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.
