Ant Colony Optimization for Prediction of Compound-Protein Interactions

  • Akhmad Rezki Purnajaya Universitas Universal
Keywords: Ant colony optimization, Compound-protein interaction, Drug-target analysis, Link prediction


The prediction of Compound-Protein Interactions (CPI) is an essential step in drug-target analysis for developing new drugs. Therefore, it needs a good incentive to develop a faster and more effective method to predicting the interaction between compound and protein. Predicting the unobserved link of CPI can be done with Ant Colony Optimization for Link Prediction (ACO_LP) algorithms. Each ant selects its path according to the pheromone value and the heuristic information in the link. The path passed by the ant is evaluated and the pheromone information on each link is updated according to the quality of the path. The pheromones on each link are used as the final value of similarity between nodes. The ACO_LP are tested on benchmark CPI data: Nuclear Receptor, G-Protein Coupled Receptor (GPCR), Ion Channel, and Enzyme. Result show that the accuracy values for Nuclear Receptor, GPCR, Ion Channel, and Enzyme dataset are 0.62, 0.62, 0.74, and 0.79 respectively. The results indicate that ACO_LP has good accuracy for prediction of CPI.


Download data is not yet available.


N. Ajay, “Morphological Similarity: A 3D Molecular Similarity Method Correlated With Protein-Ligand Recognition,’’ Journal of Computer-Aided Molecular Design, Page: 199–213, 2000.

T. Yasuo, and Y. Yoshiro, “Scalable Prediction Of Compound-Protein Interactions Using Minwise Hashing,” International Conference on Genome Informatics,” doi:10.1186/1752-0509-7-S6-S3, 2013.

H. Woong, Z. Xiaolei, G. Mark, and K, Daisuke, “3D Compound Comparison Methods and Their Application in Drug Discovery,” Molecules, Page:12841-12862; doi:10.3390/molecules200712841, 2004.

S. Kim, D. Jin, and H. Lee, “Predicting drug-target interactions using drug-drug interactions”. PLoS ONE 8,, 2013.

Engelbrecht and P. Andries, “Computational intelligence : an Introduction”, John Wiley & Sons Ltd. England, 2007.

C. Bolun, and C. Ling. ”A Link Prediction Algorithm Based on Ant Colony Optimization”, Applied Intelligence; DOI: 10.1007/s10489-014-0558-5, 2014.

P. Sonego, A. Kocsor and S. Pongor, “ROC analysis: Applications to the classification of biological sequences and 3D structures”. Briefings in Bioinformatics 9, 198-209, 2007.

T. Fawcett, “ROC Graphs: Notes and practical considerations for data mining researchers”. Patter n Recognition Letters 31, 1-38, 2003.

Y. Yamanishi, A. Michihiro, G. Alex, H. Wataru and K. Minoru, “Prediction of drug–target interaction networks from the integration of chemical and genomic spaces”, Bioinformatics, Vol. 24 ISMB, pages i232–i240, doi:10.1093/bioinformatics/btn162, 2008.

CW. Harris, “Problems in measuring change”. Madison: University of Wisconsin Press. pp. 167–198, 1967.

How to Cite
A. Purnajaya, “Ant Colony Optimization for Prediction of Compound-Protein Interactions”, JAIC, vol. 3, no. 2, pp. 38-41, Oct. 2019.