SyntaLinker: automatic fragment linking with deep conditional transformer neural networks. Issue 31 (31st July 2020)

• Record Type: Journal Article
• Authors: Yang, Yuyao; Zheng, Shuangjia; Su, Shimin; Zhao, Chao; Xu, Jun; Chen, Hongming
• Is Part Of: Chemical science. Volume 11:Issue 31(2020)
• Page Start: 8312
• Page End: 8322
• Publication Date: 2020-07-31
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d0sc03126g
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Abstract : Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural networks. This state-of-the-art transformer can link molecular fragments automatically by learning from the knowledge of structures in medicinal chemistry databases ( e.g. ChEMBL database). Conventionally, linking molecular fragments was viewed as connecting substructures that were predefined by empirical rules. In SyntaLinker, however, the rules of linking fragments can be learned implicitly from known chemical structures by recognizing syntactic patterns embedded in SMILES notations. With deep conditional transformer neural networks, SyntaLinker can generate molecular structures based on a given pair of fragments and additional restrictions. Case studies have demonstrated the advantages and usefulness of SyntaLinker in FBDD.