Prediction of the Side Effects Associated with the Drug-Drug Interaction in Human Beings using Chaotic Particle Swarm Optimisation Based Deep Radial Networks


  • M. Arunkumar, T.S. Baskaran


Prediction, side effects, drug-drug interaction, Chaotic Particle swarm optimisation, deep radial networks


Prediction of the side effects associated with the drug-drug interaction (DDI) in human beings using Chaotic Particle swarm optimisation (CPSO) based deep radial networks (DRN). As drug classes, feature vectors, pathways, target, and enzymes are utilised; afterwards, CPSO is utilised to extract feature interactions between these drug-related entities. We made use of DRN as a predictor of events associated with DDIs by basing it on the representation of characteristics. The findings indicate that when compared to several other metrics that are state-of-the-art, DRN-DDI performs better. In the meanwhile, we discuss the ways in which individual and combinational characteristics contribute. DRN-DDI provides greater advantages than other methods when it comes to the prediction of DDI events.


Download data is not yet available.


Jang, H. Y., Song, J., Kim, J. H., Lee, H., Kim, I. W., Moon, B., & Oh, J. M. (2022). Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digital Medicine, 5(1), 88.

Hou, X., You, J., & Hu, P. (2019, February). Predicting drug-drug interactions using deep neural network. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing (pp. 168-172).

Zhang, Y., Qiu, Y., Cui, Y., Liu, S., & Zhang, W. (2020). Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning. Methods, 179, 37-46.

Olayan, R. S., Ashoor, H., & Bajic, V. B. (2018). DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics, 34(7), 1164-1173.

Wang, Y., & Zeng, J. (2013). Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 29(13), i126-i134.

Chu, Y., Shan, X., Chen, T., Jiang, M., Wang, Y., Wang, Q., ... & Wei, D. Q. (2021). DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings in bioinformatics, 22(3), bbaa205.

Shi, H., Liu, S., Chen, J., Li, X., Ma, Q., & Yu, B. (2019). Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics, 111(6), 1839-1852.

Fu, G., Ding, Y., Seal, A., Chen, B., Sun, Y., & Bolton, E. (2016). Predicting drug target interactions using meta-path-based semantic network analysis. BMC bioinformatics, 17(1), 1-10.

Tanvir, F., Islam, M. I. K., & Akbas, E. (2021, October). Predicting drug-drug interactions using meta-path based similarities. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-8). IEEE.

Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadan, R., & Friedman, C. (2012). Drug—drug interaction through molecular structure similarity analysis. Journal of the American Medical Informatics Association, 19(6), 1066-1074.

Vilar, S., Uriarte, E., Santana, L., Friedman, C., & P Tatonetti, N. (2014). State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity. Current Drug Metabolism, 15(5), 490-501.

Luo, H., Zhang, P., Huang, H., Huang, J., Kao, E., Shi, L., ... & Yang, L. (2014). DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic acids research, 42(W1), W46-W52.

Zakharov, A. V., Varlamova, E. V., Lagunin, A. A., Dmitriev, A. V., Muratov, E. N., Fourches, D., ... & Nicklaus, M. C. (2016). Qsar modeling and prediction of drug-drug interactions. Molecular pharmaceutics, 13(2), 545-556.

Huang, H., Zhang, P., Qu, X. A., Sanseau, P., & Yang, L. (2014). Systematic prediction of drug combinations based on clinical side-effects. Scientific reports, 4(1), 7160.

Herrero-Zazo, M., Segura-Bedmar, I., Hastings, J., & Martinez, P. (2015). DINTO: using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms. Journal of chemical information and modeling, 55(8), 1698-1707.

Cami, A., Manzi, S., Arnold, A., & Reis, B. Y. (2013). Pharmacointeraction network models predict unknown drug-drug interactions. PloS one, 8(4), e61468.




How to Cite

M. Arunkumar. (2024). Prediction of the Side Effects Associated with the Drug-Drug Interaction in Human Beings using Chaotic Particle Swarm Optimisation Based Deep Radial Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2394–2401. Retrieved from



Research Article