Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …

Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …