Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …

Automating retrosynthesis with artificial intelligence expedites organic chemistry research in
digital laboratories. However, most existing deep-learning approaches are hard to explain …

The year 2021 marks the 125th anniversary of the Bayer Chemical Research Laboratory in
Wuppertal, Germany. A significant number of prominent small-molecule drugs, from Aspirin …

We investigated the effect of different training scenarios on predicting the (retro) synthesis of
chemical compounds using text-like representation of chemical reactions (SMILES) and …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

The molecules of today—the medicines that cure diseases, the agrochemicals that protect
our crops, the materials that make life convenient—are becoming increasingly sophisticated …

The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus
valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user …

Highlights•GNN has attracted wide attention from the field of designing drug molecules.•The
applications of GNN in molecule scoring, molecule generation and optimization, and …

There is significant interest and importance to develop robust machine learning models to
assist organic chemistry synthesis. Typically, task-specific machine learning models for …