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

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

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

This paper studies unsupervised/self-supervised whole-graph representation learning,
which is critical in many tasks such as molecule properties prediction in drug and material …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance on graph-
based tasks. The key idea for GNNs is to obtain informative representation through …

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 …

The sustainable production of chemicals from renewable, nonedible biomass has emerged
as an essential alternative to address pressing environmental issues arising from our heavy …

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 …

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 …

Finding synthesis routes for molecules of interest is essential in the discovery of new drugs
and materials. To find such routes, computer-assisted synthesis planning (CASP) methods …