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, California 94158, United States.
Journal of chemical information and modeling
|April 24, 2025
概括
一个新的数据库共享数十亿个分子对接结果,使得更好的机器学习模型训练和化学空间探索成为可能. 分享这些对接分数和实验数据对于推进药物发现方法至关重要.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 大型复合库的可用性已经推进了分子对接,但共享的结果是有限的.
- 这种数据稀缺性阻碍了机器学习和化学太空探索方法的基准测试.
研究的目的:
- 创建一个大规模分子对接活动的公开可访问的数据库.
- 为11个目标提供63亿个对接分子的姿势,分数和体外结果.
- 为了促进对比和探索广的化学空间.
主要方法:
- 开发了一个网站来托管最近大型图书馆对接活动的数据.
- 在11个生物目标上对接了63亿个分子.
- 实验测试了3729种选定的化合物.
- 训练有素的机器学习模型来预测对接得分并识别得分最高的分子.
主要成果:
- 该数据库提供了前所未有的对接姿势,分数和实验验证数据的访问权限.
- 概念验证研究表明,机器学习模型在更大的训练数据集下得到改善.
- 与对接分数相对应的模型并不总是丰富排名第一的分子或新型连接体.
结论:
- 新的数据库是推动药物发现中的分子对接和机器学习应用程序的宝贵资源.
- 分享全面的对接数据对于稳健的方法开发和验证至关重要.
- 需要使用更复杂的方法进行进一步的研究,以充分利用这些数据来发现连接体.
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