Page 14 - GPD-2-1

Gene & Protein in Disease AI-based drug repositioning

Vet Med, 53(12): 123–127. COVID-19 based on mixed graph network and ion channel.
Math Biosci Eng, 19(4): 3269–3284.
3. Nelson BS, Kremer DM, Lyssiotis CA, 2018, New tricks for
an old drug. Nat Chem Biol, 14: 990–991. 17. Wall ME, Rechtsteiner A, Rocha LM, 2002, Singular
Value Decomposition and Principal Component Analysis.
4. Li FT, Liu MX, Wang XB, et al., 2022, Progress of COVID-19
drug repositioning study. China Pharm Biotechnol, 17(4): 8. Springer, Germany.
18. Shen M, Xiao Y, Golbraikh A, et al., 2003, Development and
5. Huang F, Yang HF, Zhu X, 2021, Advances in the application
of artificial intelligence in new drug discovery. Adv Pharm, validation of k-nearest-neighbor QSPR models of metabolic
2021(7): 502–511. stability of drug candidates. J Med Chem, 46(3): 3013–3020.
6. Jarada TN, Rokne JG, Alhajj R, 2020, A review of https://doi.org/10.1021/jm020491t
computational drug repositioning: strategies, approaches, 19. Susnow RG, Dixon SL, 2003, Use of robust classification
opportunities, challenges, and directions. J Cheminform, techniques for the prediction of human cytochrome
12(1): 46. P450 2D6 inhibition. J Chem Inform Comput Sci, 43(4):
https://doi.org/10.1186/s13321-020-00450-7 1308–1315.
7. Cheng, Fei X, Liu, et al., 2012, Prediction of drug-target 20. Christianini N, Shawe-Taylor J, 2002, Support Vector
interactions and drug repositioning via network-based Machines and other Kernel-based Learning Methods.
inference. PLoS Comput Biol, 8(5): e1002503. Cambridge University Press, United Kingdom.
https://doi.org/10.1371/journal.pcbi.1002503 21. Guengerich FP, 2006, Cytochrome P450s and other enzymes
in drug metabolism and toxicity. AAPS J, 8(1): E101–E111.
8. Guney E, Menche J, Vidal M, et al., 2016, Network-based in
silico drug efficacy screening. Nat Commun, 7(1): 10331. 22. Cheng F, Yu Y, Shen J, et al., 2011, Classification of
cytochrome P450 inhibitors and noninhibitors using
https://doi.org/10.1038/ncomms10331 combined classifiers. J Chem Inform Model, 51: 996–1011.

9. Wang W, Yang S, Zhang X, et al., 2014, Drug repositioning https://doi.org/10.1021/ci200028n
by integrating target information through a heterogeneous
network model. Bioinformatics, 30(20): 2923–2930. 23. Napolitano F, Zhao Y, Moreira VM, et al., 2013, Drug
repositioning: A machine-learning approach through data
https://doi.org/10.1093/bioinformatics/btu403 integration. J Cheminform, 5(1): 30.
10. Chen, X, Liu, Yan GY, 2012, Drug-target interaction 24. Gottlieb A, Stein GY, Ruppin E, et al., PREDICT: A method
prediction by random walk on the heterogeneous network. for inferring novel drug indications with application to
Mol Biosyst, 8(7): 1970–1978. personalized medicine. Mol Syst Biol, 7(1): 496.
11. Olayan RS, Haitham A, Bajic VB, 2018, DDR: Efficient https://doi.org/10.1038/msb.2011.26
computational method to predict drug–target interactions
using graph mining and machine learning approaches. 25. Gönen M, 2012, Predicting drug-target interactions from
Bioinformatics, 34(7): 1164–1173. chemical and genomic kernels using Bayesian matrix
factorization. Bioinformatics, 28(18): 2304–2310.
https://doi.org/10.1093/bioinformatics/btx731
https://doi.org/10.1093/bioinformatics/bts360
12. Peng J, Li J, Shang X, 2020, A learning-based method
for drug-target interaction prediction based on feature 26. Aliper A, Plis S, Artemov A, et al., 2016, Deep learning
representation learning and deep neural network. BMC applications for predicting pharmacological properties of
Bioinformatics, 21(Suppl 13): 394. drugs and drug repurposing using transcriptomic data. Mol
Pharm, 13(7): 2524–2530.
13. Ji B, You ZH, Jiang HJ, et al., 2020, Prediction of drug-target
interactions from multi-molecular network based on LINE https://doi.org/10.1021/acs.molpharmaceut.6b00248
network representation method. J Transl Med, 18(1): 347. 27. Segler MH, Preuss M, Waller MP, 2018, Planning chemical
https://doi.org/10.1186/s12967-020-02490-x syntheses with deep neural networks and symbolic AI.
Nature, 555(7698): 604–610.
14. Lu XG, Liu F, Li JX, et al., 2021, A drug target prediction
method based on multi-source data fusion and network https://doi.org/10.1038/nature25978
structure perturbation CN112420126A. China: Hunan 28. Hughes TB, Swamidass SJ, 2017, Deep learning to predict
Province.
the formation of quinone species in drug metabolism. Chem
15. He JY, Yang XX, Gong Z, 2021. A computational drug Res Toxicol, 30(2): 642–656.
repositioning method based on memory network and 29. Turk S, Merget B, Rippmann F, et al., Coupling matched
attention CN112331275A. China: Jiangsu Province.
molecular pairs with machine learning for virtual compound
16. Wang X, Li Q, Liu Y, et al., 2022, Drug repositioning of optimization. J Chem Inform Model, 57(12): 3079–3085.

Volume 2 Issue 1 (2023) 8 https://doi.org/10.36922/gpd.v1i3.201