Novel Molecules Generation Using Graph Generative Adversarial Networks

De novo molecular generation remains a central challenge in drug discovery due to the vastness of chemical space and the difficulty of generating molecules that are simultaneously valid, diverse, and property-aware. While graph-based generative models have improved structural validity over sequence-based approaches, many existing methods suffer from mode collapse, limited controllability, and unstable reinforcement learning dynamics. In this work, we propose NovMolG-GAN, a graph-based generative adversarial framework that integrates graph attention mechanisms, a learnable reward network, mode-seeking regularization, and proximal policy optimization-based reinforcement learning to address these limitations. The proposed model enables stable training while explicitly encouraging molecular validity, novelty, diversity, and drug-likeness. Experiments on the ChEMBL-35 dataset demonstrate that NovMolG-GAN achieves high validity (99.6%), high novelty (99.4%) strong uniqueness (88.5%), and competitive quantitative drug-likeness scores under unconstrained generation. Furthermore, a conditional generation ablation incorporating synthetic accessibility as an additional reward illustrates the controllability of the framework and its ability to steer generation toward synthesis-feasible regions of chemical space. These results highlight the potential of NovMolG-GAN as a flexible and extensible foundation for goal-directed molecular design.