To aid this latter approach and overcome the absence of knowledge of the target or its structure, computational models may be developed using artificial intelligence (AI) and machine learning (ML) . Such approaches allow the activities of new compounds in a phenotypic-screening program to be predicted. For instance, matched molecular pair analysis and quantitative structure activity relationship (QSAR) models are commonly used in medicinal chemistry campaigns to determine the relationships between the physical and biological properties of a series of compounds. This information can then be used to guide the design of new active compounds. In those cases in which a target has been identified but its structure is not yet determined, a structural model may be developed based on a known close homolog of the target . This method allows for docking studies to be conducted to examine potential binding interactions that may occur in the actual target, thus guiding the lead optimisation process more effectively. Recent years have seen the increased use of computational methods such as these to aid the drug discovery process . For instance, there have been successes in the in silico target prediction of small molecules with activity against Mycobacterium tuberculosis .