The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy)

A general feeling of disillusionment with QSAR has settled across the modeling community in recent years. Most practitioners seem to agree that QSAR has not fulfilled the expectations set for its ability to predict biological activity. Among the possible reasons that have been proposed recently for this disappointment are chance correlation, rough response surfaces, incorrect functional forms, and overtraining. Undoubtedly, each of these plays an important role in the lack of predictivity seen in most QSAR models. Likely to be just as important is the role of the fallacy cum hoc ergo propter hoc in the poor prediction seen with many QSAR models. By embracing fallacy along with an over reliance on statistical inference, it may well be that the manner in which QSAR is practiced is more responsible for its lack of success than any other innate cause.

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