A variety of machine learning methods such as naive Bayesian, support vector machines and
more recently deep neural networks are demonstrating their utility for drug discovery and …

An international security conference explored how artificial intelligence (AI) technologies for
drug discovery could be misused for de novo design of biochemical weapons. A thought …

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to
form qualitative or semi‐quantitative relations between molecular structure and activity in …

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the
most severe disruptions to societies and economies since the Great Depression. Massive …

Highlights • We describe our prior efforts in open drug discovery for Ebola and Zika virus. •
We summarize the current literature for severe acute respiratory syndrome coronavirus 2 (…

Understanding the development of a scientific approach is a valuable exercise in gauging
the potential directions the process could take in the future. The relatively short history of …

In an environment driven to find the next blockbuster drug, failure years into a project should
not be an option. Recent studies have shown that poor absorption, distribution, metabolism, …

Machine learning methods have been applied to many data sets in pharmaceutical
research for several decades. The relative ease and availability of fingerprint type molecular …

One approach to speed up drug discovery is to examine new uses for existing approved
drugs, so-called ‘drug repositioning’ or ‘drug repurposing’, which has become increasingly …

The protein product of the human ether-a-go-go gene (hERG) is a potassium channel that
when inhibited by some drugs may lead to cardiac arrhythmia. Previously, a three-…