GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning
| Autor: | Violeta I. Pérez-Nueno, Arnaud Sinan Karaboga, David W. Ritchie, Michel Souchet |
|---|---|
| Přispěvatelé: | Harmonic Phama, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Models
Molecular Databases Pharmaceutical Polypharmacology General Chemical Engineering Normal Distribution Nanotechnology Computational biology Library and Information Sciences Biology ENCODE Ligands Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Similarity (network science) ComputingMilieux_MISCELLANEOUS 030304 developmental biology 0303 health sciences Fingerprint (computing) Drug Repositioning Chemoinformatics General Chemistry 3. Good health Computer Science Applications Drug repositioning Pharmaceutical Preparations Cheminformatics Drug Design [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] DrugBank 030217 neurology & neurosurgery |
| Zdroj: | Journal of Chemical Information and Modeling Journal of Chemical Information and Modeling, American Chemical Society, 2014, 54 (3), pp.720-734. ⟨10.1021/ci4006723⟩ Journal of Chemical Information and Modeling, 2014, 54 (3), pp.720-734. ⟨10.1021/ci4006723⟩ |
| ISSN: | 1549-9596 1549-960X |
| Popis: | Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|