深层超分子语言处理用于共同晶体预测
Rebecca Birolo1, Rıza Özçelik1, Andrea Aramini2
1Eindhoven University of Technology: Technische Universiteit Eindhoven, Biomedical Engineering, NETHERLANDS, KINGDOM OF THE.
Angewandte Chemie (International ed. in English)
|May 13, 2025
概括
DeepCocrystal是一个新的深度学习模型,可以预测药物联合晶体的形成,准确度为78%. 这种人工智能工具通过识别有前途的共同晶体对来加速药物开发,有助于发现新的药物配方.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 低于最佳的药物动力学特征影响了大约40%的商业化药物.
- 同结晶增强药物的物理化学特性,而不会影响药理活性.
- 鉴定合适的共同晶体对是具有挑战性的,因为大量的分子组合.
研究的目的:
- 开发一种新的深度学习方法,DeepCocrystal,用于预测共晶形成.
- 从超分子的角度处理化学信息,使用化学语言处理.
- 加速发现新的共同晶体,以改善药物开发.
主要方法:
- 开发了DeepCocrystal,这是一个使用分子字符串表示的深度学习模型.
- 训练并验证了预测共同晶体形成的模型.
- 采用可解释的AI来理解模型的决策过程.
- 将不确定性估计集成到预测框架中.
主要成果:
- 在现实的预测场景中,DeepCocrystal实现了78%的平衡精度,超过了现有的模型.
- 可解释的人工智能证实,该模型学习了化学相关的超分子特征.
- 该模型在一项前性研究中成功识别了两种新的diflunisal联合晶体.
- 不确定性估计指导了潜在发现过程.
结论:
- DeepCocrystal有效地预测了共同晶体的形成,加速了有前途的候选药物的识别.
- 深度学习和化学语言处理为制药研究提供了强大的工具.
- 开发的模型及其网络应用程序可以使学术和工业药物开发工作受益.
相关概念视频
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