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Trying to squeeze sense out of chemical data

Drug-Target Networks & Polypharmacology

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I came across Takigawa et al where they address polypharmacology by investigating drug-target pairs.  Their approach is to simultaneously identify substructures from the ligand and subsequences from the target and combine this information to suggest drug-target pairs that represent some form of polypharmacology.  More specifically their hypothesis is that “polypharmacological principles” are embedded in a special set of paired fragments (substructures on the ligand side, subsequence on the target side). When you think about it, this is a more generalized (abstract?) version of a pharmacophore that makes the role of the target explicit.

Their approach originates from two assumptions

These results suggest that targets of promiscuous drugs can be dissimilar, implying that only a small part of each target is related with the principle of polypharmacology.


Similarly, recent research shows that smaller drugs in molecular weight are likely to be more promiscuous, suggesting that small fragments in each ligand would be a key to drug promiscuity

These lead to their hypothesis

… that paired fragments significantly shared in drug-target pairs could be crucial factors behind polypharmacology.

Based on this idea they first apply a frequent itemset algorithm to identify pairs of subgraph (SG) and subsequences (SS), that occur frequently (more than 5%) in the drug-target pairs. After identifying about 10,000 such SS-SG pairs, they define a sparse fingerprint, where each bit corresponds to one such pair. Using these fingerprints they then cluster the drug-target pairs, ending up with a selection of clusters. They then propose that individual clusters represent distinct polypharmacologies.

Our significant substructure pairs partitioned drug-target pairs covering most of approved drugs into clusters, which were clearly separated from each other, implying that each cluster corresponds to a unique polypharmacology type

While the underlying algorithms to obtain their results are nice, a lot of things weren’t clear.

Foremost, given the above quote, it’s not exactly clear from the paper what is meant by “unique polypharmacology type? Given that a cluster will consist of multiple drugs and multiple targets, it is not apparent from the text that a cluster highlights either promiscuity of compounds or ligand preferences for a small number of targets. While I think this is a major issue there are some other lesser problems

  • I get the impression that they consider promiscuity and polypharmacology as equivalent concepts. While there is a degree of similarity, I’d regard polypharmacology more as a rationally, controlled type of promiscuity
  • Most fragments they highlight in Figure 2 are relatively trivial paths. Certainly, reactive groups can lead to promiscuity; none of the subgraphs list exhibit reactive functionality and their application of the frequent itemset method, using a support of 5% could easily filter these out
  • Given they consider arbitrary subsequences of the target, the resulting associations could be meaningless. Again, it’d be interesting to note, in cases where crystal structure is available, how many of the subsequences, in the list of significant SS-SG pairs, lie in or around the binding site. A related question would be, of the SG-SS pairs associated with a cluster, how are individual subsequences distributed? Few unique subsequences could point towards a common binding site or active domain.
  • Related to the previous point, it’d be interesting to see in how many of the SG-SS paired fragments, the members correspond to actual interacting motifs (again based on crystal structure data).
  • One could argue that just using string subsequences to characterize the target misses information on important ligand-target interactions.

And while they may be the first to consider an analysis of drug-target pairs specifically, the idea of considering ligand and target simultaneously is not new. For example, the SiFT approach is quite similar and was described in 2004.

So, even though the paper seems pretty fuzzy on the supposed polypharmacology that they identify, it is overall an interesting paper (and one of the more interesting cheminformatics applications of frequent itemset methods).

Written by Rajarshi Guha

March 9th, 2011 at 6:07 am

3 Responses to 'Drug-Target Networks & Polypharmacology'

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  1. The so-called (ligand descriptors + protein descriptors) models are really widely used in chemogenomics approaches. This for example:

  2. Vladimir – you’re absolutely right, proteochemometrics is a great example of this type of approach

    Rajarshi Guha

    9 Mar 11 at 1:56 pm

  3. Interesting paper, would like to test it with some some inhouse data.


    10 Mar 11 at 7:31 pm

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