整合生成预训练的变压器和遗传算法,以实现高效和多样化的分子生成
Chengcheng Xu1, Chen Zeng1, Xi Yang1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.
Molecular informatics
|August 5, 2025
概括
我们开发了一种用于药物设计的新型化合物构造模型 (CCMol),将生成预训练变压器 (GPT) 和遗传算法 (GA) 结合起来. CCMol产生多样化,有效的分子结构,改善早期药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 分子生成模型对于加速药物开发至关重要.
- 当前的模型提供了可解释性差的高性能 (深度学习) 或具有有限性能的更好的可解释性 (启发式算法).
研究的目的:
- 引入一种创新的分子生成模型,CCMol,集成生成预训练变压器 (GPT) 和遗传算法 (GA).
- 通过混合方法实现有效和创新的分子结构.
- 通过扩大化学有效性来增强早期药物发现.
主要方法:
- 使用基于结构的药物设计,包括连接体和蛋白质初级结构.
- 集成的GPT用于初始分子生成.
- 用GA来代优化物理化学和生物特性.
- 通过产生向GLP1,WRN和JAK2蛋白质的分子来验证模型.
主要成果:
- 在多个指标上,CCMol在多个指标上表现出与先进模型相当的性能.
- 在结构多样性和与药物相关的特性方面表现优于基线模型.
- 成功生成了针对关键疾病蛋白质的新型候选药物分子.
结论:
- CCMol为计算机辅助药物设计中分子生成提供了一种强大而可解释的方法.
- 该模型显示了开发新型和有效药物候选者的巨大潜力.
- CCMol特别适合在早期药物发现中增强化学多样性.
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