Drug Effect Prediction by Polypharmacology-Based Interaction Profiling

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an appro...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemical information and modeling 2012-01, Vol.52 (1), p.134-145
Main Authors: Simon, Zoltán, Peragovics, Ágnes, Vigh-Smeller, Margit, Csukly, Gábor, Tombor, László, Yang, Zhenhui, Zahoránszky-Kőhalmi, Gergely, Végner, László, Jelinek, Balázs, Hári, Péter, Hetényi, Csaba, Bitter, István, Czobor, Pál, Málnási-Csizmadia, András
Format: Article
Language:English
Subjects:
Algorithms
Binding Sites
Biological and medical sciences
Biomarkers, Pharmacological - analysis
Databases, Factual
Fundamental and applied biological sciences. Psychology
Humans
Interactions. Associations
Intermolecular phenomena
Molecular biophysics
Molecular chemistry
Pharmaceutical Modeling
Pharmacology
Prescription Drugs - chemistry
Prescription Drugs - pharmacology
Protein Binding
Proteins
Proteins - agonists
Proteins - antagonists & inhibitors
Proteins - chemistry
ROC Curve
Small Molecule Libraries - chemistry
Small Molecule Libraries - pharmacology
Statistical analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug–protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.
ISSN:1549-9596
1549-960X
DOI:10.1021/ci2002022