AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS

Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design. Here we propose augmentation of deep generative models with topological data analysis (TDA) representations, known as persistence images, for robust encoding of 3D molecular geometry.