在预测药物膜分区时应用生成性人工智能:结合Denoising Diffusion概率模型和MD模拟,将计算成本降低到三分之一
Peter Obi1, Jeevan B Gc2, Charles Mariasoosai1
1College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States.
Journal of chemical theory and computation
|June 28, 2024
概括
生成型人工智能模型显著降低了药物-膜相互作用研究的计算成本. 拒绝扩散概率模型 (DDPMs) 准确地使用部分模拟数据预测药物分区和行为.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 药物与细胞膜的相互作用对于有效的药理学至关重要.
- 了解药物的分离和与膜蛋白结合需要详细的模拟数据.
- 目前的分子动力学方法在计算上昂贵,用于高通量选.
研究的目的:
- 调查无声扩散概率模型 (DDPMs) 的应用,以降低药物-膜相互作用研究中的计算成本.
- 评估DDPM在预测脂质双层中的药物分区概况和行为方面的准确性.
- 通过优化模拟方法,探索AI在加速药物发现方面的潜力.
主要方法:
- 利用了无声扩散概率模型 (DDPMs),一种生成性AI方法.
- 将DDPM应用于用于药物膜相互作用的雨采样 (US) 模拟数据.
- 在部分美国数据 (三分之一) 上训练模型,以预测平均力 (PMF) 配置文件的完整潜力.
主要成果:
- 与传统的美国模拟相比,DDPM显著降低了计算成本.
- 准确地复制了FDA批准的药物和其他化学品的完整PMF配置文件.
- 预测的无溶解能量,双层位置,以及高准确度的配体方向.
结论:
- DDPM提供了一种计算效率高的方法,用于表征药物分子的完整膜分区配置文件.
- 这种人工智能驱动的方法可以使用更少的美国模拟来实现准确的预测.
- 不管药物的两性-脂性-脑性特征如何,DDPMs都可以应用,从而推动药物发现.
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