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SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

revised on 22.05.2020, 01:21 and posted on 22.05.2020, 07:11 by Yuyao Yang, Shuangjia Zheng, Shimin Su, Jun Xu, Hongming Chen
Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (SyntaLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our SyntaLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that SyntaLinkercan be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


Email Address of Submitting Author


Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University



ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest