德诺沃分子设计中的多种打击:基于多样性的目标定向发电机的比较
Philipp Renz1, Sohvi Luukkonen2, Günter Klambauer2
1Johannes Kepler University Linz, Altenbergerstraße 69, Linz, AT 4040, Austria.
Journal of chemical information and modeling
|July 19, 2024
概括
药物发现的生成人工智能可以创建类似的分子. 本研究介绍了基于多样性的评估和约束,以改善分子生成,优于其他方法.
科学领域:
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
背景情况:
- 生成型人工智能模型越来越多地用于发现新药候选药物.
- 现有的分子发生器经常产生非常相似的分子,并过度依赖得分功能.
- 这限制了它们捕获所需的潜在分子性质的能力.
研究的目的:
- 为了解决当前人工智能驱动的分子生成的局限性,特别是过度强调分子相似性和评分函数.
- 引入基于多样性的评估和计算约束,以改善分子生成.
- 为了比较不同的生成模型在生产多样化和可取的分子中的性能.
主要方法:
- 使用基于多样性的评估与#Circles指标.
- 嵌入了对得分函数调用和计算时间的限制.
- 基于SMILES的自回归模型与基于图形的模型和遗传算法进行了比较.
主要成果:
- 基于SMILES的自回归模型在生成多样化的分子集方面表现出卓越的性能.
- #Circles指标有效地增强了产生的分子中的多样性.
- 考虑计算约束改善了分子发生器的实际应用.
结论:
- 基于多样性的评估和计算约束对于有效的分子生成至关重要.
- 基于SMILES的自回归模型对发现各种候选药物的显著前景.
- 这种方法纠正了当前在药物发现中的生成人工智能工具的关键缺陷.
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