Highlights•Review of the state-of-the-art in uncertainty quantification in drug
design.•Examples from drug-design settings are provided.•Impact on decision making is …

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the
drug discovery process, but it is important in order to rationalise phenotypic findings and to …

In pharmaceutical research, compounds are optimized for metabolic stability to avoid a too
fast elimination of the drug. Intrinsic clearance (CLint) measured in liver microsomes or …

Immense effort has been exerted in the materials informatics community towards enhancing
the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) …

Artificial intelligence (AI) has undergone rapid development in recent years and has been
successfully applied to real-world problems such as drug design. In this chapter, we review …

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of
potential drug targets play an important role in small-molecule drug discovery. The aim of …

The problem of human trust is one of the most fundamental problems in applied artificial
intelligence in drug discovery. In silico models have been widely used to accelerate the …

Estimating protein targets of compounds based on the similarity principle—similar molecules
are likely to show comparable bioactivity—is a long-standing strategy in drug research …

Measurements of protein–ligand interactions have reproducibility limits due to experimental
errors. Any model based on such assays will consequentially have such unavoidable errors …

Ionic conductivity is a crucial parameter in the electrochemical applications of ionic liquids
(ILs). Conventional methods to obtain this parameter, ie, experimental measurements or …