Prediction of Drug-target Interaction Networks from the Integration of Chemical and Genomic Spaces

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2008 Jul 1

PMID 18586719

Citations 345

Authors

Yoshihiro Yamanishi

Michihiro Araki

Alex Gutteridge

Wataru Honda

Minoru Kanehisa

Affiliations

Soon will be listed here.

Abstract

Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.

Results: In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery.

Availability: Softwares are available upon request.

Supplementary Information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

Citing Articles

Prediction of drug target interaction based on under sampling strategy and random forest algorithm.

Chen F, Zhao Z, Ren Z, Lu K, Yu Y, Wang W PLoS One. 2025; 20(3):e0318420.

PMID: 40048461 PMC: 11884685. DOI: 10.1371/journal.pone.0318420.

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks.

Kalemati M, Zamani Emani M, Koohi S Heliyon. 2025; 11(3):e42476.

PMID: 40007773 PMC: 11850134. DOI: 10.1016/j.heliyon.2025.e42476.

Top-DTI: Integrating Topological Deep Learning and Large Language Models for Drug Target Interaction Prediction.

Talo M, Bozdag S bioRxiv. 2025; .

PMID: 39975019 PMC: 11839103. DOI: 10.1101/2025.02.07.637146.

ISLRWR: A network diffusion algorithm for drug-target interactions prediction.

Sun L, Yin Z, Lu L PLoS One. 2025; 20(1):e0302281.

PMID: 39883675 PMC: 11781719. DOI: 10.1371/journal.pone.0302281.

PbsNRs: predict the potential binders and scaffolds for nuclear receptors.

Zheng G, Wu D, Wei X, Xu D, Mao T, Yan D Brief Bioinform. 2025; 26(1.

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References

Keiser M, Roth B, Armbruster B, Ernsberger P, Irwin J, Shoichet B . Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007; 25(2):197-206. DOI: 10.1038/nbt1284. View

Dobson C . Chemical space and biology. Nature. 2004; 432(7019):824-8. DOI: 10.1038/nature03192. View

Yamanishi Y, Vert J, Kanehisa M . Protein network inference from multiple genomic data: a supervised approach. Bioinformatics. 2004; 20 Suppl 1:i363-70. DOI: 10.1093/bioinformatics/bth910. View

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S . From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2005; 34(Database issue):D354-7. PMC: 1347464. DOI: 10.1093/nar/gkj102. View

Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G . BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 2003; 32(Database issue):D431-3. PMC: 308815. DOI: 10.1093/nar/gkh081. View

Stockwell B . Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet. 2001; 1(2):116-25. PMC: 3171795. DOI: 10.1038/35038557. View

Cheng A, Coleman R, Smyth K, Cao Q, Soulard P, Caffrey D . Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol. 2007; 25(1):71-5. DOI: 10.1038/nbt1273. View

Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H . A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Bioinformatics. 2005; 21 Suppl 2:ii245-51. DOI: 10.1093/bioinformatics/bti1141. View

Gunther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E . SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2007; 36(Database issue):D919-22. PMC: 2238858. DOI: 10.1093/nar/gkm862. View

10.

Hattori M, Okuno Y, Goto S, Kanehisa M . Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc. 2003; 125(39):11853-65. DOI: 10.1021/ja036030u. View

11.

Smith T, Waterman M . Identification of common molecular subsequences. J Mol Biol. 1981; 147(1):195-7. DOI: 10.1016/0022-2836(81)90087-5. View

12.

Wishart D, Knox C, Guo A, Cheng D, Shrivastava S, Tzur D . DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2007; 36(Database issue):D901-6. PMC: 2238889. DOI: 10.1093/nar/gkm958. View

13.

Gribskov M, Robinson N . Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput Chem. 1996; 20(1):25-33. DOI: 10.1016/s0097-8485(96)80004-0. View

14.

Haggarty S, Koeller K, Wong J, Butcher R, Schreiber S . Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. Chem Biol. 2003; 10(5):383-96. DOI: 10.1016/s1074-5521(03)00095-4. View

15.

Kratochwil N, Malherbe P, Lindemann L, Ebeling M, Hoener M, Muhlemann A . An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application. J Chem Inf Model. 2005; 45(5):1324-36. DOI: 10.1021/ci050221u. View

16.

Rarey M, Kramer B, Lengauer T, Klebe G . A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 1996; 261(3):470-89. DOI: 10.1006/jmbi.1996.0477. View

17.

Kuruvilla F, Shamji A, Sternson S, Hergenrother P, Schreiber S . Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature. 2002; 416(6881):653-7. DOI: 10.1038/416653a. View

18.

Wheeler D, Barrett T, Benson D, Bryant S, Canese K, Chetvernin V . Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2005; 34(Database issue):D173-80. PMC: 1347520. DOI: 10.1093/nar/gkj158. View

19.

Rainsford K . Anti-inflammatory drugs in the 21st century. Subcell Biochem. 2007; 42:3-27. DOI: 10.1007/1-4020-5688-5_1. View

20.

Yildirim M, Goh K, Cusick M, Barabasi A, Vidal M . Drug-target network. Nat Biotechnol. 2007; 25(10):1119-26. DOI: 10.1038/nbt1338. View