Finding the Rules for Successful Drug Optimisation

Abstract

Drug discovery is widely recognised as a process of multi-parameter optimisation. The aim? Finding compounds that meet a profile of many property criteria. These criteria depend on the ultimate therapeutic goal of the project and are typically chosen based on the subjective opinion of the project team.  However, analysing historical data can help guide the determination of the most appropriate profile.

In this article we discuss computational approaches, described as rule induction, which enable an objective analysis of complex data to identify multi-parameter rules that distinguish compounds likely to be successful for a project’s goal. The resulting rules are interpretable and modifiable. This allows experts to understand and adjust them based on their knowledge of the underlying biology and chemistry. Furthermore, the importance of each property criterion can be identified, allowing scientists to focus experimental resources on generating the most critical data necessary to make effective compound prioritisation decisions. We illustrate this method with two applications. Firstly, determining rules for simple, calculated properties for orally administered drugs, and comparing these with previously published measures of ‘drug-likeness’. Next, exploring experimental target inhibition data to find rules to reduce the risk of hepatotoxicity and cardiotoxicity.