ClickGen:通过模块化反应和强化学习来定向探索可合成的化学空间
Mingyang Wang1, Shuai Li2,3, Jike Wang1,4
1College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Nature communications
|November 23, 2024
概括
一个深度学习模型ClickGen使用点击化学生成具有高合成能力的新型药物分子. 这种人工智能方法加速了药物发现,导致具有抗癌功效的强大的PARP1抑制剂.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药物发现的生成模型面临着分子合成能力的挑战.
- 新型分子生成需要高效和可靠的方法来实现现实应用.
研究的目的:
- 开发一个深度学习模型,ClickGen,用于生成新,多样化和可合成的分子.
- 为了验证ClickGen生成的分子对多氨酸二酸盐-糖聚合酶1 (PARP1) 的疗效.
主要方法:
- 利用点击化学用于模块化分子组装.
- 集成的强化学习和对分子多样性和结合亲和力进行染色.
- 对产生化合物的生物活性进行了湿实验室验证,针对PARP1.
主要成果:
- 在新性,合成性和对接相似性方面,ClickGen的表现优于现有的模型.
- 在20天内成功合成并测试了新的PARP1抑制剂.
- 确定了两种具有纳米分子抑制活性,低毒性和对癌细胞系的抗增殖作用的化合物.
结论:
- ClickGen为生成化学中的合成能力挑战提供了一个可行的解决方案.
- 这种人工智能驱动的方法加速了药物发现和自动化实验.
- 代表了闭环分子设计和人工智能驱动的药物开发的新范式.
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