Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Drug discovery and development are among the most important translational science
activities that contribute to human health and wellbeing. However, the development of a new …

Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in
modeling graph-structured data. However, the primary focus has been on handling simple …