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 …

In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with
artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction …

As the enzyme toolbox for biocatalysis has expanded, so has the potential for the
construction of powerful enzymatic cascades for efficient and selective synthesis of target …

Artificial intelligence and machine learning have demonstrated their potential role in
predictive chemistry and synthetic planning of small molecules; there are at least a few …

Retrosynthetic planning is a critical task in organic chemistry which identifies a series of
reactions that can lead to the synthesis of a target product. The vast number of possible …

Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …

Automated Synthesis Planning has recently re-emerged as a research area at the
intersection of chemistry and machine learning. Despite the appearance of steady progress …

Organic synthesis remains a major challenge in drug discovery. Although a plethora of
machine learning models have been proposed as solutions in the literature, they suffer from …

Molecule optimization is a critical step in drug development to improve the desired
properties of drug candidates through chemical modification. We have developed a novel …

Retrosynthesis consists of breaking down a chemical compound recursively step-by-step
into molecular precursors until a set of commercially available molecules is found with the …