Machine Learning Applications in Drug Repurposing

Interdisciplinary Sciences: Computational Life Sciences - Tập 14 Số 1 - Trang 15-21 - 2022
Fan Yang1, Qi Zhang2, Xiaokang Ji2, Yanchun Zhang3, Wentao Li4, Shaoliang Peng5, Fei Xue2
1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
2Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
3Institute for Sustainable Industries & Liveable Citie, Victoria University, Melbourne, Australia
4School of Computer Science, National University of Defense Technology, Changsha, China
5College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inform Model 53(7):1563–1575. https://doi.org/10.1021/ci400187y

Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292

Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci 4(2):268–276. https://doi.org/10.1021/acscentsci.7b00572

Jin W, Yang K, Barzilay R, Jaakkola T (2018) Learning multimodal graph-to-graph translation for molecular optimization. arXiv preprint arXiv:1812.01070

Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J (2020) Graphaf: a flow-based autoregressive model for molecular graph generation. arXiv preprint arXiv:2001.09382

Zang C, Wang F (2020) Moflow: an invertible flow model for generating molecular graphs. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 617–626. https://doi.org/10.1145/3394486.3403104

Jin W, Barzilay R, Jaakkola T (2020) Multi-objective molecule generation using interpretable substructures. In: International Conference on Machine Learning. PMLR, pp. 4849–4859. http://proceedings.mlr.press/v119/jin20b.html

Bung N, Krishnan SR, Bulusu G, Roy A (2021) De novo design of new chemical entities for sars-cov-2 using artificial intelligence. Fut Med Chem 13(06):575–585. https://doi.org/10.4155/fmc-2020-0262

Zhavoronkov A, Zagribelnyy B, Zhebrak A, Aladinskiy V, Terentiev V, Vanhaelen Q, Bezrukov DS, Polykovskiy D, Shayakhmetov R, Filimonov A, Bishop M (2020) Potential non-covalent SARS-CoV-2 3C-like protease inhibitors designed using generative deep learning approaches and reviewed by human medicinal chemist in virtual reality. https://doi.org/10.26434/chemrxiv.12301457.v1

Cai Y, Zeng M, Chen YZ (2021) The pharmacological mechanism of huashi baidu formula for the treatment of covid-19 by combined network pharmacology and molecular docking. Ann Palliat Med. https://doi.org/10.21037/apm-20-1759

Xia QD, Xun Y, Lu JL, Lu YC, Yang YY, Zhou P, Hu J, Li C, Wang SG (2020) Network pharmacology and molecular docking analyses on lianhua qingwen capsule indicate akt1 is a potential target to treat and prevent covid-19. Cell Proliferat 53(12):e12949. https://doi.org/10.1111/cpr.12949

Ren X, Shao XX, Li XX, Jia XH, Song T, Zhou WY, Wang P, Li Y, Wang XL, Cui QH et al (2020) Identifying potential treatments of covid-19 from traditional chinese medicine (tcm) by using a data-driven approach. J Ethnopharmacol 258:112932. https://doi.org/10.1016/j.jep.2020.112932

Yan H, Zou C (2021) Mechanism and material basis of lianhua qingwen capsule for improving clinical cure rate of covid-19: a study based on network pharmacology and molecular docking technology. J South Med Univ 41(1):20–30. https://doi.org/10.12122/j.issn.1673-4254.2021.01.03

Guney E, Menche J, Vidal M, Barábasi AL (2016) Network-based in silico drug efficacy screening. Nat Commun 7(1):1–13. https://doi.org/10.1038/ncomms10331

Zeng X, Song X, Ma T, Pan X, Zhou Y, Hou Y, Zhang Z, Li K, Karypis G, Cheng F (2020) Repurpose open data to discover therapeutics for covid-19 using deep learning. J Proteome Res 19(11):4624–4636. https://doi.org/10.1021/acs.jproteome.0c00316

Wu Z, Wang Y, Chen L (2013) Network-based drug repositioning. Mol BioSyst 9(6):1268–1281. https://doi.org/10.1007/978-1-4939-8955-3_6

Beck BR, Shin B, Choi Y, Park S, Kang K (2020) Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 18:784–790. https://doi.org/10.1016/j.csbj.2020.03.025

Kim E, Choi AS, Nam H (2019) Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinform 20(10):33–43. https://doi.org/10.1186/s12859-019-2811-8

Hooshmand SA, Ghobadi MZ, Hooshmand SE, Jamalkandi SA, Alavi SM, Masoudi-Nejad AA (2020) A multimodal deep learning-based drug repurposing approach for treatment of covid-19. Mol Diver 1–14. https://doi.org/10.1007/s11030-020-10144-9

Huang K, Fu T, Xiao C, Glass L, Sun J (2020) Deeppurpose: a deep learning based drug repurposing toolkit. arXiv preprint arXiv:2004.08919

Belyaeva A, Cammarata L, Radhakrishnan A, Squires C, Dai Yang K, Shivashankar G, Uhler C (2021) Causal network models of sars-cov-2 expression and aging to identify candidates for drug repurposing. Nat Commun 12(1):1–13. https://doi.org/10.1038/s41467-021-21056-z

Liu R, Wei L, Zhang P (2021) A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nat Mach Intell 3(1):68–75. https://doi.org/10.1038/s42256-020-00276-w

Capodice JL, Chubak BM (2021) Traditional Chinese herbal medicine-potential therapeutic application for the treatment of covid-19. Chin Med 16(1):1–6. https://doi.org/10.1186/s13020-020-00419-6

Wang Z, Li L, Song M, Yan J, Shi J, Yao Y (2021) Evaluating the traditional Chinese medicine (tcm) officially recommended in china for covid-19 using ontology-based side-effect prediction framework (ospf) and deep learning. J Ethnopharmacol 272:113957. https://doi.org/10.1016/j.jep.2021.113957

Liao H, Wen G, Hu Y, Wang C (2019) Convolutional herbal prescription building method from multi-scale facial features. Multimed Tools Appl 78(24):35665–35688. https://doi.org/10.1007/s11042-019-08118-7

Guo F, Tang X, Zhang W, Wei J, Tang S, Wu H, Yang H (2020) Exploration of the mechanism of traditional Chinese medicine by ai approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, xiaoerfupi granules as an example. Pharmacol Res 160:105077. https://doi.org/10.1007/s11042-019-08118-7

Weng H, Liu Z, Yan S, Fan M, Ou A, Chen D, Hao TA (2017) Framework for automated knowledge graph construction towards traditional Chinese medicine. In: International Conference on Health Information Science. Springer, pp. 170–181. https://doi.org/10.1007/978-3-319-69182-4_18

Ruan C, Ma J, Wang Y, Zhang Y, Yang Y, Kraus S (2019) Discovering regularities from traditional Chinese medicine prescriptions via bipartite embedding model. In: IJCAI, pp. 3346–3352. https://doi.org/10.24963/ijcai.2019/464

Wang Y, Jafari M, Tang Y, Tang J (2019) Predicting meridian in Chinese traditional medicine using machine learning approaches. PLoS Comput Biol 15(11):e1007249. https://doi.org/10.1371/journal.pcbi.1007249

Liu Z, Zheng Z, Guo X, Qi L, Gui J, Fu D, Yao Q, Jin L (2019) Attentiveherb: a novel method for traditional medicine prescription generation. IEEE Access 7:139069–139085. https://doi.org/10.1109/ACCESS.2019.2941503