Highlights•Advances in artificial intelligence (AI) are modernising several aspects of our
lives.•The pharma industry is facing challenges to overcome the high attrition rates in drug …

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-
based drug design (SBDD) making a comeback while high-throughput screening (HTS) …

Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …

Analysis of macromolecular/small-molecule binding pockets can provide important insights
into molecular recognition and receptor dynamics. Since its release in 2011, the POVME …

Current pharmaceutical formulation development still strongly relies on the traditional trial-
and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming …

Since the first distribution of Molden in 1995 and the publication of the first article about this
software in 2000 work on Molden has continued relentlessly. A few of the many improved or …

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in
silico screening tools have gained considerable importance to archive, analyze and exploit …

Background: Quantitative and qualitative structure–activity relationships (QSARs) have been
used to understand chemical behavior for almost a century. The main source of QSAR …

To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease
2019 (COVID-19). Five databases were searched (December 2019–May 2020). We …

Due to its impact on society, the design of new drugs has the potential to interest a wide
audience, and provides a rare opportunity to introduce several concepts in chemistry and …