将药物化学专业知识与深度学习相结合,用于自动化分子优化
Li Liang1, Xinyi Yang1, Boheng Wan1,2
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
Journal of medicinal chemistry
|January 30, 2026
概括
本研究介绍了AutoOptimizer,这是一个AI平台,利用药物化学专业知识和深度学习来增强分子优化. 它成功地确定了FGFR4和HPK1的强有力的候选药物,加速了药物发现.
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:
- 药物化学家的专业知识对于成功的化合物优化至关重要.
- 现有的分子优化策略往往是有限的.
- 需要系统的,数据驱动的方法来扩展优化策略.
研究的目的:
- 开发一个自动平台,AutoOptimizer,用于分子优化.
- 创建一个新的框架,MolOpt,利用图表深度学习和专家知识.
- 建立第一个深度学习生成的分子优化策略数据库.
主要方法:
- 从文献中策划了 ~9000 个分子优化策略.
- 使用图形深度学习开发了MolOpt框架.
- 将专家知识和MolOpt集成到AutoOptimizer平台中.
- 对FGFR4和HPK1抑制剂进行的案例研究.
主要成果:
- 确定了针对FGFR4和HPK1.1的强效抑制剂M8和M9.
- 取得了显著的改善:FGFR4的77.6倍和HPK1.6的51.6倍.
- 展示了AutoOptimizer在领先优化中的实际应用和有效性.
结论:
- 自动优化器代表了一种新的AI驱动的分子优化方法.
- 该平台加速了率优化,并推动了药物发现.
- 这项工作建立了一个基于药物化学专业知识的独特数据库.
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