一种基于帕雷托算法和蒙特卡洛树搜索的多目标分子生成方法
Yifei Liu1, Yiheng Zhu2, Jike Wang1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|April 4, 2025
概括
新的帕雷托蒙特卡洛树搜索分子生成 (PMMG) 方法有效地优化了多种药物发现目标. 这种先进的算法可显著加速识别具有所需特性的新药候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 药物发现受到同时优化多个分子目标的挑战的阻碍.
- 目前的计算方法在处理四个以上的目标上是有限的,限制了分子设计的进步.
研究的目的:
- 引入一种新的方法,帕雷托蒙特卡洛树搜索分子生成 (PMMG),用于多目标分子设计.
- 克服高维客观空间中现有算法的局限性.
主要方法:
- 利用蒙特卡罗树搜索 (MCTS) 有效地探索化学空间并识别帕雷托前线.
- 采用简化分子输入线输入系统 (SMILES) 来进行分子表示.
- 应用PMMG以优化多种药物发现目标,包括结合亲缘关系和药物相似性.
主要成果:
- 在同时优化七个目标方面,PMMG取得了51.65%的成功率,超过现有方法的2.5倍.
- 证明了PMMG在产生对EGFR和HER2点具有高对接分数的分子的能力.
- 生成的分子具有有利的预测类似药物的特性.
结论:
- PMMG有效地解决了分子设计中多目标优化的挑战.
- 该方法显示了通过处理众多复杂的目标来加速药物发现管道的巨大潜力.
- PMMG代表了用于识别新药候选者的计算方法的实质性进步.
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