  Structure-based drug design is drawing growing attentions in computer-aided
drug discovery. Compared with the virtual screening approach where a
pre-defined library of compounds are computationally screened, de novo drug
design based on the structure of a target protein can provide novel drug
candidates. In this paper, we present a generative solution named TamGent
(Target-aware molecule generator with Transformer) that can directly generate
candidate drugs from scratch for a given target, overcoming the limits imposed
by existing compound libraries. Following the Transformer framework (a
state-of-the-art framework in deep learning), we design a variant of
Transformer encoder to process 3D geometric information of targets and
pre-train the Transformer decoder on 10 million compounds from PubChem for
candidate drug generation. Systematical evaluation on candidate compounds
generated for targets from DrugBank shows that both binding affinity and
drugability are largely improved. TamGent outperforms previous baselines in
terms of both effectiveness and efficiency. The method is further verified by
generating candidate compounds for the SARS-CoV-2 main protease and the
oncogenic mutant KRAS G12C. The results show that our method not only
re-discovers previously verified drug molecules , but also generates novel
molecules with better docking scores, expanding the compound pool and
potentially leading to the discovery of novel drugs.
