Thanks to a comment on this post, I’ve had a chance to read this interesting article from Stephen Johnson of Bristol-Myers Squibb, entitled “The Trouble with QSAR (Or How I Learned to Stop Worrying And Embrace Fallacy)”. (As a side note, it’s interesting to see that people still make references to the titling of Dr. Strangelove. I’ve never met Johnson, but I’d gather from that that he can’t be much younger than I am).


The most arresting part of the article is the graph found in its abstract. No mention is made of it in the text, but none has to be. It’s a plot of the US highway fatality rate versus the tonnage of fresh lemons imported from Mexico, and I have to say, it’s a pretty darn straight line. I’ve seen a lot shakier plots used to justify some sweeping conclusions, and if those were justified, well, then I’m forced to conclude that Mexican lemons have improved highway safety a great deal. The vitamin C, maybe? The fragrance? Bioflavanoids?

None of the above, of course. Correlation, tiresomely, once again refuses to imply causation, even when you ask it nicely. And that’s the whole point of the article. QSAR, for those outside the business, stands for Quantitative Structure-Activity Relationship(s), an attempt to rationalize the behavior of a series of drug candidate compounds through computational means. The problem is, there are plenty of possible variables (size, surface area, molecular weight, polarity, solubility, charge, hydrogen bond donors and acceptors, and as many structural representation parameters as you can stand). As Johnson notes dryly:

” With such an infinite array of descriptions possible, each of which can be coupled with any of a myriad of statistical methods, the number of equivalent solutions is typically fairly substantial.”

That it is. And (as he rightly mentions) one of the other problems is that all these variables are discontinuous. Some region of the molecule can get larger, but only up to a point. When it’s too large to fit into the binding site any more, activity drops off steeply. Similarly, the difference between forming a crucial hydrogen bond and not forming one is a big difference, and it can be realized by a very small change in structure and properties. (Thus the “magic methyl” effect).

But that’s not the whole problem. Johnson takes many of his fellow computational chemists to task for what he sees as sloppy work. Too many models are advanced just because they’ve shown some (limited) correlations, and they’re not tested hard enough afterwards. Finding a model with a good “fitness score” becomes an end in itself:

”We can generate so many hypotheses, relating convoluted molecular factors to activity in such complicated ways, that the process of careful hypothesis testing so critical to scientific understanding has been circumvented in favor of blind validation tests with low resulting information content. QSAR disappoints so often, not only because the response surface is not smooth but because we have embraced the fallacy that correlation begets causation.”

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