Drug effect prediction by polypharmacology-based interaction profiling

Zoltán Simon, Ágnes Peragovics, Margit Vigh-Smeller, Gábor Csukly, László Tombor, Zhenhui Yang, Gergely Zahoránszky-Kóhalmi, László Végner, Balázs Jelinek, Péter Hári, C. Hetényi, I. Bitter, P. Czobor, A. Málnási-Csizmadia

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)134-145
Number of pages12
JournalJournal of Chemical Information and Modeling
Volume52
Issue number1
DOIs
Publication statusPublished - Jan 23 2012

Fingerprint

drug
Proteins
Molecules
Binding sites
interaction
Pharmaceutical Preparations
Chemical analysis
Binding Sites
Protein Binding

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Drug effect prediction by polypharmacology-based interaction profiling. / Simon, Zoltán; Peragovics, Ágnes; Vigh-Smeller, Margit; Csukly, Gábor; Tombor, László; Yang, Zhenhui; Zahoránszky-Kóhalmi, Gergely; Végner, László; Jelinek, Balázs; Hári, Péter; Hetényi, C.; Bitter, I.; Czobor, P.; Málnási-Csizmadia, A.

In: Journal of Chemical Information and Modeling, Vol. 52, No. 1, 23.01.2012, p. 134-145.

Research output: Contribution to journalArticle

Simon, Z, Peragovics, Á, Vigh-Smeller, M, Csukly, G, Tombor, L, Yang, Z, Zahoránszky-Kóhalmi, G, Végner, L, Jelinek, B, Hári, P, Hetényi, C, Bitter, I, Czobor, P & Málnási-Csizmadia, A 2012, 'Drug effect prediction by polypharmacology-based interaction profiling', Journal of Chemical Information and Modeling, vol. 52, no. 1, pp. 134-145. https://doi.org/10.1021/ci2002022
Simon Z, Peragovics Á, Vigh-Smeller M, Csukly G, Tombor L, Yang Z et al. Drug effect prediction by polypharmacology-based interaction profiling. Journal of Chemical Information and Modeling. 2012 Jan 23;52(1):134-145. https://doi.org/10.1021/ci2002022
Simon, Zoltán ; Peragovics, Ágnes ; Vigh-Smeller, Margit ; Csukly, Gábor ; Tombor, László ; Yang, Zhenhui ; Zahoránszky-Kóhalmi, Gergely ; Végner, László ; Jelinek, Balázs ; Hári, Péter ; Hetényi, C. ; Bitter, I. ; Czobor, P. ; Málnási-Csizmadia, A. / Drug effect prediction by polypharmacology-based interaction profiling. In: Journal of Chemical Information and Modeling. 2012 ; Vol. 52, No. 1. pp. 134-145.
@article{17d0193661d745a085e8be4a2514a148,
title = "Drug effect prediction by polypharmacology-based interaction profiling",
abstract = "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.",
author = "Zolt{\'a}n Simon and {\'A}gnes Peragovics and Margit Vigh-Smeller and G{\'a}bor Csukly and L{\'a}szl{\'o} Tombor and Zhenhui Yang and Gergely Zahor{\'a}nszky-K{\'o}halmi and L{\'a}szl{\'o} V{\'e}gner and Bal{\'a}zs Jelinek and P{\'e}ter H{\'a}ri and C. Het{\'e}nyi and I. Bitter and P. Czobor and A. M{\'a}ln{\'a}si-Csizmadia",
year = "2012",
month = "1",
day = "23",
doi = "10.1021/ci2002022",
language = "English",
volume = "52",
pages = "134--145",
journal = "Journal of Chemical Information and Modeling",
issn = "1549-9596",
publisher = "American Chemical Society",
number = "1",

}

TY - JOUR

T1 - Drug effect prediction by polypharmacology-based interaction profiling

AU - Simon, Zoltán

AU - Peragovics, Ágnes

AU - Vigh-Smeller, Margit

AU - Csukly, Gábor

AU - Tombor, László

AU - Yang, Zhenhui

AU - Zahoránszky-Kóhalmi, Gergely

AU - Végner, László

AU - Jelinek, Balázs

AU - Hári, Péter

AU - Hetényi, C.

AU - Bitter, I.

AU - Czobor, P.

AU - Málnási-Csizmadia, A.

PY - 2012/1/23

Y1 - 2012/1/23

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84858067143&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858067143&partnerID=8YFLogxK

U2 - 10.1021/ci2002022

DO - 10.1021/ci2002022

M3 - Article

C2 - 22098080

AN - SCOPUS:84858067143

VL - 52

SP - 134

EP - 145

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

SN - 1549-9596

IS - 1

ER -