数据的局限性,有效地产生多样化,高性能的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 生成模型是设计新药的关键.
- 有限的实验数据往往阻碍了生成模型的性能,导致结果差或过拟合.
- 现有的方法难以产生多样化,新的化学型.
研究的目的:
- 为了减少对新化学型的生成分子设计的数据依赖.
- 通过强化学习增强生成的分子的多样性和性能.
- 为了加快潜在的治疗药物的发现.
主要方法:
- 集成的对接得分进入深度生成模型的奖励函数.
- 使用基于机器学习的贝叶斯回归模型来估计对接分数.
- 结合有限的药物活性数据与初始培训的近似对接分数.
- 通过完整的对接模拟,对高得分化合物的最终评估.
- 采用强化学习来推断分子-受体相互作用.
主要成果:
- 与大小相似的分子相比,生成的分子对接得分提高了10-20%.
- 在GPU工作站上展示了130倍的速度增加,而不是仅对接的方法.
- 展示了较高的对接分数和与已知的抑制剂相似的姿势之间的相关性.
- 观察到的MM-GBSA结合能量与已知的DDR1抑制剂相当.
结论:
- 开发的方法有效地减少了产生新型化学型的数据依赖.
- 学习的分子表示和基于特征的对接回归的结合使得受体相互作用的有效学习成为可能.
- 这种方法是发现具有治疗潜力的新化学型的强大工具.
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