Systemic evolutionary chemical space exploration for drug discovery

Journal of Cheminformatics

卷:14

Ning Xia¹  Jun Yu²  Xiaoxiao Zhang²  Faji Cai²  Yikai Wang²  Xiaoli Lu²  Weihua Shi²  Chong Lu²  Shien Liu²  Zhou Zhou² 

[1] Chemical.AI;

[2] Keen Therapeutics Co., Ltd.;

关键词: Chemical space exploration;    Fragment-based drug discovery;    Deep learning;    De novo drug design;    PHGDH;   

DOI  :  10.1186/s13321-022-00598-4

来源: DOAJ

【 摘 要 】

Abstract Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

【 授权许可】

Unknown