Learning to learn how to design drugs

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A key step in developing a new drug is to learn quantitative structure activity relationships (QSARs). These are mathematical functions that predict how well chemical compounds will act as drugs. QSARs are used to guide the synthesis of new drugs.The current situation is:1) There is a vast range of approaches to learning QSARs.2) It is clear from theory and practice that the best QSAR approach depends on the type of problem.3) Currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. We therefore propose to make a step-change in QSAR research. We will utilise newly available public domain chemoinformatic databases, and in-house datasets, to systematically run extensive comparative QSAR experiments. We will then generalise these results to learn which target-type/ compound-type/ compound-representation /learning-method combinations work best together. We do not propose to develop any new QSAR method. Rather, we will learn how to better apply existing QSAR methods. This approach is called "meta-learning", using machine learning to learn about QSAR leaning. We will make the knowledge we learn publically available to guide and improve future QSAR learning.