Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics

Data-driven inverse molecular design (IMD) has attracted significant attention in recent years. Despite the remarkable progress, existing IMD methods lag behind in terms of trustworthiness, as indicated by their misalignment to the ground-truth function that models the molecular dynamics. Here, we propose TrustMol, an IMD method built to be trustworthy by inverting a reliable molecular property predictor. TrustMol first constructs a latent space with a novel variational autoencoder (VAE) and trains an ensemble of property predictors to learn the mapping from the latent space to the property space. The training samples for the ensemble are obtained from a new reacquisition method to ensure that the samples are representative of the latent space. To generate a desired molecule, TrustMol optimizes a latent design by minimizing both the predictive error and the uncertainty quantified by the ensemble. As a result, TrustMol achieves state-of-the-art performance in terms of IMD accuracy, and more importantly, aligned with the ground-truth function which indicates trustworthiness.