A Deep Generative Model for Molecular Graphs by Niloy Ganguly

Explore a deep generative model for molecular graphs in this 23-minute conference talk from the International Centre for Theoretical Sciences. Learn about designing random graph models using variational autoencoders to discover new, plausible drug-like molecules. Examine the limitations of current models and delve into NeVAE, a variational autoencoder for graphs. Understand the probabilistic encoder and decoder, their properties, and how they guarantee structural integrity. Discover how the training process is permutation invariant and efficient. Review experimental results showcasing a smooth, meaningful space of molecules and evaluate the model's performance using metrics for validity, novelty, and uniqueness. Investigate methods for predicting and optimizing molecule properties using sparse Gaussian processes and Bayesian optimization. Conclude with key takeaways and participate in a Q&A session to deepen your understanding of this innovative approach to molecular graph generation.