  Deep generative models have gained significant advancements to accelerate
drug discovery by generating bioactive chemicals against desired targets.
Nevertheless, most generated compounds that have been validated for potent
bioactivity often exhibit structural novelty levels that fall short of
satisfaction, thereby providing limited inspiration to human medicinal
chemists. The challenge faced by generative models lies in their ability to
produce compounds that are both bioactive and novel, rather than merely making
minor modifications to known actives present in the training set. Recognizing
the utility of pharmacophores in facilitating scaffold hopping, we developed
TransPharmer, an innovative generative model that integrates ligand-based
interpretable pharmacophore fingerprints with generative pre-training
transformer (GPT) for de novo molecule generation. TransPharmer demonstrates
superior performance across tasks involving unconditioned distribution
learning, de novo generation and scaffold elaboration under pharmacophoric
constraints. Its distinct exploration mode within the local chemical space
renders it particularly useful for scaffold hopping, producing compounds that
are structurally novel while pharmaceutically related. The efficacy of
TransPharmer is validated through two case studies involving the dopamine
receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably in the case of PLK1,
three out of four synthesized designed compounds exhibit submicromolar
activities, with the most potent one, IIP0943, demonstrating a potency of 5.1
nM. Featuring a new scaffold of 4-(benzo[b]thiophen-7-yloxy)pyrimidine, IIP0943
also exhibits high selectivity for PLK1. It was demonstrated that TransPharmer
is a powerful tool for discovery of novel and bioactive ligands.
