Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …

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 …

Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …

Generative molecular design for drug discovery and development has seen a recent
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …

In recent years, investigations on molecular interaction mechanisms between food proteins
and ligands have attracted much interest. The interaction mechanisms can supply much …

Molecular simulation methods, such as molecular docking, molecular dynamic (MD)
simulation, and quantum chemical (QC) calculation, have become popular as …

A well-known limitation of existing molecular generative models is that the generated
molecules highly resemble those in the training set. To generate truly novel molecules that …

Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney
disease (CKD) and end-stage renal failure. Despite extensive research, there have been …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …