Machine learning resources for drug design

Artificial intelligence and machine learning techniques have paved the way for the advent of automated strategies enabling the faster discovery of new bioactive molecules and the assessment of toxicological human-health endpoints. Specifically, the continuous updating of large-sized databases containing high-quality experimental data has allowed the derivation of predictive models with unprecedented accuracy levels whose applications are strongly pursued for scientific as well as regulatory purposes. This chapter offers a general overview of the most representative applications of machine learning techniques in drug design and surveys some explicative case studies to make users confident with new in silico tools helpful for the early stages of the drug discovery process.