CardioGenAI:基于机器学习的框架,用于重新设计药物,以减少hERG责任
Gregory W Kyro1,2, Matthew T Martin3, Eric D Watt3
1Department of Chemistry, Yale University, New Haven, CT, 06511, USA. gregory.kyro@yale.edu.
Journal of cheminformatics
|March 5, 2025
概括
CardioGenAI使用机器学习来重新设计药物,减少hERG通道活动并预防心律失常. 这一框架有助于通过优化药物安全性,同时保持有效性来拯救药物开发计划.
科学领域:
- 计算化学和药物发现
- 心血管安全药理学心血管安全药理学
- 机器学习在制药研究中的应用
背景情况:
- 在体外hERG离子通道抑制与体内QT间隔延长相关,存在药物诱导的心律失常的风险,如Torsade de Pointes.
- 早期识别hERG活性化合物至关重要,以防止终止有前途的候选药物.
- 重新设计药物以减少hERG责任,同时保持药理活性是非常有趣的.
研究的目的:
- 介绍CardioGenAI,一种用于重新设计药物以减少hERG活动的机器学习框架.
- 为了预测对hERG的活性,NaV1.5和CaV1.2通道进行全面的心血管安全评估.
- 证明该框架能够优化药物特征,同时保持基本的药理和物理化学性质.
主要方法:
- 开发一种基于机器学习的框架 (CardioGenAI) 用于药物再工程.
- 纳入最先进的歧视模型来预测离子通道活性 (hERG,NaV1.5,CaV1.2).
- 该框架应用于FDA批准的药物,包括皮莫齐德,以产生具有更好的安全概况的精制候选药物.
主要成果:
- CardioGenAI成功地从皮莫齐德生成了100种精制候选物,其中包括具有700倍弱hERG结合力的fluspirilene.
- 该框架优化了多个FDA批准的化合物的hERG,NaV1.5和CaV1.2配置文件.
- 重新设计的化合物保持了它们的原始物理化学特性和药理活性.
结论:
- 卡迪奥基因AI提供了一种方法来拯救与hERG相关的安全问题阻碍的药物开发计划.
- 框架内的歧视模型可以作为虚拟选的独立组件.
- 卡迪奥基因AI的开源性质有助于将其整合到分子假设生成的药物发现工作流中.
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