引导多目标生成AI以增强基于结构的药物设计
Amit Kadan1, Kevin Ryczko1, Erika Lloyd1
1SandboxAQ Palo Alto CA USA amit.kadan@sandboxaq.com kevin.ryczko@sandboxaq.com takeshi.yamazaki@sandboxaq.com.
Chemical science
|June 4, 2025
概括
使用IDOLpro的生成人工智能,创造出具有卓越结合亲和力和合成能力的新药分子. 这种人工智能平台通过超越现有方法和对优化药物类化合物的虚拟查来加速药物发现.
科学领域:
- 药物发现中的计算化学和人工智能.
背景情况:
- 目前的生成人工智能模型努力创建具有药物设计所需物理化学性质的分子.
- 基于结构的药物设计需要优化多个参数,如结合亲和力和合成可访问性.
研究的目的:
- 介绍IDOLpro,一个新的生成化学AI平台,用于基于结构的药物设计.
- 为了证明IDOLpro能够产生具有优化结合亲和力和合成可访问性的分子的能力.
主要方法:
- IDOLpro将扩散模型与多目标优化相结合.
- 微分得分函数指导扩散模型的潜在变量来探索化学空间.
- 该平台在中产生新型连接体,优化多种物理化学性质.
主要成果:
- 在基准集上,IDOLpro产生的配体与最先进的方法相比,具有10-20%的更高的结合亲和力.
- 该平台产生了更多类似药物的分子,具有更好的合成可访问性得分.
- IDOLpro超越了详尽的虚拟选,更快,更便宜地产生具有优越结合亲和度和合成可访问性的分子.
- 在一组实验复合物的测试中,IDOLpro产生了比实验观察到的配体具有更高的结合亲和度的分子.
结论:
- IDOLpro代表了药物发现的生成化学的重大进步.
- 该平台可以通过容纳各种评分功能 (例如,ADME-Tox) 来加快击中寻找,击中到领先,以及领先优化.
- IDOLpro能够产生具有改进性质的新药候选者,可能会彻底改变药物发现管道.
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