Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …

Proteolysis-targeting chimeras (PROTACs) have emerged as effective tools to selectively
degrade disease-related proteins by using the ubiquitin-proteasome system. Developing …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

An outstanding challenge in deep learning in chemistry is its lack of interpretability. The
inability of explaining why a neural network makes a prediction is a major barrier to …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …

The prediction of compound properties from chemical structure is a main task for machine
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …