Normalizing flows are a class of deep generative models that are especially interesting for
modeling probability distributions in physics, where the exact likelihood of flows allows …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-
based drug discovery. Naturally, molecules can be represented as 2D topological graphs or …

Transition state search is key in chemistry for elucidating reaction mechanisms and
exploring reaction networks. The search for accurate 3D transition state structures, however …

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 …

Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …

Theoretical and empirical comparisons have been made to assess the expressive power
and performance of invariant and equivariant GNNs. However, there is currently no …

Many processes in biology and drug discovery involve various 3D interactions between
molecules, such as protein and protein, protein and small molecule, etc. Given that different …

Large molecular representation models pre-trained on massive unlabeled data have shown
great success in predicting molecular properties. However, these models may tend to overfit …

Molecular Representation Learning (MRL) has proven impactful in numerous biochemical
applications such as drug discovery and enzyme design. While Graph Neural Networks …