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CoDrug:通过多模式语言模型的文本驱动的分子虚拟选和多属性优化框架
Rui Gu1, Yingxu Liu1, Bingxing Zhu1
1School of Science, China Pharmaceutical University, Nanjing 210009, China.
Journal of chemical information and modeling
|January 22, 2026
概括
CoDrug 是一种新的AI框架,用于药物发现,使用文本和分子结构. 它加速了虚拟选和分子优化,特别是当3D数据有限时.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 生物信息学是一种生物信息学.
背景情况:
- 传统的分子查在计算上昂贵,并且依赖于完整的3D蛋白质结构.
- 缺乏可靠的3D结构阻碍了药物设计.
- 现有的方法在速度和可访问性方面面临限制.
研究的目的:
- 推出CoDrug,这是一个用于人工智能辅助药物发现的多式融合框架.
- 通过整合文本和结构数据来克服传统方法的局限性.
- 为了实现文本驱动的虚拟选和分子优化.
主要方法:
- CoDrug使用多式联接技术,将文本数据 (SciBERT,ChemFormer) 和结构数据 (ESM,ChemFormer) 结合起来.
- 对比式学习将文本和结构嵌入在一个共享的潜在空间中对齐.
- 两个融合策略:文本-蛋白质序列和文本-化合物结构.
主要成果:
- 与最先进的基线相比,CoDrug 实现了与竞争力相匹配或更高的性能.
- 该框架在零射击设置和3D结构数据不完整的情况下表现出色.
- 表现出强大的概括性和表现表现力.
结论:
- CoDrug 为早期药物发现提供了一个可扩展和灵活的范式.
- 自然语言界面降低了化学家的技术障碍.
- 加快了语言驱动假设与结构导向分子设计的整合.
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