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SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

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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.

Graphical abstract: SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

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Supplementary files

Article information


Submitted
04 Jun 2020
Accepted
21 Jul 2020
First published
22 Jul 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020, Advance Article
Article type
Edge Article

SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Y. Yang, S. Zheng, S. Su, C. Zhao, J. Xu and H. Chen, Chem. Sci., 2020, Advance Article , DOI: 10.1039/D0SC03126G

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