Quantifying the Relationships among Drug Classes

Abstract

The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.

Identifying mechanism-of-action targets for drugs and probes

“…To discover molecular targets for these 78 drugs, we looked for chemical similarities to ligand sets for over 2,500 targets, using SEA (10,14). SEA describes each target by its known ligands, as represented by topological fingerprints (here extended connectivity fingerprints [ECFP] ECFP_4 (15)).…”

“…For each of these databases, the drugs that do not have an associated target have been taken as the initial set of "drug with unknown protein target." These have been further explored by SEA (10,14) and by a literature search to get the final set of drugs with no known targets.…”

The Presynaptic Component of the Serotonergic System is Required for Clozapine's Efficacy

“…Each target was represented solely by its set of known ligands. We used both the 1024-bit folded ECFP4 (Hert et al, 2008) and 2048-bit daylight (James et al, 1992) fingerprints for SEA, as separate screens, and accepted the highestscoring predictions arising from either fingerprint. Because of the small size of the query data set (three drugs), all SEA predictions are reported here by P-values instead of by E-values (Keiser et al, 2009).…”

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

“…The first detailed study of this type was by Willett and Winterman [12], who found that computed molecular similarities could be used to predict a range of physical, chemical and biological properties in a range of small datasets for which both structural and property information were available. There have been many subsequent examples of this approach to the evaluation of similarity procedures [13][14][15][16][17], and further supporting evidence for the general applicability of the Principle comes from studies in chemogenomics [18][19][20][21]. It must be emphasized that there are many exceptions to the Principle [22,23], but it has been found to provide a very useful basis for the development of a range of similarity-based approaches for the processing of large chemical databases.…”