  Discovering novel drug candidate molecules is one of the most fundamental and
critical steps in drug development. Generative deep learning models, which
create synthetic data given a probability distribution, offer a high potential
for designing de novo molecules. However, for them to be useful in real-life
drug development pipelines, these models should be able to design drug-like and
target-centric molecules. In this study, we propose an end-to-end generative
system, DrugGEN, for the de novo design of drug candidate molecules that
interact with intended target proteins. The proposed method represents
molecules as graphs and processes them via a generative adversarial network
comprising graph transformer layers. The system is trained using a large
dataset of drug-like compounds and target-specific bioactive molecules to
design effective inhibitory molecules against the AKT1 protein, which is
critically important in developing treatments for various types of cancer. We
conducted molecular docking and dynamics to assess the target-centric
generation performance of the model, as well as attention score visualisation
to examine model interpretability. Results indicate that our de novo molecules
have a high potential for interacting with the AKT1 protein at the level of its
native ligands. Using the open-access DrugGEN codebase, it is possible to
easily train models for other druggable proteins, given a dataset of
experimentally known bioactive molecules.
