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 …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

Molecule representation learning (MRL) has been extensively studied and current methods
have shown promising power for various tasks, eg, molecular property prediction and target …

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 …

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 …

Designing efficient synthetic routes for a target molecule remains a major challenge in
organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful …

AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making
the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its …

The central challenge in automated synthesis planning is to be able to generate and predict
outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely …