基于遗传算法的受体连接体:一种由遗传算法引导的生成模型,用于在采样化学空间中提高分子的新性和药物相似性
Mingyang Wang1,2, Zhengjian Wu1,3, Jike Wang1,2
1College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China.
Journal of chemical information and modeling
|February 1, 2024
概括
一个新的基因算法引导的深度学习模型,GARel,产生了具有独特支架的新型药物样分子. 这种方法改进了现有的方法,为药物发现中的de novo分子设计提供了有希望的方向.
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
- 分子建模分子建模
背景情况:
- 基于深度学习 (DL) 的de novo分子设计正在迅速发展.
- 目前的DL模型经常产生缺乏新型支架或类似药物的特性的分子.
- 现有的评估指标可能无法完全捕捉这些局限性.
研究的目的:
- 引入GARel (基于遗传算法的受体-连接体相互作用发生器),这是一个新的框架,用于新的分子设计.
- 训练一个基于DL的生成模型,能够用新的支架生产类似药物的分子.
- 解决当前生成模型的新性和药物相似性的局限性.
主要方法:
- 开发了GARel,一种由遗传算法指导的生成模型.
- 利用密集的网络,以基于新型脚手架和类似药物的特性进行高效的模型训练.
- 应用GARel来设计针对AA2AR,EGFR和SARS-CoV-2目标的抑制剂.
主要成果:
- 由GARel生成的分子表现出更多样化和新的支架.
- 产生的化合物具有理想的物理化学特性和有利的对接分数.
- GARel在分子新性和药物相似性之间取得了更好的平衡.
结论:
- GARel代表了基于DL的新分子设计的重大进步.
- 该模型显示了产生具有独特化学结构的药物样分子的前景.
- 这种方法对加速药物发现过程具有潜在的影响.
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