对进化药物设计的基准测试分子突变运营者
Raúl Acosta Murillo1, Patricio Adrián Zapata-Morin1, José Carlos Ortiz-Bayliss2
1Department of Microbiology and Immunology, School of Biological Sciences, Universidad Autónoma de Nuevo León, Pedro de Alba SN, San Nicolás de los Garza 66455, Nuevo Leon, Mexico.
International journal of molecular sciences
|December 11, 2025
概括
选择正确的分子突变策略是人工智能驱动药物设计的关键. 基于图形的遗传算法提供了高的有效性和效率,而其他算法则以不同的方式影响分子复杂性和生物活性.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 遗传算法是药物设计的强大工具.
- 分子突变操作者对于探索化学空间至关重要.
- 优化这些运营商可以提高人工智能驱动的药物发现效率.
研究的目的:
- 为了比较药物设计中的遗传算法中的五种分子突变策略.
- 评估它们的计算效率,分子有效性和对复杂性的影响.
- 评估它们对生物活性和结构性保护的影响.
主要方法:
- 评估了基于图形的遗传算法,基于图形的生成模型,SmilesClickChem,SELFIES令牌和SMILES令牌突变.
- 评估计算效率,分子有效性,复杂性和结构保护.
- 分析了pIC50强度和生物活性中的突变诱导的变化.
主要成果:
- 基于图形的遗传算法显示出最高的分子有效性 (96.5%) 和效率.
- SmilesClickChem和基于图形的生成模型增加了分子复杂性.
- 自拍标记显著改变了生物活性,特别是针对SRC向的分子.
结论:
- 选择突变策略会影响药物设计结果,平衡有效性,多样性和成本.
- 基于图形的遗传算法适用于快速发现药物.
- 这些发现指导了分子生成和候选物选择的进化算法的改进.
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