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扩展DNA编码图书馆使用生成化学和超大化合物目录的成功

Brandon Novy¹, Shu-Hang Lin¹, Devan J Shell¹

¹UNC Eshelman School of Pharmacy, Center for Integrative Chemical Biology and Drug Discovery, Chemical Biology and Medicinal Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

bioRxiv : the preprint server for biology

|November 19, 2025

概括

这项研究将DNA编码库 (DEL) 与人工智能 (AI) 结合起来,以发现类似药物的分子. 协同方法迅速将初始的DEL成功扩展到不同的,可购买的化合物,用于药物发现.

科学领域:

药物发现药物发现药物发现
计算化学计算化学
药用化学医学化学

背景情况:

用DNA编码的图书馆 (DEL) 提供了强大的打击识别,但可以受到化学空间和打击药物相似性的限制.
生成型人工智能可以探索化学空间,但难以合成,需要初始验证.

研究的目的:

开发一种结合DEL数据和人工智能的协同方法,以实现有效的扩展.
识别用于染色体读蛋白53BP1.1.的新型,类似药物和商用化合物.

主要方法:

利用验证的DEL数据来初始化和偏差一个人工智能驱动的虚拟选管道.
从超大化学图书馆扩展初始DEL的成功与de novo和可购买的化合物.
使用时间解析光共振能量转移 (TR-FRET) 试验验证已识别的化合物.

主要成果:

确定了来自Enamine REAL空间的53BP1的新型,商业上可用的结果.
三种化合物显示TR-FRET IC50值≤50μM;11种化合物显示IC50值≤100μM.
与DEL选择相比,AI提名的热点显示出更高的化学多样性,药物相似性和可购买性.

结论:

结合DEL和AI的方法为快速扩张提供了一个精简的平台.
这种方法可以有效地发现多样化,类似药物和易于获得的化合物.
协同战略克服了DEL和生成AI的局限性,以加速药物发现.

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