Towards the inverse design of molecules with targeted quantum-mechanical properties

Reconstructing the molecular structures that matches a given set of quantum-mechanical (QM) properties is a fundamental task in the pursuit of discovering advanced molecular materials or novel pharmaceuticals. In this regard, generative models have been proven to be a valuable tool, e.g., by combining variational autoencoders (VAEs) together with a mapping between latent and property space to obtain continuous representations of molecules, allowing the generation of novel chemical compounds. However, most of these models work with SMILES representations and present limited mapping between properties and molecular structures. Thus, in the present work, we investigate the impact of including diverse global and local QM properties on the mapping between latent and property space employing a VAE on 3D geometric and/or electronic representations. In doing so, we use the QM7-X dataset which includes 42 QM properties for ~4.2 million (equilibrium and non-equilibrium) primarily organic molecular structures. The effectiveness of considering non-equilibrium structures as data augmentation tool is also studied. We expect our findings to give insights into the inverse design process of molecules with targeted and diverse QM properties.