Molecular Adversarial Generative Graph Network Model for Large-scale Molecular Networks

An understanding of the human innate immune response is essential to accelerate the progress and clinical trials of drugs and antibiotics. This requires efficient modelling of the molecular network. A lack of diverse and vast molecular data has been a major hurdle in molecular modelling research. Existing synthetic graph models such as variational auto encoders, Restricted Boltzmann Machines and auto regressive models are computationally expensive and experience inefficiencies due to their sequential architectural design. To address these issues this research paper introduces MAGNet - a Molecular Adversarial Graph Network based graph generative AI model to generate synthetic graph structures. MAGNet model architecture consists of two neural networks, a generator and discriminator which are involved in a minmax game to improve each other's performance. The model is trained on the graphs generated from unlabelled SMILES strings. The MAGNet architecture enabled the model to converge in fewer epochs and the generator was able to produce an accurate synthetic graph which is indistinguishable by the discriminator. The experimental results establish that the Graph Adversarial Network model is able to converge the discriminator loss within the first 15 epochs of the training.