Machine Learning Applications in Drug Repurposing
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