使用机器学习方法加速分子对接
Abdulsalam Y Bande1, Sefer Baday1,2,3
1Computer Science Department, Informatics Institute, Istanbul Technical University, Istanbul, Türkiye.
Molecular informatics
|June 8, 2024
概括
这项研究通过使用机器学习来预测分子对接分数,绕过漫长的计算来加速药物发现. 这种方法有效地选了数百万种化合物,大大减少了识别潜在候选药物的时间和成本.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 虚拟查 (VS) 对于药物发现至关重要,减少实验成本和时间.
- 基于结构的药物发现方法,如对接是有效的,但对于大型化学库来说具有挑战性.
- 当前的VS方法与化学数据库的快速增长作斗争.
研究的目的:
- 通过在没有明确计算的情况下预测对接分数来加速对接研究.
- 开发一种机器学习模型,有效地预测分子对接得分.
- 为了快速选大型化学图书馆用于药物发现.
主要方法:
- 利用基于注意力的长期短期记忆 (LSTM) 神经网络.
- 采用其他机器学习模型,包括XGBoost.
- 训练模型在一个小组的连接器对接得分上预测数百万个分子的得分.
主要成果:
- 在11个数据集中平均获得了0.77的R2和0.85的Spearman等级相关性.
- 在只训练了7000个分子后,成功预测了大约380万个分子的对接分数.
- 开发了一个用户友好的系统,将SMILES和对接分数作为输入,以生成预测模型.
结论:
- 机器学习模型可以准确预测对接分数,显著加速虚拟选.
- 开发的系统为药物发现中的大规模化合物查提供了高效和成本效益的解决方案.
- 这种方法有助于从广的化学空间中识别有前途的候选药物.
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