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, Csaba Hetényi, István Bitter, Pál Czobor, András Málnási-Csizmadia

Research output: Article

46 Citations (Scopus)


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
Issue number1
Publication statusPublished - jan. 23 2012

ASJC Scopus subject areas

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

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  • Cite this

    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, 52(1), 134-145. https://doi.org/10.1021/ci2002022