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 …

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the
challenges in drug discovery. Although traditional AI techniques generally have high …

Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …

The use of sophisticated machine learning (ML) models, such as graph neural networks
(GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly …

Academic and pharmaceutical industry research are both key for progresses in the field of
molecular machine learning. Despite common open research questions and long-term …

Graph neural networks (GNNs) have received increasing attention because of their
expressive power on topological data, but they are still criticized for their lack of …

Graph neural networks (GNNs) recursively propagate signals along the edges of an input
graph, integrate node feature information with graph structure, and learn object …

Activity cliffs (AC) are formed by pairs of structural analogues that are active against the
same target but have a large difference in potency. While much of our knowledge about ACs …

Activity cliffs (ACs) are formed by pairs of structurally similar or analogous active small
molecules with large differences in potency. In medicinal chemistry, ACs are of high interest …

Highlights•Artificial intelligence (AI) is transforming the drug discovery process by providing
actionable insights from huge amount of data.•Deep-learning models, especially generative …