Property prediction models have been developed for several decades with varying degrees of performance and complexity, from the group contribution-based methods to molecular simulations-based methods. An interesting issue in this area is finding an appropriate representation molecules inherently suited property modeling problem. Here, we propose Grammar2vec, a SMILES grammar-based framework gen...

Abstract Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision making. However, models can produce badly calibrated it is therefore crucial detect handle carefully. work we extend a message passing neura...