Accuracy (of Prediction)

The non-specialist might have assumed that the main objective of a QSAR study is to predict whether an untested compound will be active or inactive (or to do virtual screening, i.e., predictions about a whole virtual library of compounds). In practice, much work has been devoted to “explanatory” QSAR, relating changes in molecular structure to changes in activity, and only recently has there been considerable interest in predictivity; QSAR is now being used for virtual screening, to find biologically active molecules. There are many reasons why models fail [1,2]: bad data, bad methodology, inappropriate descriptors, domain inapplicability [3], etc. In this article we can address only a few of the issues. Vendors are supplying models that may or may not be applicable to a corporate virtual library [2] and many (in-house approved) models are now available to non-experts on corporate intranets. How are these users to judge applicability?