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 …

We present a comprehensive analysis of all ring systems (both heterocyclic and
nonheterocyclic) in clinical trial compounds and FDA-approved drugs. We show 67% of …

Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Drug development is one of the most significant processes in the pharmaceutical industry.
Various computational methods have dramatically reduced the time and cost of drug …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Due to the massive data sets available for drug candidates, modern drug discovery has
advanced to the big data era. Central to this shift is the development of artificial intelligence …

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions,
mixtures, and materials settings. It therefore forms the basis for all molecular dynamics …