使用强化学习和基于结构的药物设计来识别纳米分子腺A2A受体配体
Morgan Thomas1, Pierre G Matricon2, Robert J Gillespie2
1Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
我们开发了一种新的AI方法,将化学语言模型和基于结构的药物设计结合起来,以创建新的药物分子. 这种方法成功地确定了具有高结合活性和新型化学结构的有力腺A2A受体配体.
科学领域:
- 人工智能在药物发现中的作用
- 计算化学和分子建模.
- 药品化学和药理学 药品化学和药理学
背景情况:
- 生成性化学语言模型 (CLMs) 在新药设计的分子表示方面表现出色.
- 基于结构的药物设计 (SBDD) 原则对于优化配体-蛋白相互作用至关重要.
- 将CLM与SBDD集成可以加速发现新型配体.
研究的目的:
- 将SBDD原则与CLM集成,以便从蛋白质结构直接生成新的配体.
- 在没有先前的化学知识的情况下,探索氨酸A2A受体配体的新化学空间.
- 有效地优化多个设计目标,包括蛋白质 - 配体互补性.
主要方法:
- 利用基于结构的药物设计 (SBDD) 的生成化学语言模型 (CLMs).
- 采用了Augmented Hill-Climb算法进行多目标优化.
- 进行实验验证,包括结合试验和联合结晶.
主要成果:
- 产生了针对腺A2A受体的新型小分子配体.
- 实现了88%的约束性命中率,其中50%显示功能活动.
- 确定了三种纳米分子配体和两种新化学型,其中两种通过联合晶体学证实.
结论:
- 综合人工智能SBDD方法成功生成了新的高亲和度连接体.
- 该方法访问商业新型化学空间,并关闭AI SBDD循环.
- 同结晶学阐明了结合机制,使得代的设计改进成为可能.
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