Unsupervised learning, in contrast, investigates the concealed organisation of unlabeled data without human involvement ( ) (Figure ). With the utilisation of ML in the analysis and resolution of intricate biological data networks, such as (Figure ), ML algorithms have the potential to unveil latent patterns and correlations within the data that may elude human perception. Therefore, ML can potentially enhance comprehension and therapeutic approaches for various diseases. ML has significantly advanced target prediction. Besides target prediction, ML has demonstrated its effectiveness in indication prioritisation, biomarker and target discovery, pharmacokinetics prediction, drug-like molecule creation, clinical trial design, and drug-target interaction. The examples demonstrate that ML has great potential not only in target pre-dictions but also for other steps in drug discovery.