Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …

Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …

Contemporary materials discovery requires intricate sequences of synthesis, formulation,
and characterization that often span multiple locations with specialized expertise or …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

Geometric deep learning enables the encoding of physical symmetries in modeling 3D
objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks …

Abstract Molecular Representation Learning (MRL) has emerged as a powerful tool for drug
and materials discovery in a variety of tasks such as virtual screening and inverse design …

The recent explosion of performance of large language models (LLMs) has changed the
field of Natural Language Processing (NLP) more abruptly and seismically than any other …

Lack of rigorous reproducibility and validation are significant hurdles for scientific
development across many fields. Materials science, in particular, encompasses a variety of …

Graph diffusion equations are intimately related to graph neural networks (GNNs) and have
recently attracted attention as a principled framework for analyzing GNN dynamics …