Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

This work introduces DiGress, a discrete denoising diffusion model for generating graphs
with categorical node and edge attributes. Our model utilizes a discrete diffusion process …

Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data
Commons is an initiative to access and evaluate AI capability across therapeutic modalities …

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

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

Application of deep learning techniques for de novo generation of molecules, termed as
inverse molecular design, has been gaining enormous traction in drug design. The …

The last few years have seen the development of numerous deep learning-based protein–
ligand docking methods. They offer huge promise in terms of speed and accuracy. However …