AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

08 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 (AutoLinker) 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 AutoLinker 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 AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


Fragment Linking
Deep generative models
Conditional Transformer model

Supplementary materials

Autolinker-Supporting Information


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