| Literature DB >> 35365231 |
Chong Lu1, Shien Liu1, Weihua Shi1, Jun Yu1, Zhou Zhou1, Xiaoxiao Zhang1, Xiaoli Lu1, Faji Cai1, Ning Xia2, Yikai Wang3.
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.Entities:
Keywords: Chemical space exploration; De novo drug design; Deep learning; Fragment-based drug discovery; PHGDH
Year: 2022 PMID: 35365231 PMCID: PMC8973791 DOI: 10.1186/s13321-022-00598-4
Source DB: PubMed Journal: J Cheminform ISSN: 1758-2946 Impact factor: 5.514