title: Ligand-based target prediction with signature fingerprints.
abstract: When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.
authors.affiliation: Department of Pharmaceutical Biosciences and §Science for Life Laboratory, Uppsala University , SE-751 05 Uppsala, Sweden.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Alvarsson
authors.forename: Martin
authors.initials: M
authors.lastname: Eklund
authors.forename: Ola
authors.initials: O
authors.lastname: Engkvist
authors.forename: Ola
authors.initials: O
authors.lastname: Spjuth
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsson
authors.forename: Jarl E S
authors.initials: JE
authors.lastname: Wikberg
authors.forename: Tobias
authors.initials: T
authors.lastname: Noeske
mesh_terms: D019540:Area Under Curve; D003198:Computer Simulation; D062126:Databases, Chemical; D015195:Drug Design; D008024:Ligands; D008956:Models, Chemical; D054802:Molecular Imprinting / Q000379:methods*; D015394:Molecular Structure; D012372:ROC Curve; D012984:Software
chemical_list: D008024:Ligands