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Current Drug Metabolism

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ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

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Review Article

Machine Learning in Drug Metabolism Study

Author(s): Krishnendu Sinha, Jyotirmoy Ghosh and Parames Chandra Sil*

Volume 23, Issue 13, 2022

Published on: 06 January, 2023

Page: [1012 - 1026] Pages: 15

DOI: 10.2174/1389200224666221227094144

Price: $65

Abstract

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug’s reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug’s metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

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[1]
Zhang, Z.; Tang, W. Drug metabolism in drug discovery and development. Acta Pharm. Sin. B, 2018, 8(5), 721-732.
[http://dx.doi.org/10.1016/j.apsb.2018.04.003] [PMID: 30245961]
[2]
Chen, B.; Dong, J.Q.; Pan, W-J.; Ruiz, A. Pharmacokinetics/pharmacodynamics model-supported early drug development. Curr. Pharm. Biotechnol., 2012, 13(7), 1360-1375.
[http://dx.doi.org/10.2174/138920112800624436] [PMID: 22201585]
[3]
Iga, K. Verification of pharmacokinetic approaches in prior drug development. Yakugaku Zasshi, 2019, 139(3), 437-460.
[http://dx.doi.org/10.1248/yakushi.18-00190] [PMID: 30828023]
[4]
Singh, S. Preclinical pharmacokinetics: An approach towards safer and efficacious drugs. Curr. Drug Metab., 2006, 7(2), 165-182.
[http://dx.doi.org/10.2174/138920006775541552] [PMID: 16472106]
[5]
Zhao, M.; Ma, J.; Li, M.; Zhang, Y.; Jiang, B.; Zhao, X.; Huai, C.; Shen, L.; Zhang, N.; He, L.; Qin, S. Cytochrome P450 enzymes and drug metabolism in humans. Int. J. Mol. Sci., 2021, 22(23), 12808.
[http://dx.doi.org/10.3390/ijms222312808] [PMID: 34884615]
[6]
Guengerich, F.P. Mechanisms of drug toxicity and relevance to pharmaceutical development. Drug Metab. Pharmacokinet., 2011, 26(1), 3-14.
[http://dx.doi.org/10.2133/dmpk.DMPK-10-RV-062] [PMID: 20978361]
[7]
Tang, W.; Lu, A.Y.H. Metabolic bioactivation and drug-related adverse effects: current status and future directions from a pharmaceutical research perspective. Drug Metab. Rev., 2010, 42(2), 225-249.
[http://dx.doi.org/10.3109/03602530903401658] [PMID: 19939207]
[8]
Baillie, T.A.; Rettie, A.E. Role of biotransformation in drug-induced toxicity: influence of intra- and inter-species differences in drug me-tabolism. Drug Metab. Pharmacokinet., 2011, 26(1), 15-29.
[http://dx.doi.org/10.2133/dmpk.DMPK-10-RV-089] [PMID: 20978360]
[9]
Crettol, S.; Petrovic, N.; Murray, M. Pharmacogenetics of phase I and phase II drug metabolism. Curr. Pharm. Des., 2010, 16(2), 204-219.
[http://dx.doi.org/10.2174/138161210790112674] [PMID: 19835560]
[10]
Ma, M.K.; Woo, M.H.; McLeod, H.L. Genetic basis of drug metabolism. Am. J. Health Syst. Pharm., 2002, 59(21), 2061-2069.
[http://dx.doi.org/10.1093/ajhp/59.21.2061] [PMID: 12434718]
[11]
Thompson, A.; Silverman, B.; Dzeng, L.; Treisman, G. Psychotropic Medications and HIV. Clin. Infect. Dis., 2006, 42(9), 1305-1310.
[http://dx.doi.org/10.1086/501454] [PMID: 16586391]
[12]
Erzinger, M.M.; Sturla, S.J. Bioreduction-mediated food-drug interactions: opportunities for oncology nutrition. Chimia (Aarau), 2011, 65(6), 411-415.
[http://dx.doi.org/10.2533/chimia.2011.411] [PMID: 21797170]
[13]
Aitio, M.L.; Vuorenmaa, T. Enhanced metabolism and diminished efficacy of disopyramide by enzyme induction? Br. J. Clin. Pharmacol., 1980, 9(2), 149-152.
[http://dx.doi.org/10.1111/j.1365-2125.1980.tb05825.x] [PMID: 7356902]
[14]
Litsa, E.E.; Das, P.; Kavraki, L.E. Machine learning models in the prediction of drug metabolism: challenges and future perspectives. Expert Opin. Drug Metab. Toxicol., 2021, 17(11), 1245-1247.
[http://dx.doi.org/10.1080/17425255.2021.1998454] [PMID: 34706606]
[15]
Bhattacharyya, S.; Sinha, K.; Sil, P.C. Cytochrome P450s: Mechanisms and biological implications in drug metabolism and its interaction with oxidative stress. Curr. Drug Metab., 2014, 15(7), 719-742.
[http://dx.doi.org/10.2174/1389200215666141125121659]
[16]
Testa, B.; Pedretti, A.; Vistoli, G. Reactions and enzymes in the metabolism of drugs and other xenobiotics. Drug Discov. Today, 2012, 17(11-12), 549-560.
[http://dx.doi.org/10.1016/j.drudis.2012.01.017] [PMID: 22305937]
[17]
Elfaki, I.; Mir, R.; Almutairi, F.M.; Duhier, F.M.A. Cytochrome P450: Polymorphisms and roles in cancer, diabetes and atherosclerosis. Asian Pac. J. Cancer Prev., 2018, 19(8), 2057-2070.
[PMID: 30139042]
[18]
Manikandan, P.; Nagini, S. Cytochrome P450 structure, function and clinical significance: a review. Curr. Drug Targets, 2018, 19(1), 38-54.
[PMID: 28124606]
[19]
Tyzack, J.D.; Kirchmair, J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem. Biol. Drug Des., 2019, 93(4), 377-386.
[http://dx.doi.org/10.1111/cbdd.13445] [PMID: 30471192]
[20]
Kazmi, S.R.; Jun, R.; Yu, M.S.; Jung, C.; Na, D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med., 2019, 106, 54-64.
[http://dx.doi.org/10.1016/j.compbiomed.2019.01.008] [PMID: 30682640]
[21]
Zheng, M.; Luo, X.; Shen, Q.; Wang, Y.; Du, Y.; Zhu, W.; Jiang, H. Site of metabolism prediction for six biotransformations mediated by cytochromes P450. Bioinformatics, 2009, 25(10), 1251-1258.
[http://dx.doi.org/10.1093/bioinformatics/btp140] [PMID: 19286831]
[22]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: machine intelligence ap-proach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[23]
D’Souza, S.; Prema, K.V.; Balaji, S. Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discov. Today, 2020, 25(4), 748-756.
[http://dx.doi.org/10.1016/j.drudis.2020.03.003] [PMID: 32171918]
[24]
Chen, R.; Liu, X.; Jin, S.; Lin, J.; Liu, J. Machine learning for drug-target interaction prediction. Molecules, 2018, 23(9), 2208.
[http://dx.doi.org/10.3390/molecules23092208] [PMID: 30200333]
[25]
Gupta, R.R. Application of artificial intelligence and machine learning in drug discovery. Methods Mol. Biol., 2022, 2390, 113-124.
[http://dx.doi.org/10.1007/978-1-0716-1787-8_4] [PMID: 34731466]
[26]
Sasahara, K.; Shibata, M.; Sasabe, H.; Suzuki, T.; Takeuchi, K.; Umehara, K.; Kashiyama, E. Predicting drug metabolism and pharmacoki-netics features of in-house compounds by a hybrid machine-learning model. Drug Metab. Pharmacokinet., 2021, 39, 100395.
[http://dx.doi.org/10.1016/j.dmpk.2021.100395] [PMID: 33991751]
[27]
Kong, J.; Lee, H.; Kim, D.; Han, S.K.; Ha, D.; Shin, K.; Kim, S. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun., 2020, 11(1), 5485.
[http://dx.doi.org/10.1038/s41467-020-19313-8] [PMID: 33127883]
[28]
Almazroo, O.A.; Miah, M.K.; Venkataramanan, R. Drug Metabolism in the Liver. Clin. Liver Dis., 2017, 21(1), 1-20.
[http://dx.doi.org/10.1016/j.cld.2016.08.001] [PMID: 27842765]
[29]
Susa, S. T.; Preuss, C. v. Introduction to Basics of Pharmacology and Toxicology: Volume 1: General and Molecular Pharmacology: Principles of drug action. Drug Metabolism, 2022.
[30]
Xu, C.; Li, C.Y.T.; Kong, A.N.T. Induction of phase I, II and III drug metabolism/transport by xenobiotics. Arch. Pharm. Res., 2005, 28(3), 249-268.
[http://dx.doi.org/10.1007/BF02977789] [PMID: 15832810]
[31]
Nebert, D.W.; Russell, D.W. Clinical importance of the cytochromes P450. Lancet, 2002, 360(9340), 1155-1162.
[http://dx.doi.org/10.1016/S0140-6736(02)11203-7] [PMID: 12387968]
[32]
Guengerich, F.P. Cytochrome P-450 3A4: regulation and role in drug metabolism. Annu. Rev. Pharmacol. Toxicol., 1999, 39(1), 1-17.
[http://dx.doi.org/10.1146/annurev.pharmtox.39.1.1] [PMID: 10331074]
[33]
Doherty, M.M.; Charman, W.N. The mucosa of the small intestine: how clinically relevant as an organ of drug metabolism? Clin. Pharmacokinet., 2002, 41(4), 235-253.
[http://dx.doi.org/10.2165/00003088-200241040-00001] [PMID: 11978143]
[34]
Guengerich, F.P. Common and uncommon cytochrome P450 reactions related to metabolism and chemical toxicity. Chem. Res. Toxicol., 2001, 14(6), 611-650.
[http://dx.doi.org/10.1021/tx0002583] [PMID: 11409933]
[35]
Jancova, P.; Anzenbacher, P.; Anzenbacherova, E. Phase II drug metabolizing enzymes. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub., 2010, 154(2), 103-116.
[http://dx.doi.org/10.5507/bp.2010.017] [PMID: 20668491]
[36]
Boyland, E.; Chasseaud, L.F. The role of glutathione and glutathione S-transferases in mercapturic acid biosynthesis. Adv. Enzymol. Relat. Areas Mol. Biol., 2006, 32, 173-219.
[http://dx.doi.org/10.1002/9780470122778.ch5] [PMID: 4892500]
[37]
Kume, T. Can drug interactions be evaluated by monitoring plasma drug concentrations? Drug Metab. Pharmacokinet., 2013, 28(4), 289.
[http://dx.doi.org/10.2133/dmpk.DMPK-13-PF-904] [PMID: 23979143]
[38]
Corinna, C.; Mehryar, M.; Umar, S. Deep Boosting Proceedings of the Thirty-First International Conference on Machine Learning. PMLR, 2014, 32(2), 1179-1187.
[39]
Colizzi, M.; Weltens, N.; McGuire, P.; Van Oudenhove, L.; Bhattacharyya, S. Descriptive psychopathology of the acute effects of intrave-nous delta-9-tetrahydrocannabinol administration in humans. Brain Sci., 2019, 9(4), 93.
[http://dx.doi.org/10.3390/brainsci9040093] [PMID: 31027219]
[40]
Baldo, B.A. Opioid analgesic drugs and serotonin toxicity (syndrome): mechanisms, animal models, and links to clinical effects. Arch. Toxicol., 2018, 92(8), 2457-2473.
[http://dx.doi.org/10.1007/s00204-018-2244-6] [PMID: 29916050]
[41]
Kotlinska-Lemieszek, A.; Klepstad, P.; Haugen, D.F. Clinically significant drug-drug interactions involving medications used for symptom control in patients with advanced malignant disease: A systematic review. J. Pain Symptom Manage., 2019, 57(5), 989-998.e1.
[http://dx.doi.org/10.1016/j.jpainsymman.2019.02.006] [PMID: 30776538]
[42]
Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems O’Reilly Media , 2019; p. 851.
[43]
Samuel, A.L. Some studies in machine learning using the game of checkers. IBM J. Res. Develop., 1959, 3(3), 210-229.
[http://dx.doi.org/10.1147/rd.33.0210]
[44]
Burkov, A. Machine Learning Engineering; , 2020. Available from: https://www.saint-gobain.co.in/sites/saint-gobain.co.in/files/webform/apply_for_indec/_sid_/machine-learning-engineering-andriy-burkov-pdf-download-free-book-c6498c8.pdf
[45]
Badillo, S.; Banfai, B.; Birzele, F.; Davydov, I.I.; Hutchinson, L.; Kam-Thong, T.; Siebourg-Polster, J.; Steiert, B.; Zhang, J.D. An introduc-tion to machine learning. Clin. Pharmacol. Ther., 2020, 107(4), 871-885.
[http://dx.doi.org/10.1002/cpt.1796] [PMID: 32128792]
[46]
Burkov, A. The Hundred-Page Machine Learning Book; Quebec City, Canada, 2020, p. 160.
[47]
Kaviani, P.; Dhotre, M.S. Short survey on naive bayes algorithm. Int. J. Adv. Eng. Res. Develop., 2017, 4(11), 607-611.
[48]
Carracedo-Reboredo, P.; Liñares-Blanco, J.; Rodríguez-Fernández, N.; Cedrón, F.; Novoa, F.J.; Carballal, A.; Maojo, V.; Pazos, A. Fernan-dez-Lozano, C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J., 2021, 19, 4538-4558.
[http://dx.doi.org/10.1016/j.csbj.2021.08.011] [PMID: 34471498]
[49]
Rish, I. An empirical study of the naive bayes classifier. IJCAI, 2001, 3(22), 41-46.
[50]
Bayes, T. LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F. R. S. Philos. Trans. R. Soc. Lond., 1763, 53, 370-418.
[http://dx.doi.org/10.1098/rstl.1763.0053]
[51]
Huang, Y.; Li, L. Naive bayes classification algorithm based on small sample set. In: IEEE International Conference on Cloud Computing and Intelligence Systems; Beijing, China, 2011; pp. 34-39.
[http://dx.doi.org/10.1109/CCIS.2011.6045027]
[52]
Cheng, F.; Zhao, Z. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc., 2014, 21(e2), e278-e286.
[http://dx.doi.org/10.1136/amiajnl-2013-002512] [PMID: 24644270]
[53]
Bai, L.Y.; Dai, H.; Xu, Q.; Junaid, M.; Peng, S.L.; Zhu, X.; Xiong, Y.; Wei, D.Q. Prediction of effective drug combinations by an improved naïve bayesian algorithm. Int. J. Mol. Sci., 2018, 19(2), 467.
[http://dx.doi.org/10.3390/ijms19020467] [PMID: 29401735]
[54]
Mei, S.; Zhang, K. A machine learning framework for predicting drug–drug interactions. Sci. Rep., 2021, 11(1), 17619.
[http://dx.doi.org/10.1038/s41598-021-97193-8] [PMID: 34475500]
[55]
Altman, N.S. An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat., 1992, 46, 175.
[56]
Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory, 1967, 13(1), 21-27.
[http://dx.doi.org/10.1109/TIT.1967.1053964]
[57]
Raschka, S. STAT 479: Machine Learning Lecture Notes 2018.
[58]
Yan, C.; Duan, G.; Pan, Y.; Wu, F.X.; Wang, J. DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels. BMC Bioinformatics, 2019, 20(S15)(Suppl. 15), 538.
[http://dx.doi.org/10.1186/s12859-019-3093-x] [PMID: 31874609]
[59]
Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning, Data Mining, Inference, and Prediction; Second Edition; Springer: New York, NY, 2009.
[http://dx.doi.org/10.1007/978-0-387-84858-7]
[60]
Breiman, L. Bagging Predictors. Mach. Learn., 1996, 24, 123-140.
[http://dx.doi.org/10.1007/BF00058655]
[61]
Louppe, G.; Geurts, P. Ensembles on Random Patches. In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012; Lecture Notes in Computer ScienceSpringer: Berlin, Heidelberg, 2012; p. 7523.
[http://dx.doi.org/10.1007/978-3-642-33460-3_28]
[62]
Xuan, P.; Chen, B.; Zhang, T.; Yang, Y. Prediction of drug-target interactions based on network representation learning and ensemble learn-ing. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 18(6), 2671-2681.
[http://dx.doi.org/10.1109/TCBB.2020.2989765]
[63]
Plonka, W.; Stork, C.; Šícho, M.; Kirchmair, J. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorg. Med. Chem., 2021, 46, 116388.
[http://dx.doi.org/10.1016/j.bmc.2021.116388] [PMID: 34488021]
[64]
Holmer, M.; de Bruyn Kops, C.; Stork, C.; Kirchmair, J. CYPstrate: A set of machine learning models for the accurate classification of cy-tochrome p450 enzyme substrates and non-substrates. Molecules, 2021, 26(15), 4678.
[http://dx.doi.org/10.3390/molecules26154678] [PMID: 34361831]
[65]
Friedman, J.H. Greedy function approximation: A gradient boosting machine. Ann. Stat., 2001, 29(5), 1189-1232.
[66]
Hastie, T.; Rosset, S.; Zhu, J.; Zou, H.; Hastie, T. Multi-class AdaBoost. Stat. Interface, 2009, 2(3), 349-360.
[http://dx.doi.org/10.4310/SII.2009.v2.n3.a8]
[67]
Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System, 2016.
[http://dx.doi.org/10.1145/2939672.2939785]
[68]
Dang, L.H.; Dung, N.T.; Quang, L.X.; Hung, L.Q.; Le, N.H.; Le, N.T.N.; Diem, N.T.; Nga, N.T.T.; Hung, S.H.; Le, N.Q.K. Machine learn-ing-based prediction of drug-drug interactions for histamine antagonist using hybrid chemical features. Cells, 2021, 10(11), 3092.
[http://dx.doi.org/10.3390/cells10113092] [PMID: 34831315]
[69]
Wu, Z.; Lei, T.; Shen, C.; Wang, Z.; Cao, D.; Hou, T. ADMET evaluation in drug discovery. 19. reliable prediction of human cytochrome p450 inhibition using artificial intelligence approaches. J. Chem. Inf. Model., 2019, 59(11), 4587-4601.
[http://dx.doi.org/10.1021/acs.jcim.9b00801] [PMID: 31644282]
[70]
Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods., 2000.
[http://dx.doi.org/10.1017/CBO9780511801389]
[71]
Campbell, C.; Ying, Y. Learning with Support Vector Machines. Synth. Lect. Artif. Intell. Machine Learn., 2011, 10, 1.
[72]
Moulin, L.S.; daSilva, A.P.A.; El-Sharkawi, M.A.; Marks, II, R.J. Support vector machines for transient stability analysis of large-scale pow-er systems. IEEE Trans. Power Syst., 2004, 19(2), 818-825.
[http://dx.doi.org/10.1109/TPWRS.2004.826018]
[73]
Michielan, L.; Terfloth, L.; Gasteiger, J.; Moro, S. Comparison of multilabel and single-label classification applied to the prediction of the isoform specificity of cytochrome p450 substrates. J. Chem. Inf. Model., 2009, 49(11), 2588-2605.
[http://dx.doi.org/10.1021/ci900299a] [PMID: 19883102]
[74]
Keum, J.; Nam, H. SELF-BLM: Prediction of drug-target interactions via self-training SVM. PLoS One, 2017, 12(2), e0171839.
[http://dx.doi.org/10.1371/journal.pone.0171839] [PMID: 28192537]
[75]
Mishra, N.K.; Agarwal, S.; Raghava, G.P.S. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol., 2010, 10(1), 8.
[http://dx.doi.org/10.1186/1471-2210-10-8] [PMID: 20637097]
[76]
Chollet, F. Deep Learning with Python, 2nd ed; Deep Learning with Python, 2021.
[77]
Deep Neural Networks | Kaggle Available from: https://www.kaggle.com/code/ryanholbrook/deep-neural-networks
[78]
Bishop, C.M. Pattern Recoginiton and Machine Learning; Springer-Verlag: New York, 2006.
[79]
Bengio, Y.; Goodfellow, I.; Courville, A. Deep Learning; MIT press: Cambridge, London, 2017.
[80]
Cybenko, G. Approximation by superpositions of a sigmoidal function. Math. Control Signal Syst., 1989, 2, 303-314.
[http://dx.doi.org/10.1007/BF02551274]
[81]
Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521, 436-444.
[http://dx.doi.org/10.1038/nature14539]
[82]
Li, X.; Xu, Y.; Lai, L.; Pei, J. Prediction of human cytochrome p450 inhibition using a multitask deep autoencoder neural network. Mol. Pharm., 2018, 15(10), 4336-4345.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00110] [PMID: 29775322]
[83]
Drug-target interactions: Prediction methods and applications. Curr. Protein Pept. Sci., 2018, 19, 1.
[84]
Agamah, F.E.; Mazandu, G.K.; Hassan, R.; Bope, C.D.; Thomford, N.E.; Ghansah, A.; Chimusa, E.R. Computational/in silico methods in drug target and lead prediction. Brief. Bioinform., 2020, 21(5), 1663-1675.
[http://dx.doi.org/10.1093/bib/bbz103] [PMID: 31711157]
[85]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Computational prediction of drug–target interactions using chemogenomic approaches: An empiri-cal survey. Brief. Bioinform., 2019, 20(4), 1337-1357.
[http://dx.doi.org/10.1093/bib/bby002] [PMID: 29377981]
[86]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug–target interaction prediction: databases, web servers and com-putational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[87]
Wu, Z.; Li, W.; Liu, G.; Tang, Y. Network-based methods for prediction of drug-target interactions. Front. Pharmacol., 2018, 9, 1134.
[http://dx.doi.org/10.3389/fphar.2018.01134] [PMID: 30356768]
[88]
Shi, H.; Liu, S.; Chen, J.; Li, X.; Ma, Q.; Yu, B. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics, 2019, 111(6), 1839-1852.
[http://dx.doi.org/10.1016/j.ygeno.2018.12.007] [PMID: 30550813]
[89]
El-Behery, H.; Attia, A.F.; El-Fishawy, N.; Torkey, H. Efficient machine learning model for predicting drug-target interactions with case study for Covid-19. Comput. Biol. Chem., 2021, 93, 107536.
[http://dx.doi.org/10.1016/j.compbiolchem.2021.107536] [PMID: 34271420]
[90]
Sachdev, K.; Gupta, M.K. A comprehensive review of feature based methods for drug target interaction prediction. J. Biomed. Inform., 2019, 93, 103159.
[http://dx.doi.org/10.1016/j.jbi.2019.103159] [PMID: 30926470]
[91]
Che, J.; Chen, L.; Guo, Z-H.; Wang, S. Aorigele, Drug Target Group Prediction with Multiple Drug Networks. Comb. Chem. High Throughput Screen., 2020, 23(4), 274-284.
[http://dx.doi.org/10.2174/18755402OTkzFMzcsTcVY] [PMID: 31267864]
[92]
KEGG DRUG Database. Available from: https://www.genome.jp/kegg/drug/
[93]
Fu, G.; Ding, Y.; Seal, A.; Chen, B.; Sun, Y.; Bolton, E. Predicting drug target interactions using meta-path-based semantic network analy-sis. BMC Bioinformatics, 2016, 17(1), 160.
[http://dx.doi.org/10.1186/s12859-016-1005-x] [PMID: 27071755]
[94]
Lee, S.; Park, K.; Kim, D. Building a drug–target network and its applications. Expert Opin. Drug Discov., 2009, 4(11), 1177-1189.
[http://dx.doi.org/10.1517/17460440903322234] [PMID: 23480435]
[95]
Xie, L.; Li, J.; Xie, L.; Bourne, P.E. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLOS Comput. Biol., 2009, 5(5), e1000387.
[http://dx.doi.org/10.1371/journal.pcbi.1000387] [PMID: 19436720]
[96]
Wild, D.J.; Ding, Y.; Sheth, A.P.; Harland, L.; Gifford, E.M.; Lajiness, M.S. Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discov. Today, 2012, 17(9-10), 469-474.
[http://dx.doi.org/10.1016/j.drudis.2011.12.019] [PMID: 22222943]
[97]
Barabasi, A.L.; Jeong, H.; Neda, Z.; Ravasz, E.; Schubert, A.; Vicsek, T. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 2001, 311(3-4), 590-614.
[98]
McCoubrey, L.E.; Elbadawi, M.; Orlu, M.; Gaisford, S.; Basit, A.W. Machine learning uncovers adverse drug effects on intestinal bacte-ria. Pharmaceutics, 2021, 13, 1026.
[http://dx.doi.org/10.3390/pharmaceutics13071026]
[99]
Gong, Y.; Teng, D.; Wang, Y.; Gu, Y.; Wu, Z.; Li, W.; Tang, Y.; Liu, G. In silico prediction of potential drug‐induced nephrotoxicity with machine learning methods. J. Appl. Toxicol., 2022, 42(10), 1639-1650.
[http://dx.doi.org/10.1002/jat.4331] [PMID: 35429013]
[100]
Kha, Q.H.; Le, V.H.; Hung, T.N.K.; Le, N.Q.K. Development and validation of an efficient MRI radiomics signature for improving the pre-dictive performance of 1p/19q co-deletion in lower-grade gliomas. Cancers, 2021, 13, 5398.
[101]
Wang, N.N.; Wang, X.G.; Xiong, G.L.; Yang, Z.Y.; Lu, A.P.; Chen, X.; Liu, S.; Hou, T.J.; Cao, D.S. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J. Cheminform., 2022, 14(1), 23.
[http://dx.doi.org/10.1186/s13321-022-00602-x] [PMID: 35428354]
[102]
Zhu, E.Y.; Dupuy, A.J. Machine learning approach informs biology of cancer drug response. BMC Bioinformatics, 2022, 23(1)
[http://dx.doi.org/10.1186/s12859-022-04720-z]
[103]
Turki, T.; Wang, J.T.L. Clinical intelligence: New machine learning techniques for predicting clinical drug response. Comput. Biol. Med., 2019, 107, 302-322.
[http://dx.doi.org/10.1016/j.compbiomed.2018.12.017] [PMID: 30771879]
[104]
Turki, T.; Wei, Z.; Wang, J.T.L. A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction. J. Bioinform. Comput. Biol., 2018, 16(3), 1840014.
[http://dx.doi.org/10.1142/S0219720018400140]
[105]
Kocarnik, J.M.; Compton, K.; Dean, F.E.; Fu, W.; Gaw, B.L.; Harvey, J.D.; Henrikson, H.J.; Lu, D.; Pennini, A.; Xu, R.; Ababneh, E.; Ab-basi-Kangevari, M.; Abbastabar, H.; Abd-Elsalam, S.M.; Abdoli, A.; Abedi, A.; Abidi, H.; Abolhassani, H.; Adedeji, I.A.; Adnani, Q.E.S.; Advani, S.M.; Afzal, M.S.; Aghaali, M.; Ahinkorah, B.O.; Ahmad, S.; Ahmad, T.; Ahmadi, A.; Ahmadi, S.; Ahmed Rashid, T.; Ahmed Sa-lih, Y.; Akalu, G.T.; Aklilu, A.; Akram, T.; Akunna, C.J.; Al Hamad, H.; Alahdab, F.; Al-Aly, Z.; Ali, S.; Alimohamadi, Y.; Alipour, V.; Aljunid, S.M.; Alkhayyat, M.; Almasi-Hashiani, A.; Almasri, N.A.; Al-Maweri, S.A.A.; Almustanyir, S.; Alonso, N.; Alvis-Guzman, N.; Amu, H.; Anbesu, E.W.; Ancuceanu, R.; Ansari, F.; Ansari-Moghaddam, A.; Antwi, M.H.; Anvari, D.; Anyasodor, A.E.; Aqeel, M.; Ara-bloo, J.; Arab-Zozani, M.; Aremu, O.; Ariffin, H.; Aripov, T.; Arshad, M.; Artaman, A.; Arulappan, J.; Asemi, Z.; Asghari Jafarabadi, M.; Ashraf, T.; Atorkey, P.; Aujayeb, A.; Ausloos, M.; Awedew, A.F.; Ayala Quintanilla, B.P.; Ayenew, T.; Azab, M.A.; Azadnajafabad, S.; Azari Jafari, A.; Azarian, G.; Azzam, A.Y.; Badiye, A.D.; Bahadory, S.; Baig, A.A.; Baker, J.L.; Balakrishnan, S.; Banach, M.; Bärnighau-sen, T.W.; Barone-Adesi, F.; Barra, F.; Barrow, A.; Behzadifar, M.; Belgaumi, U.I.; Bezabhe, W.M.M.; Bezabih, Y.M.; Bhagat, D.S.; Bhaga-vathula, A.S.; Bhardwaj, N.; Bhardwaj, P.; Bhaskar, S.; Bhattacharyya, K.; Bhojaraja, V.S.; Bibi, S.; Bijani, A.; Biondi, A.; Bisignano, C.; Bjørge, T.; Bleyer, A.; Blyuss, O.; Bolarinwa, O.A.; Bolla, S.R.; Braithwaite, D.; Brar, A.; Brenner, H.; Bustamante-Teixeira, M.T.; Butt, N.S.; Butt, Z.A.; Caetano dos Santos, F.L.; Cao, Y.; Carreras, G.; Catalá-López, F.; Cembranel, F.; Cerin, E.; Cernigliaro, A.; Chakinala, R.C.; Chattu, S.K.; Chattu, V.K.; Chaturvedi, P.; Chimed-Ochir, O.; Cho, D.Y.; Christopher, D.J.; Chu, D.T.; Chung, M.T.; Conde, J.; Cor-tés, S.; Cortesi, P.A.; Costa, V.M.; Cunha, A.R.; Dadras, O.; Dagnew, A.B.; Dahlawi, S.M.A.; Dai, X.; Dandona, L.; Dandona, R.; Darwesh, A.M.; das Neves, J.; De la Hoz, F.P.; Demis, A.B.; Denova-Gutiérrez, E.; Dhamnetiya, D.; Dhimal, M.L.; Dhimal, M.; Dianatinasab, M.; Di-az, D.; Djalalinia, S.; Do, H.P.; Doaei, S.; Dorostkar, F.; dos Santos Figueiredo, F.W.; Driscoll, T.R.; Ebrahimi, H.; Eftekharzadeh, S.; El Tantawi, M.; El-Abid, H.; Elbarazi, I.; Elhabashy, H.R.; Elhadi, M.; El-Jaafary, S.I.; Eshrati, B.; Eskandarieh, S.; Esmaeilzadeh, F.; Etemadi, A.; Ezzikouri, S.; Faisaluddin, M.; Faraon, E.J.A.; Fares, J.; Farzadfar, F.; Feroze, A.H.; Ferrero, S.; Ferro Desideri, L.; Filip, I.; Fischer, F.; Fisher, J.L.; Foroutan, M.; Fukumoto, T.; Gaal, P.A.; Gad, M.M.; Gadanya, M.A.; Gallus, S.; Gaspar Fonseca, M.; Getachew Obsa, A.; Ghafourifard, M.; Ghashghaee, A.; Ghith, N.; Gholamalizadeh, M.; Gilani, S.A.; Ginindza, T.G.; Gizaw, A.T.T.; Glasbey, J.C.; Golechha, M.; Goleij, P.; Gomez, R.S.; Gopalani, S.V.; Gorini, G.; Goudarzi, H.; Grosso, G.; Gubari, M.I.M.; Guerra, M.R.; Guha, A.; Gun-asekera, D.S.; Gupta, B.; Gupta, V.B.; Gupta, V.K.; Gutiérrez, R.A.; Hafezi-Nejad, N.; Haider, M.R.; Haj-Mirzaian, A.; Halwani, R.; Ha-madeh, R.R.; Hameed, S.; Hamidi, S.; Hanif, A.; Haque, S.; Harlianto, N.I.; Haro, J.M.; Hasaballah, A.I.; Hassanipour, S.; Hay, R.J.; Hay, S.I.; Hayat, K.; Heidari, G.; Heidari, M.; Herrera-Serna, B.Y.; Herteliu, C.; Hezam, K.; Holla, R.; Hossain, M.M.; Hossain, M.B.H.; Hos-seini, M.S.; Hosseini, M.; Hosseinzadeh, M.; Hostiuc, M.; Hostiuc, S.; Househ, M.; Hsairi, M.; Huang, J.; Hugo, F.N.; Hussain, R.; Hus-sein, N.R.; Hwang, B.F.; Iavicoli, I.; Ibitoye, S.E.; Ida, F.; Ikuta, K.S.; Ilesanmi, O.S.; Ilic, I.M.; Ilic, M.D.; Irham, L.M.; Islam, J.Y.; Islam, R.M.; Islam, S.M.S.; Ismail, N.E.; Isola, G.; Iwagami, M.; Jacob, L.; Jain, V.; Jakovljevic, M.B.; Javaheri, T.; Jayaram, S.; Jazayeri, S.B.; Jha, R.P.; Jonas, J.B.; Joo, T.; Joseph, N.; Joukar, F.; Jürisson, M.; Kabir, A.; Kahrizi, D.; Kalankesh, L.R.; Kalhor, R.; Kaliyadan, F.; Kalkonde, Y.; Kamath, A.; Kameran Al-Salihi, N.; Kandel, H.; Kapoor, N.; Karch, A.; Kasa, A.S.; Katikireddi, S.V.; Kauppila, J.H.; Ka-vetskyy, T.; Kebede, S.A.; Keshavarz, P.; Keykhaei, M.; Khader, Y.S.; Khalilov, R.; Khan, G.; Khan, M.; Khan, M.N.; Khan, M.A.B.; Khang, Y.H.; Khater, A.M.; Khayamzadeh, M.; Kim, G.R.; Kim, Y.J.; Kisa, A.; Kisa, S.; Kissimova-Skarbek, K.; Kopec, J.A.; Koteeswaran, R.; Koul, P.A.; Koulmane Laxminarayana, S.L.; Koyanagi, A.; Kucuk Bicer, B.; Kugbey, N.; Kumar, G.A.; Kumar, N.; Kumar, N.; Kurmi, O.P.; Kutluk, T.; La Vecchia, C.; Lami, F.H.; Landires, I.; Lauriola, P.; Lee, S.; Lee, S.W.H.; Lee, W.C.; Lee, Y.H.; Leigh, J.; Leong, E.; Li, J.; Li, M.C.; Liu, X.; Loureiro, J.A.; Lunevicius, R.; Magdy Abd El Razek, M.; Majeed, A.; Makki, A.; Male, S.; Malik, A.A.; Mansournia, M.A.; Martini, S.; Masoumi, S.Z.; Mathur, P.; McKee, M.; Mehrotra, R.; Mendoza, W.; Menezes, R.G.; Mengesha, E.W.; Mesregah, M.K.; Mestrovic, T.; Miao Jonasson, J.; Miazgowski, B.; Miazgowski, T.; Michalek, I.M.; Miller, T.R.; Mirzaei, H.; Mirzaei, H.R.; Misra, S.; Mith-ra, P.; Moghadaszadeh, M.; Mohammad, K.A.; Mohammad, Y.; Mohammadi, M.; Mohammadi, S.M.; Mohammadian-Hafshejani, A.; Mo-hammed, S.; Moka, N.; Mokdad, A.H.; Molokhia, M.; Monasta, L.; Moni, M.A.; Moosavi, M.A.; Moradi, Y.; Moraga, P.; Morgado-da-Costa, J.; Morrison, S.D.; Mosapour, A.; Mubarik, S.; Mwanri, L.; Nagarajan, A.J.; Nagaraju, S.P.; Nagata, C.; Naimzada, M.D.; Nangia, V.; Naqvi, A.A.; Narasimha Swamy, S.; Ndejjo, R.; Nduaguba, S.O.; Negoi, I.; Negru, S.M.; Neupane Kandel, S.; Nguyen, C.T.; Nguyen, H.L.T.; Niazi, R.K.; Nnaji, C.A.; Noor, N.M.; Nuñez-Samudio, V.; Nzoputam, C.I.; Oancea, B.; Ochir, C.; Odukoya, O.O.; Ogbo, F.A.; Olagunju, A.T.; Olakunde, B.O.; Omar, E.; Omar Bali, A.; Omonisi, A.E.E.; Ong, S.; Onwujekwe, O.E.; Orru, H.; Ortega-Altamirano, D.V.; Otstavnov, N.; Otstavnov, S.S.; Owolabi, M.O.; P A, M.; Padubidri, J.R.; Pakshir, K.; Pana, A.; Panagiotakos, D.; Panda-Jonas, S.; Pardhan, S.; Park, E.C.; Park, E.K.; Pashazadeh Kan, F.; Patel, H.K.; Patel, J.R.; Pati, S.; Pattanshetty, S.M.; Paudel, U.; Pereira, D.M.; Pereira, R.B.; Perianayagam, A.; Pillay, J.D.; Pirouzpanah, S.; Pishgar, F.; Podder, I.; Postma, M.J.; Pourjafar, H.; Prashant, A.; Preotescu, L.; Rabiee, M.; Rabiee, N.; Radfar, A.; Radhakrishnan, R.A.; Radhakrishnan, V.; Rafiee, A.; Rahim, F.; Rahimzadeh, S.; Rahman, M.; Rahman, M.A.; Rah-mani, A.M.; Rajai, N.; Rajesh, A.; Rakovac, I.; Ram, P.; Ramezanzadeh, K.; Ranabhat, K.; Ranasinghe, P.; Rao, C.R.; Rao, S.J.; Rawassiza-deh, R.; Razeghinia, M.S.; Renzaho, A.M.N.; Rezaei, N.; Rezaei, N.; Rezapour, A.; Roberts, T.J.; Rodriguez, J.A.B.; Rohloff, P.; Romoli, M.; Ronfani, L.; Roshandel, G.; Rwegerera, G.M.; S, M.; Sabour, S.; Saddik, B.; Saeed, U.; Sahebkar, A.; Sahoo, H.; Salehi, S.; Salem, M.R.; Salimzadeh, H.; Samaei, M.; Samy, A.M.; Sanabria, J.; Sankararaman, S.; Santric-Milicevic, M.M.; Sardiwalla, Y.; Sarveazad, A.; Sathian, B.; Sawhney, M.; Saylan, M.; Schneider, I.J.C.; Sekerija, M.; Seylani, A.; Shafaat, O.; Shaghaghi, Z.; Shaikh, M.A.; Shamsoddin, E.; Shan-nawaz, M.; Sharma, R.; Sheikh, A.; Sheikhbahaei, S.; Shetty, A.; Shetty, J.K.; Shetty, P.H.; Shibuya, K.; Shirkoohi, R.; Shivakumar, K.M.; Shivarov, V.; Siabani, S.; Siddappa Malleshappa, S.K.; Silva, D.A.S.; Singh, J.A.; Sintayehu, Y.; Skryabin, V.Y.; Skryabina, A.A.; Soeberg, M.J.; Sofi-Mahmudi, A.; Sotoudeh, H.; Steiropoulos, P.; Straif, K.; Subedi, R.; Sufiyan, M.B.; Sultan, I.; Sultana, S.; Sur, D.; Szerencsés, V.; Szócska, M.; Tabarés-Seisdedos, R.; Tabuchi, T.; Tadbiri, H.; Taherkhani, A.; Takahashi, K.; Talaat, I.M.; Tan, K.K.; Tat, V.Y.; Tedla, B.A.A.; Tefera, Y.G.; Tehrani-Banihashemi, A.; Temsah, M.H.; Tesfay, F.H.; Tessema, G.A.; Thapar, R.; Thavamani, A.; Thoguluva Chandrasekar, V.; Thomas, N.; Tohidinik, H.R.; Touvier, M.; Tovani-Palone, M.R.; Traini, E.; Tran, B.X.; Tran, K.B.; Tran, M.T.N.; Tripa-thy, J.P.; Tusa, B.S.; Ullah, I.; Ullah, S.; Umapathi, K.K.; Unnikrishnan, B.; Upadhyay, E.; Vacante, M.; Vaezi, M.; Valadan Tahbaz, S.; Ve-lazquez, D.Z.; Veroux, M.; Violante, F.S.; Vlassov, V.; Vo, B.; Volovici, V.; Vu, G.T.; Waheed, Y.; Wamai, R.G.; Ward, P.; Wen, Y.F.; Westerman, R.; Winkler, A.S.; Yadav, L.; Yahyazadeh Jabbari, S.H.; Yang, L.; Yaya, S.; Yazie, T.S.Y.; Yeshaw, Y.; Yonemoto, N.; Younis, M.Z.; Yousefi, Z.; Yu, C.; Yuce, D.; Yunusa, I.; Zadnik, V.; Zare, F.; Zastrozhin, M.S.; Zastrozhina, A.; Zhang, J.; Zhong, C.; Zhou, L.; Zhu, C.; Ziapour, A.; Zimmermann, I.R.; Fitzmaurice, C.; Murray, C.J.L.; Force, L.M. cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019. JAMA Oncol., 2022, 8(3), 420-444.
[http://dx.doi.org/10.1001/jamaoncol.2021.6987] [PMID: 34967848]
[106]
Pardoe, D.; Stone, P. Boosting for Regression Transfer Proceedings of the Twenty-Seventh International Conference on Machine Learning 2010.
[107]
Venezian Povoa, L.; Ribeiro, C.H.C.; Silva, I.T. Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response. PLoS One, 2021, 16(7), e0254596.
[http://dx.doi.org/10.1371/journal.pone.0254596] [PMID: 34320000]
[108]
MMRF Research Gateway Login. Available from: https://research.themmrf.org/auth/login?service= [https%3A%2F%2Fresearch.themmrf.org%2Fj_spring_cas_security_check]
[109]
Bloomingdale, P.; Mager, D.E. Machine learning models for the prediction of chemotherapy-induced peripheral neuropathy. Pharm. Res., 2019, 36(2), 35.
[http://dx.doi.org/10.1007/s11095-018-2562-7] [PMID: 30617559]
[110]
Vogl, D.T.; Martin, T.G.; Vij, R.; Hari, P.; Mikhael, J.R.; Siegel, D.; Wu, K.L.; Delforge, M.; Gasparetto, C. Phase I/II study of the novel proteasome inhibitor delanzomib (CEP-18770) for relapsed and refractory multiple myeloma. Leuk. Lymphoma, 2017, 58(8), 1872-1879.
[http://dx.doi.org/10.1080/10428194.2016.1263842] [PMID: 28140719]
[111]
Viira, B. In Silico mining for antimalarial structure-activity knowledge and discovery of novel antimalarial curcuminoids. Molecules, 2016, 21, 853.
[http://dx.doi.org/10.3390/molecules21070853]
[112]
Wang, D.; Liu, W.; Shen, Z.; Jiang, L.; Wang, J.; Li, S.; Li, H. Deep learning based drug metabolites prediction. Front. Pharmacol., 2020, 10, 1586.
[http://dx.doi.org/10.3389/fphar.2019.01586] [PMID: 32082146]
[113]
Hu, J.; Cai, Y.; Li, W.; Liu, G.; Tang, Y. In silico prediction of metabolic epoxidation for drug‐like molecules via machine learning meth-ods. Mol. Inform., 2020, 39(8), 1900178.
[http://dx.doi.org/10.1002/minf.201900178] [PMID: 32162831]
[114]
McCoubrey, L.E.; Thomaidou, S.; Elbadawi, M.; Gaisford, S.; Orlu, M.; Basit, A.W. Machine learning predicts drug metabolism and bioac-cumulation by intestinal microbiota. Pharmaceutics, 2021, 13(12), 2001.
[http://dx.doi.org/10.3390/pharmaceutics13122001] [PMID: 34959282]
[115]
Klünemann, M.; Andrejev, S.; Blasche, S.; Mateus, A.; Phapale, P.; Devendran, S.; Vappiani, J.; Simon, B.; Scott, T.A.; Kafkia, E.; Kon-stantinidis, D.; Zirngibl, K.; Mastrorilli, E.; Banzhaf, M.; Mackmull, M.T.; Hövelmann, F.; Nesme, L.; Brochado, A.R.; Maier, L.; Bock, T.; Periwal, V.; Kumar, M.; Kim, Y.; Tramontano, M.; Schultz, C.; Beck, M.; Hennig, J.; Zimmermann, M.; Sévin, D.C.; Cabreiro, F.; Savitski, M.M.; Bork, P.; Typas, A.; Patil, K.R. Bioaccumulation of therapeutic drugs by human gut bacteria. Nature, 2021, 597(7877), 533-538.
[http://dx.doi.org/10.1038/s41586-021-03891-8] [PMID: 34497420]
[116]
Zimmermann, M.; Zimmermann-Kogadeeva, M.; Wegmann, R.; Goodman, A.L. Mapping human microbiome drug metabolism by gut bac-teria and their genes. Nature, 2019, 570(7762), 462-467.
[http://dx.doi.org/10.1038/s41586-019-1291-3] [PMID: 31158845]
[117]
Javdan, B.; Lopez, J.G.; Chankhamjon, P.; Lee, Y.C.J.; Hull, R.; Wu, Q.; Wang, X.; Chatterjee, S.; Donia, M.S. Personalized mapping of drug metabolism by the human gut microbiome. Cell, 2020, 181(7), 1661-1679.e22.
[http://dx.doi.org/10.1016/j.cell.2020.05.001] [PMID: 32526207]
[118]
Cai, Y.; Yang, H.; Li, W.; Liu, G.; Lee, P.W.; Tang, Y. Computational prediction of site of metabolism for UGT-catalyzed reactions. J. Chem. Inf. Model., 2019, 59(3), 1085-1095.
[http://dx.doi.org/10.1021/acs.jcim.8b00851] [PMID: 30586295]
[119]
Sasahara, K.; Shibata, M.; Sasabe, H.; Suzuki, T.; Takeuchi, K.; Umehara, K.; Kashiyama, E. Feature importance of machine learning pre-diction models shows structurally active part and important physicochemical features in drug design. Drug Metab. Pharmacokinet., 2021, 39, 100401.
[http://dx.doi.org/10.1016/j.dmpk.2021.100401] [PMID: 34089983]

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