Drug discovery is a process of multiparameter optimisation, with the objective of finding compounds that achieve multiple, project-specific property criteria. These criteria are often based on the subjective opinion of the project team, but analysis of historical data can help to find the most appropriate profile. Computational 'rule induction' approaches enable an objective analysis of complex data to identify interpretable, multiparameter rules that distinguish compounds with the greatest likelihood of success for a project. Each property criterion highlights the most critical data that enable effective compound prioritisation decisions. We illustrate this with two applications: determining rules for simple, drug-like properties; and exploring experimental target inhibition data to find rules to reduce the risk of toxicity.
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