Just a few days ago we were talking about whether anything could be predicted about a molecule’s toxicity by looking over its biophysical properties. Some have said yes, this is possible (that less polar compounds tend to be more toxic), but a recent paper has said no, that no such correlation exists. This is part of the larger “Rule of 5” discussion, about whether clinical success in general can be (partially) predicted by such measurements (lack of unexpected toxicity is a big factor in that success). And that discussion shows no sign of resolving any time soon, either.
Now comes a new paper that lands right in the middle of this argument. Douglas Kell’s group at Manchester has analyzed a large data set of known human metabolites (the Recon2 database, more here) and looked at how similar marketed drugs are to the structures in it. Using MACCS structural fingerprints, they find that 90% of marketed drugs have a Tanimoto similarity of more than 0.5 to at least one compound in the database, and suggest that this could be a useful forecasting tool for new structures.
Now, that’s an interesting idea, and not an implausible one, either. But the next things to ask are “Is it valid?” and “What could be wrong with it?” That’s the way we learn how to approach pretty much anything new that gets reported in science, of course, although people do tend to take it the wrong way around the dinner table. Applying that in this case, here’s what I can think of that could be off:
1. Maybe the reason that everything looks like one of the metabolites in the database is that the database contains a bunch of drug metabolites to start with, perhaps even the exact ones from the drugs under discussion? This isn’t the case, though: Recon2 contains endogenous metabolites only, and the Manchester group went through the list removing compounds that are listed as drugs but are also known metabolites (nutritional supplements, for the most part).
2. Maybe Tanimoto similarities aren’t the best measurement to use, and overestimate things? Molecular similarity can be a slippery concept, and different people often mean different things by it. The Tanimoto coefficient is the ratio of shared features of two molecules to their unique features, so a Tanimoto of 1 means that the two are identical. What does a coefficient of 0.5 tell us? That depends on how those “features” are counted, as one could well imagine, and the various ones are usually referred to as compound “fingerprints”. The Manchester group tried several of these, and settled on the 166 descriptors of the MACCS set. And that brings up the next potential problem. . .
3. Maybe MACCS descriptors aren’t the best ones to use? I’m not enough of an informatics person to say, although this point did occur to the authors. They don’t seem to know the answer, either, however:
However, the cumulative plots of the (nearest metabolite Tanimoto similarity) for each drug using different fingerprints do differ quite significantly depending on which fingerprint is used, and clearly the well-established MACCS fingerprints lead to a substantially greater degree of ‘metabolite-likeness’ than do almost all the other encodings (we do not pursue this here).
So this one is an open question – it’s not for sure if there’s something uniquely useful about the MACCS fingerprint set here, or if there’s something about the MACCS fingerprint set that makes it just appear to be uniquely useful. The authors do note in the paper that they tried to establish that the patterns they saw were “. . .not a strange artefact of the MACCS encoding itself.” And there’s another possibility. . .
4. Maybe the universe of things that make this cutoff is too large to be informative? That’s another way of asking “What does a Tanimoto coefficient of 0.5 or greater tell you?” The authors reference a paper (Baldi and Nasr) on that very topic, which says:
Examples of fundamental questions one would like to address include: What threshold should one use to assess significance in a typical search? For instance, is a Tanimoto score of 0.5 significant or not? And how many molecules with a similarity score above 0.5 should one expect to find? How do the answers to these questions depend on the size of the database being queried, or the type of queries used? Clear answers to these questions are important for developing better standards in chemoinformatics and unifying existing search methods for assessing the significance of a similarity score, and ultimately for better understanding the nature of chemical space.
The Manchester authors say that applying the methods of that paper to their values show that they’re highly significant. I’ll take their word for that, since I’m not in a position to run the numbers, but I do note that the earlier paper emphasizes that a particular Tanimoto score’s significance is highly dependent on the size of the database, the variety of molecules in it, and the representations used. The current paper doesn’t (as far as I can see) go into the details of applying the Baldi and Nasr calculations to their own data set, though.
The authors have done a number of other checks, to make sure that they’re not being biased by molecular weights, etc. They looked for trends that could be ascribed to molecular properties like cLogP, but found none. And they tested their hypothesis by running 2000 random compounds from Maybridge through, which did indeed generate much different-looking numbers than the marketed drugs.
As for whether their overall method is useful, here’s the Manchester paper’s case:
. . .we have shown that when encoded using the public MDL MACCS keys, more than 90 % of individual marketed drugs obey a ‘rule of 0.5’ mnemonic, elaborated here, to the effect that a successful drug is likely to lie within a Tanimoto distance of 0.5 of a known human metabolite. While this does not mean, of course, that a molecule obeying the rule is likely to become a marketed drug for humans, it does mean that a molecule that fails to obey the rule is statistically most unlikely to do so.
That would indeed be a useful thing to know. I would guess that people inside various large drug organizations are going to run this method over their own internal database of compounds to see how it performs on their own failures and successes – and that is going to be the real test. How well it performs, though, we may not hear for a while. But I’ll keep my ears open, and report on anything useful.