整合QSAR建模与强化学习用于Syk抑制剂发现
Maria Zavadskaya1, Anastasia Orlova1, Andrei Dmitrenko2
1Center for AI in Chemistry, ITMO University, Lomonosova St. 9, St. Petersburg, 197101, Russia.
Journal of cheminformatics
|April 15, 2025
概括
研究人员开发了一种新的深度强化学习策略,以发现用于自身免疫性疾病的新腺氨酸激酶 (Syk) 抑制剂. 这种方法确定了139种具有高强度和结构新性的有前途的候选药物.
科学领域:
- 药用化学 医学化学
- 计算机化药物发现技术
- 药理学 药理学是指药理学的学科.
背景情况:
- 腺氨酸激酶 (Syk) 是炎症途径的关键调解物,也是免疫性血栓塞缩症等自身免疫性疾病的治疗点.
- 现有的Syk抑制剂表现出低于最佳的疗效和安全性,推动了对新药发现方法的需求.
研究的目的:
- 开发和应用一种新的深度强化学习策略,用于识别新的腺氨酸激酶 (Syk) 抑制剂.
- 产生结构新的Syk抑制剂,具有高预测强度和有利的类似药物的特性.
主要方法:
- 整合定量结构-活动关系 (QSAR) 预测与使用堆叠整体方法的生成建模.
- 产生和选超过78,000个分子,以确定有希望的Syk抑制剂候选者.
- 根据预测的效能,结合亲和力和药物相似性来评估候选药物.
主要成果:
- 一个深度强化学习模型在预测分子活动时实现了0.78的相关系数.
- 确定了139个具有高预测功效,结合亲和力和药物相似性的候选分子.
- 这些已识别的化合物在保持关键的Syk抑制剂特征的同时表现出结构上的新性.
结论:
- 开发的QSAR引导强化学习框架加速了新型Syk抑制剂的发现.
- 这种方法为药物发现提供了一种多功能方法,特别是在罕见疾病治疗方面.
- 这项研究为Syk抑制剂发现提供了QSAR引导强化学习的首次应用,产生了有前途的新型候选者.
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