微尺度化学
化学
化学空间
药物发现
模块化设计
过程(计算)
帧(网络)
纳米技术
组合化学
计算机科学
生化工程
生物化学
工程类
程序设计语言
数学教育
电信
材料科学
数学
作者
Cara E. Brocklehurst,Eva Altmann,Corentin Bon,Holly J. Davis,David W. Dunstan,Peter Ertl,Carol Ginsburg‐Moraff,Jonathan E. Grob,Daniel J. Gosling,Guillaume Lapointe,Alexander N. Marziale,Heinrich Mues,Marco Palmieri,Sophie Racine,Richard I. Robinson,Clayton Springer,K. Tan,William Ulmer,R. Wyler
标识
DOI:10.1021/acs.jmedchem.3c02029
摘要
We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.
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