DrugGEN

TÜBİTAK-BİDEB 2247-A National Outstanding Researchers Program 2020

Discovering novel, efficacious small molecules remains a slow and failure-prone process, given the enormity of chemical space. In this project, we developed DrugGEN, a generative AI system for de novo, target-specific small-molecule design against drug-resistant cancers. Molecules were represented as graphs and modelled with a graph-transformer-based generative adversarial network conditioned on target context. The model ingested curated training corpora and was optimised to maximise chemical validity, uniqueness/novelty, and predicted bioactivity while satisfying multi-parameter property profiles. We trained and evaluated the system on expert-selected cancer targets, demonstrating high validity and novelty rates, with improved predicted affinity compared to strong baselines. A prioritised subset of designs was advanced to medicinal chemistry; multiple compounds were synthesised successfully and exhibited measurable inhibitory activity on enzymatic assays and drug-resistant cancer cell lines in vitro. Prospective rounds incorporated feedback from docking, molecular dynamics, and assay results, yielding iterative gains in predicted potency and developability. We released an open-access codebase (https://github.com/HUBioDataLab/druggen) and a web service (https://huggingface.co/spaces/HUBioDataLab/DrugGEN) that enables life-science researchers to generate candidate molecular structures by specifying target proteins, facilitating rapid hypothesis generation and experimental triage. DrugGEN establishes a practical, target-conditioned molecular generation pipeline that couples graph-native modelling with property-aware constraints to deliver experimentally testable inhibitors for resistant oncology indications.