基于对接的机器学习用于基诺姆范围内的亲和力预测.
Jordy Schifferstein1,2, Andrius Bernatavicius3, Antonius P A Janssen1,2
1Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University, Leiden 2333CC, The Netherlands.
Journal of chemical information and modeling
|December 10, 2024
概括
机器学习通过分析对接姿势来预测酶抑制剂的选择性. 这种方法通过早期识别潜在的非目标效应,帮助药物发现,改善抗癌药物开发.
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:
- 激酶抑制剂是关键的抗癌药物,但由于ATP结合部位的竞争,实现选择性是具有挑战性的.
- 非目标效应和毒性是与当前的激酶抑制剂相关的重大风险.
- 对酶抑制剂跨基因组结合的实验性评估是昂贵和耗时的.
研究的目的:
- 开发一种可靠和可解释的计算方法来预测酶抑制剂的选择性.
- 为了促进药物发现和对酶抑制剂的优化过程.
- 为评估潜在的毒性和非目标效应提供一个工具.
主要方法:
- 聚合已知的抑制剂-激酶亲属性,并通过将抑制剂与X射线结构对接生成3D相互作用体.
- 训练了一个神经网络,使用对接姿势作为一种酶特定的评分函数.
- 自动化了从分子到基于3D的亲和力预测的整个管道.
主要成果:
- 神经网络在基因组中未见的抑制剂上实现了R^2 = 0.63-0.74的性能.
- 开发的计算方法提供了可靠的预测酶选择性.
- 预测管道是完全自动化和可访问的.
结论:
- 在对接姿势上的机器学习为预测酶抑制剂选择性提供了一种强大的方法.
- 开发的自动化管道可以很容易地在药物化学实践中采用.
- 这种工具可以显著帮助发现和优化更安全,更有效的激酶抑制剂.
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