Introduction: Drug discovery is the process through which potential new compounds are identified by means of biology, chemistry, and pharmacology. Due to the high complexity of genomic data, AI techniques are increasingly needed to help reduce this and aid the adoption of optimal decisions. Phenotypic prediction is of particular use to drug discovery and precision medicine where sets of genes that predict a given phenotype are determined. Phenotypic prediction is an undetermined problem given that the number of monitored genetic probes markedly exceeds the number of collected samples (from patients). This imbalance creates ambiguity in the characterization of the biological pathways that are responsible for disease development. Areas covered: In this paper, the authors present AI methodologies that perform a robust deep sampling of altered genetic pathways to locate new therapeutic targets, assist in drug repurposing and speed up and optimize the drug selection process. Expert opinion: AI is a potential solution to a number of drug discovery problems, though one should, bear in mind that the quality of data predicts the overall quality of the prediction, as in any modeling task in data science. The use of transparent methodologies is crucial, particularly in drug repositioning/repurposing in rare diseases.
Keywords: Drug design; artificial intelligence; drug discovery; phenotype prediction.