Multi-target drugs have raised considerable interest in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance issues. Prospective drug repositioning to treat comorbid conditions is an additional, overlooked application of multi-target ligands. While medicinal chemists usually rely on some version of the lock and key paradigm to design novel therapeutics, modern pharmacology has recognized that the long-term effects of a given drug on a biological system may depend not only on the specific ligand-target recognition events but also on the influence of the chronic administration of a drug on the cell gene signature. The design of multi-target agents also poses challenging restrictions on the either the topology or flexibility of the candidate drugs which are briefly discussed in the present article. Finally, computational strategies to approach the identification of novel multi-target agents are overviewed.

(2019). Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics. Expert Opinion on Drug Discovery. Ahead of Print.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.