The prediction of polypharmocologic effects from existing networks requires a method of establishing the relationship between molecules with unknown biological properties and those which have well established pharmacology.  Towards this end, several techniques have been developed to estimate the degree of similarity between the query molecule and the compounds in the polypharmacology network.  In our own work, we have developed a method that attempts to define the probability of iso-activity of a compound to others in the data set by calibrating the similarity coefficients against a large dataset of measured activities.  We have used the resultant �Belief Theory� estimates of similarity in the generation and querying of the pharmacology networks.  This presentation will highlight the advantages of using this approach when defining networks, as well as efforts to extend the work from two-dimensional fingerprints to three-dimensional shape and electrostatic descriptors.

AbbVie polypharmacology explorer: A belief theory approach to constructing and querying chemical biology networks

246th National Meeting (2013)

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