积极学习引导的对白丰富的重复激酶2 WDR域的击中优化,基于在体中体结合的亲缘关系
Filipp Gusev1,2, Evgeny Gutkin1, Francesco Gentile3,4
1Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Journal of chemical information and modeling
|May 26, 2025
概括
研究人员开发了一种使用分子动态的新型主动学习工作流程,以发现LRRK2 WDR域的新抑制剂,这是帕金森病药物发现的关键目标. 这种方法有效地发现了8种新的抑制剂,加速了对帕金森病治疗方法的搜索.
科学领域:
- 计算化学和药物发现
- 神经科学和神经退行性疾病
- 机器学习在药理学中的应用.
背景情况:
- 氨酸丰富的重复激酶2 (LRRK2) 突变是家族性帕金森病的主要原因.
- LRRK2 WDR 域是一个尚未探索的,但对于帕金森病至关重要的药物标.
- 在CACHE挑战之前,没有LLRRK2 WDR域的抑制剂已知.
研究的目的:
- 设计和实验验证新型LRRK2 WDR域抑制剂.
- 应用和评估积极学习 (AL) 机器学习 (ML) 工作流程用于药物发现.
- 为了有效地探索化学空间,寻找具有增强结合亲和力的小分子类似物.
主要方法:
- 使用了一个主动学习 (AL) 机器学习 (ML) 工作流.
- 使用热力学集成 (TI) 框架进行优化的自由能量分子动力学 (MD) 模拟.
- 扩展化学序列围绕已确认的打击分子用于抑制剂设计.
主要成果:
- 确定了8种新的LRRK2 WDR域抑制剂.
- 从35个实验测试的分子中获得23%的成功率.
- 证明了基于自由能量的AL/ML工作流在探索化学空间中的效率.
结论:
- 开发的以自由能量为基础的积极学习工作流对快速有效地探索化学空间是有效的.
- 这种工作流显著加快了发现具有增强亲和力的小分子抑制剂的速度.
- 该方法广泛适用于选化学空间用于药物发现,TI MD计算的平均绝对误差为2.69 kcal/mol.
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