Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

There have been numerous advances in the development of computational and statistical
methods and applications of big data and artificial intelligence (AI) techniques for computer …

Conventional drug discovery is long and costly, and suffers from high attrition rates, often
leaving patients with limited or expensive treatment options. Recognizing the overwhelming …

Chemical descriptors encode the physicochemical and structural properties of small
molecules, and they are at the core of chemoinformatics. The broad release of bioactivity …

Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular
properties of small molecules are increasingly deployed within the design–make–test …

Due to the heap of data sets available for drug discovery, modern drug discovery has taken
the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery …

Deep learning models have demonstrated outstanding results in many data-rich areas of
research, such as computer vision and natural language processing. Currently, there is a …

Machine-learning and deep-learning models have been extensively used in
cheminformatics to predict molecular properties, to reduce the need for direct …