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

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 …

Transformer models coupled with a simplified molecular line entry system (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics …

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

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

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 …

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 …

Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

Graph neural networks have been widely used on modeling graph data, achieving
impressive results on node classification and link prediction tasks. Yet, obtaining an …

As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways
and intermediates for a target compound. The goal of artificial intelligence (AI)-aided …