LINGO Profiles Fingerprint and Association Rule Mining for drug-target interaction prediction
Abstract
The prediction of drug-target interactions (DTIs) using machine learning techniques together with the proper representation of compounds can speed up the time-consuming experimental work in predicting DTIs especially when a large dataset is used. Hence, in this paper, we have proposed a new molecular descriptor based on LINGO Profiles known as LINGO Profiles Fingerprint (LPFP). LPFP is used together with machine learning to predict DTIs on a ChEMBL dataset. Dimensionality reduction using Association Rule Mining (ARM) is also introduced to overcome the high dimensionality suffered by LPFP. LPFP managed to reach an equal accuracy reading to the state-of-the-art descriptor called ECFP4 (Δ0.18%), but it suffers in the time taken (Δ27 mins) due to the dimensionality problem mentioned. Hence, three new smaller size LPFPs (s = 60%, s = 70%, s = 80%) were constructed by only extracting the important fragments using ARM and then a benchmark analysis with the original LPFP and ECFP4 fingerprints was done. This study not only solved the dimensionality problem, but also managed to excel in both the accuracy and time taken when predicting DTIs. An increase in the accuracy of over 250 times faster than the original LPFP was observed after the benchmark analysis is performed. Furthermore, an accuracy of over 80% was achieved in three new activity classes that are acquired from ChEMBL, further proving the promising performance of ARM which has made it favourable for LPFPs to be used in DTI prediction and in other drug discovery problems.
Keywords
Full Text:
PDFReferences
1. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. British Journal of Pharmacology 2011; 162(6): 1239–1249. doi: 10.1111/j.1476-5381.2010.01127.x
2. Drews J. Drug discovery: A historical perspective. Science 2000; 287(5460): 1960–1964. doi: 10.1126/science.287.5460.1960
3. Hann M, Green R. Chemoinformatics—A new name for an old problem? Current Opinion in Chemical Biology 1999; 3(4): 379–383. doi: 10.1016/S1367-5931(99)80057-X
4. Hung CL, Chen CC. Computational approaches for drug discovery. Drug Development Research 2014; 75(6): 412–418. doi: 10.1002/ddr.21222
5. Agamah FE, Mazandu GK, Hassan R, et al. Computational/in silico methods in drug target and lead prediction. Briefings in Bioinformatics 2020; 21(5): 1663–1675. doi: 10.1093/bib/bbz103
6. Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal 2016; 14: 177–184. doi: 10.1016/j.csbj.2016.04.004
7. Chen R, Liu X, Jin S, et al. Machine learning for drug-target interaction prediction. Molecules 2018; 23(9): 2208. doi: 10.3390/molecules23092208
8. Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010; 26(12): 10.1093/bioinformatics/btq176
9. Glick NM, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases. Journal of Chemical Information Modeling 2006; 46(3): 1124–1133. doi: 10.1021/ci060003g
10. Wen M, Zhang Z, Niu S, et al. Deep-learning-based drug-target interaction prediction. Journal of Proteome Research 2017; 16(4): 1401–1409. doi: 10.1021/acs.jproteome.6b00618
11. Prado-Prado F, García-Mera X, Abeijón P, et al. Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. European Journal of Medicinal Chemistry 2011; 46(4): 1074–1094. doi: 10.1016/j.ejmech.2011.01.023
12. Nasution AK, Wijaya SH, Kusuma WA. Prediction of drug-target interaction on jamu formulas using machine learning approaches. In: Proceedings of 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS); 12–13 October 2019; Bali, Indonesia. pp. 169–174.
13. Rodríguez-Pérez R, Vogt M, Bajorath J. Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS Omega 2017; 2(10): 6371–6379. doi: 10.1021/acsomega.7b01079
14. Riddick G, Song H, Ahn S, et al. Predicting in vitro drug sensitivity using Random Forests. Bioinformatics 2011; 27(2): 220–224. doi: 10.1093/bioinformatics/btq628
15. Shi H, Liu S, Chen J, et al. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics 2019; 111(6): 1839–1852. doi: 10.1016/j.ygeno.2018.12.007
16. Vidal D, Thormann M, Pons M. LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities. Journal of Chemical Information and Modeling 2005; 45(2): 386–393. doi: 10.1021/ci0496797
17. Abdo A, Pupin M. LINGO-DL: A text-based approach for molecular similarity searching. Journal of Computer-Aided Molecular Design 2021; 35(5): 657–665. doi: 10.1007/s10822-021-00383-9
18. bin Javeed MJ, Malim NHAH. Storage consumption reduction using improved inverted indexing for similarity search on LINGO Profiles. International Journal of Advanced Computer Science and Applications 2019; 10(5): 2019. doi: 10.14569/IJACSA.2019.0100505
19. Siswanto S, Liong TH, Shaufiah. Dimensionality reduction for association rule mining with IST-EFP algorithm. In: Proceedings of 2015 3rd International Conference on Information and Communication Technology (ICoICT); 27–29 May 2015; pp. 184–187.
20. Li PH, Lee T, Youn HY. Dimensionality reduction with sparse locality for principal component analysis. Mathematical Problems in Engineering 2020; 2020: 1–12. doi: 10.1155/2020/9723279
21. Malavika S, Phil M, Selvam K. Reduction of dimensionality for high dimensional data using correlation measures. Available online: http://www.ripublication.com (accessed on 27 July 2023).
22. Fujiwara T, Kwon OH, Ma KL. Supporting analysis of dimensionality reduction results with contrastive learning. arXiv 2019; arXiv:1905.03911. doi: 10.1109/TVCG.2019.2934251
23. Mahmud SMH, Chen W, Jahan H, et al. Dimensionality reduction based multi-kernel framework for drug-target interaction prediction. Chemometrics and Intelligent Laboratory Systems 2021; 212: 104270. doi: 10.1016/j.chemolab.2021.104270
24. Terol RM, Reina AR, Ziaei S, Gil D. A machine learning approach to reduce dimensional space in large datasets. IEEE Access 2020; 8: 148181–148192. doi: 10.1109/ACCESS.2020.3012836
25. Gardiner EJ, Gillet VJ. Perspectives on knowledge discovery algorithms recently introduced in chemoinformatics: Rough set theory, association rule mining, emerging patterns, and formal concept analysis. Journal of Chemical Information and Modeling 2015; 55(9): 1781–1803. doi: 10.1021/acs.jcim.5b00198
26. Gaulton A, Hersey A, Nowotka M, et al. The ChEMBL database in 2017. Nucleic Acids Research 2017; 45(D1): D945–D954. doi: 10.1093/nar/gkw1074
27. Arif S, Khan NZS, Malim N, Zainudin S. Retrieval performance using different type of similarity coefficient for virtual screening. Research Journal of Applied Sciences, Engineering and Technology 2015; 9(5): 391–395. doi: 10.19026/rjaset.9.1418
28. Heikamp K, Bajorath J. Support vector machines for drug discovery. Expert Opinion on Drug Discovery 2014; 9(1): 93–104. doi: 10.1517/17460441.2014.866943
29. Steinbeck C, Han Y, Kuhn S, et al. The Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics. Journal of Chemical Information Comput Sciences 2003; 43(2): 493–500. doi: 10.1021/ci025584y
(340 Abstract Views, 233 PDF Downloads)
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Muhammad Jaziem Mohamed Javeed, Azwaar Khan Azlim Khan, Nurul Hashimah Ahamed Hassain Malim
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This site is licensed under a Creative Commons Attribution 4.0 International License.