Research from Faculty of Medicine, University of Porto, Porto, Portugal, Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, Porto, Portugal, Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal, Faculty of Health Sciences, University Fernando Pessoa, Porto, Portugal, SIGIL Scientific Enterprises, Dubai, UAE, and MedFacts Lda., Lisbon, Portugal has created MedGAN, a deep learning model utilizing Wasserstein Generative Adversarial Networks and Graph Convolutional Networks.
The model aims to generate novel quinoline scaffold molecules by working with intricate molecular graphs. The study found impressive results with the best model developing 25% valid molecules, while 62% were fully connected, 92% were quinolines, and 93% were unique.
Preserving important properties such as chirality, atom charge, and favorable drug-like attributes, the model generated 4831 fully connected and unique quinoline molecules not present in the original training dataset. The study optimizes GAN with GCN for molecule design and highlights the potential of generative AI in drug discovery.