一个大规模对接和实验结果的数据库
Brendan W Hall1, Tia A Tummino1, Khanh Tang1
1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
bioRxiv : the preprint server for biology
|March 10, 2025
概括
一个新的数据库共享数十亿个分子对接结果,使机器学习能够用于药物发现. 虽然模型随着更多数据的增加而得到改善,但高对接得分的预测并不能保证找到有效的药物候选者.
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:
- 在过去的六年中,可访问的化合物显著增加,增强了分子对接技术.
- 分享对接活动结果对于对比机器学习 (ML) 和化学太空探索方法至关重要.
研究的目的:
- 创建一个公开可访问的网站,提供大规模的分子对接活动数据.
- 为11个目标提供63亿个对接分子的姿势,分数和体外结果.
- 为了促进基准测试和探索不断扩大的化学空间.
主要方法:
- 开发了一个网站 (lsd.docking.org) 来托管对接活动数据.
- 在11个目标上对接了63亿个分子.
- 在实验中测试了3729种化合物.
- 训练有素的ML模型预测对接得分并识别得分最高的分子.
主要成果:
- 在较大的数据集上训练的模型显示性能有所改善.
- ML预测和对接分数之间的高相关性并不总是导致有效带的成功丰富.
- 即使是来自对接的顶级分子也不能保证实验成功,这突出了当前预测模型的局限性.
结论:
- 新的数据库支持ML模型培训和化学空间探索.
- 预测模型需要进一步改进,因为高的对接分数并不总是转化为实验验证.
- 开放共享大规模的对接数据对于推进药物发现方法至关重要.
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