Figure shows the impact of chemoinformatics and AI approaches that have been around at all stages of the drug-discovery process, from target selection to the pharmacovigilance of approved drugs. The current technologies allow the use of a huge diversity of data (atomic, chemical, biological, clinical, and post market data) in combination with different approaches (e.g., data fusion, clustering, ML, DL, pairwise comparisons, dimensionality reduction, and networks) to classify, predict, or recognize patterns in order to explain or decode new knowledge, opening up a vast repertoire of possible combinations of methods that are applicable to the solution of drug-design problems. Combination of data, approaches, and type of results used in drug design.