17 December 2008

50% ain’t half-bad

In the world of fragment-based ligand discovery, researchers hope that two fragments, when linked together, will behave at least additively: the free energies of binding for each fragment will sum together, with a multiplicative effect on affinity. In ideal cases, linked fragments will behave synergistically (see for example the post from 18 August, below). But all too often, linking two fragments produces disruptive behavior, and the resulting molecule actually binds less tightly than would be predicted based on the binding energies of the individual fragments. This occurs not just when linking fragments, but in fragment merging and growing as well. Can such phenomena be modeled?

The mathematical groundwork was described more than forty years ago by Spencer Free and James Wilson at the old Smith Kline and French company, and came to be known as a Free-Wilson analysis. In a nice update of this work, Julen Oyarzabal and co-workers have applied this technique to the screening results of eight libraries consisting of several hundred compounds total. The molecules belong to five diverse chemical scaffolds (shown), and were tested against a variety of different targets, including a kinase, GPCRs, ion channels, and P450s.

For each library tested against each target, the authors asked whether the binding contribution due to a substituent Rx was additive, partially additive, or non-additive with the binding contribution of a substituent Ry. The mathematics get pretty intense, and the paper goes far beyond what I can summarize in a blog post, but the main conclusion is surprisingly encouraging: roughly half of all the data sets (10 of 19) show clear additive behavior, while another quarter (5 of 19) show partially additive effects. Only 4 data sets show non-additive behavior.

In many fields, a 50% success rate wouldn’t look too impressive, but in medicinal chemistry (in fact in much of chemistry in general), half-right sounds pretty good. The authors don’t further divide the non-additive data sets into sub-additive versus super-additive categories. In other words, the non-additive effects could well be due to synergy, the quality those of us pursuing FBLD ardently desire. But even if synergy is elusive, the paper suggests that you’ve got a better than even shot of producing a whole that is at least equal to the sum of its parts.

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