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NTIS 바로가기생명과학회지 = Journal of life science, v.33 no.8, 2023년, pp.663 - 679
남재우 (건국대학교 문헌정보학과) , 김영준 (건국대학교 바이오의약학과)
This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis ...
Abdelbasset, W. K., Elsayed, S. H., Alshehri, S., Huwaimel,?B., Alobaida, A., Alsubaiyel, A. M., Alqahtani, A. A., El?Hamd, M. A., Venkatesan, K., AboRas, K. M. and Abourehab, M. A. S. 2022. Development of gbrt model as a novel and robust mathematical model to predict and optimize the solubility of decitabine as an anti-cancer drug.?Molecules 27, 5676.?
Abubaker Bagabir, S., Ibrahim, N. K., Abubaker Bagabir,?H. and Hashem Ateeq, R. 2022. Covid-19 and artificial?intelligence: Genome sequencing, drug development and?vaccine discovery. J. Infect. Public Health 15, 289-296.?
Ahmed, F., Kang, I. S., Kim, K. H., Asif, A., Rahim, C.?S. A., Samantasinghar, A., Memon, F. H. and Choi, K.?H. 2023. Drug repurposing for viral cancers: A paradigm?of machine learning, deep learning, and virtual screening-based approaches. J. Med. Virol. 95, e28693.?
An, Q., Rahman, S., Zhou, J. and Kang, J. J. 2023. A comprehensive review on machine learning in healthcare industry: Classification, restrictions, opportunities and challenges. Sensors (Basel) 23, 4178.?
Arun, R., Suresh, V., Madhavan, C. E. V. and Murty, M.?N. 2010. On finding the natural number of topics with?latent dirichlet allocation: Some observations. Lect. Notes?Artif. Int. 6118, 391-402.?
Badwan, B. A., Liaropoulos, G., Kyrodimos, E., Skaltsas,?D., Tsirigos, A. and Gorgoulis, V. G. 2023. Machine learning approaches to predict drug efficacy and toxicity in?oncology. Cell Rep. Methods 3, 100413.?
Bao, L. 2005. Identifying genes related to chemosensitivity?using support vector machine. Methods Mol. Med. 111, 233-240.?
Bao, L., Wang, Z., Wu, Z., Luo, H., Yu, J., Kang, Y.,?Cao, D. and Hou, T. 2023. Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach. Acta Pharm. Sin. B 13, 54-67.?
Baskin, II, Winkler, D. and Tetko, I. V. 2016. A renaissance of neural networks in drug discovery. Expert Opin.?Drug Discov. 11, 785-795.?
Baylon, J. L., Cilfone, N. A., Gulcher, J. R. and Chittenden, T. W. 2019. Enhancing retrosynthetic reaction prediction with deep learning using multiscale reaction classification. J. Chem. Inf. Model 59, 673-688.?
Bhalla, S. and Lagana, A. 2022. Artificial intelligence for?precision oncology. Adv. Exp. Med. Biol. 1361, 249-268.?
Bian, Y. and Xie, X. Q. 2022. Artificial intelligent deep?learning molecular generative modeling of scaffold-focused and cannabinoid cb2 target-specific small-molecule?sublibraries. Cells 11, 915.?
Bird, A., Oakden-Rayner, L., McMaster, C., Smith, L. A.,?Zeng, M., Wechalekar, M. D., Ray, S., Proudman, S. and?Palmer, L. J. 2022. Artificial intelligence and the future?of radiographic scoring in rheumatoid arthritis: A viewpoint. Arthritis Res. Ther. 24, 268.?
Blei, D., Carin, L. and Dunson, D. 2010. Probabilistic topic models: A focus on graphical model design and applications to document and image analysis. IEEE Signal Process. Mag. 27, 55-65.?
Burton, J., Ijjaali, I., Petitet, F., Michel, A. and Vercauteren, D. P. 2009. Virtual screening for cytochromes p450:?Successes of machine learning filters. Comb. Chem. High?Throughput Screen. 12, 369-382.?
Canizares-Carmenate, Y., Mena-Ulecia, K., MacLeod Carey,?D., Perera-Sardina, Y., Hernandez-Rodriguez, E. W., Marrero-Ponce, Y., Torrens, F. and Castillo-Garit, J. A. 2022. Machine learning approach to discovery of small?molecules with potential inhibitory action against vasoactive metalloproteases. Mol. Divers. 26, 1383-1397.?
Cao, J., Xia, T., Li, J. T., Zhang, Y. D. and Tang, S. 2009.?A density-based method for adaptive lda model selection.?Neurocomputing 72, 1775-1781.?
Cao, Q., Cheng, X. and Liao, S. Y. 2023. A comparison?study of topic modeling based literature analysis by using?full texts and abstracts of scientific articles: A case of covid-19 research. Libr. Hi Tech. 41, 543-569.?
Carter, R., Luchini, A., Liotta, L. and Haymond, A. 2019.?Next generation techniques for determination of protein-protein interactions: Beyond the crystal structure. Curr.?Pathobiol. Rep. 7, 61-71.?
Caruso, F. P., Scala, G., Cerulo, L. and Ceccarelli, M.?2021. A review of covid-19 biomarkers and drug targets: resources and tools. Brief. Bioinform. 22, 701-713.?
Cavalla, D. and Crichton, G. 2023. Drug repurposing:?Known knowns to unknown unknowns - network analysis?of the repurposome. Drug Discov. Today 28, 103639.?
Ceccarelli, F., Natalucci, F., Picciariello, L., Ciancarella,?C., Dolcini, G., Gattamelata, A., Alessandri, C. and Conti,?F. 2023. Application of machine learning models in systemic lupus erythematosus. Int. J. Mol. Sci. 24, 4514.?
Chang, S. S., Huang, H. J. and Chen, C. Y. 2011. Two?birds with one stone? Possible dual-targeting h1n1 inhibitors from traditional chinese medicine. PLoS Comput.?Biol. 7, e1002315.?
Chang, W. T., Liu, C. F., Feng, Y. H., Liao, C. T., Wang,?J. J., Chen, Z. C., Lee, H. C. and Shih, J. Y. 2022. An?artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline.?Arch. Toxicol. 96, 2731-2737.?
Chen, B., Garmire, L., Calvisi, D. F., Chua, M. S., Kelley,?R. K. and Chen, X. 2020. Harnessing big 'omics' data and?ai for drug discovery in hepatocellular carcinoma. Nat.?Rev. Gastroenterol. Hepatol. 17, 238-251.?
Chen, H., Kogej, T. and Engkvist, O. 2018. Cheminformatics in drug discovery, an industrial perspective. Mol.?Inform. 37, e1800041.?
Chen, J. Q., Chen, H. Y., Dai, W. J., Lv, Q. J. and Chen,?C. Y. 2019. Artificial intelligence approach to find lead?compounds for treating tumors. J. Phys. Chem. Lett. 10, 4382-4400.?
Chen, Z., Zhao, M., You, L., Zheng, R., Jiang, Y., Zhang,?X., Qiu, R., Sun, Y., Pan, H., He, T., Wei, X., Chen, Z.,?Zhao, C. and Shang, H. 2022. Developing an artificial intelligence method for screening hepatotoxic compounds in?traditional chinese medicine and western medicine combination. Chin. Med. 17, 58.?
Cheng, F. and Zhao, Z. 2014. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J.?Am. Med. Inform. Assoc. 21, e278-286.?
Coker, E. A., Stewart, A., Ozer, B., Minchom, A., Pickard,?L., Ruddle, R., Carreira, S., Popat, S., O'Brien, M., Raynaud, F., de Bono, J., Al-Lazikani, B. and Banerji, U. 2022. Individualized prediction of drug response and rational combination therapy in nsclc using artificial intelligence-enabled studies of acute phosphoproteomic?changes. Mol. Cancer Ther. 21, 1020-1029.?
Cong, Y., Chan, Y. B., Phillips, C. A., Langston, M. A.?and Ragan, M. A. 2017. Robust inference of genetic exchange communities from microbial genomes using tf-idf.?Front. Microbiol. 8, 21.?
Cong, Y., Chan, Y. B. and Ragan, M. A. 2016. Exploring?lateral genetic transfer among microbial genomes using?tf-idf. Sci. Rep. 6, 29319.?
Cova, T., Vitorino, C., Ferreira, M., Nunes, S., Rondon-Villarreal, P. and Pais, A. 2022. Artificial intelligence and?quantum computing as the next pharma disruptors. Methods Mol. Biol. 2390, 321-347.?
Cui, Q., Lu, S., Ni, B., Zeng, X., Tan, Y., Chen, Y. D.?and Zhao, H. 2020. Improved prediction of aqueous solubility of novel compounds by going deeper with deep?learning. Front. Oncol. 10, 121.?
Das, S., Babu, A., Medha, T., Ramanathan, G., Mukherjee,?A. G., Wanjari, U. R., Murali, R., Kannampuzha, S.,?Gopalakrishnan, A. V., Renu, K., Sinha, D. and George?Priya Doss, C. 2023. Molecular mechanisms augmenting?resistance to current therapies in clinics among cervical cancer patients. Med. Oncol. 40, 149.?
Deveaud, R., SanJuan, E. and Bellot, P. 2014. Accurate?and effective latent concept modeling for ad hoc information retrieval. Document numerique 17, 61-84.?
Dornick, C., Kumar, A., Seidenberger, S., Seidle, E. and?Mukherjee, P. 2021. Analysis of patterns and trends in?covid-19 research. Procedia Comput. Sci. 185, 302-310.?
El-Behery, H., Attia, A. F., El-Fishawy, N. and Torkey,?H. 2022. An ensemble-based drug-target interaction prediction approach using multiple feature information with?data balancing. J. Biol. Eng. 16, 21.?
Elemento, O., Leslie, C., Lundin, J. and Tourassi, G. 2021.?Artificial intelligence in cancer research, diagnosis and?therapy. Nat. Rev. Cancer 21, 747-752.?
Elkhader, J. and Elemento, O. 2022. Artificial intelligence?in oncology: From bench to clinic. Semin. Cancer Biol.?84, 113-128.?
Fan, K., Cheng, L. and Li, L. 2021. Artificial intelligence?and machine learning methods in predicting anti-cancer?drug combination effects. Brief Bioinform. 22, bbab271.?
Feng, H., Gao, K., Chen, D., Shen, L., Robison, A. J.,?Ellsworth, E. and Wei, G. W. 2022. Machine learning?analysis of cocaine addiction informed by dat, sert, and?net-based interactome networks. J. Chem. Theory Comput.?18, 2703-2719.?
Galati, S., Di Stefano, M., Martinelli, E., Macchia, M.,?Martinelli, A., Poli, G. and Tuccinardi, T. 2022. Venompred: A machine learning based platform for molecular?toxicity predictions. Int. J. Mol. Sci. 23, 2105.?
Gaurav, A., Agrawal, N., Al-Nema, M. and Gautam, V. 2022. Computational approaches in the discovery and development of therapeutic and prophylactic agents for viral?diseases. Curr. Top. Med. Chem. 22, 2190-2206.?
Gerdes, H., Casado, P., Dokal, A., Hijazi, M., Akhtar, N.,?Osuntola, R., Rajeeve, V., Fitzgibbon, J., Travers, J.,?Britton, D., Khorsandi, S. and Cutillas, P. R. 2021. Drug?ranking using machine learning systematically predicts the?efficacy of anti-cancer drugs. Nat. Commun. 12, 1850.?
Gimeno, M., Sada Del Real, K. and Rubio, A. 2023.?Precision oncology: A review to assess interpretability in?several explainable methods. Brief. Bioinform. 24, bbad200.?
Goller, A. H., Kuhnke, L., Ter Laak, A., Meier, K. and?Hillisch, A. 2022. Machine learning applied to the modeling of pharmacological and admet endpoints. Methods?Mol. Biol. 2390, 61-101.?
Gong, J. N., Zhao, L., Chen, G., Chen, X., Chen, Z. D.?and Chen, C. Y. 2021. A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus. Mol.?Divers 25, 1375-1393.?
Gorostiola Gonzalez, M., Janssen, A. P. A., IJzerman, A.?P., Heitman, L. H. and van Westen, G. J. P. 2022.?Oncological drug discovery: Ai meets structure-based computational research. Drug Discov. Today 27, 1661-1670.?
Griffiths, T. L. and Steyvers, M. 2004. Finding scientific?topics. Proc. Natl. Acad. Sci. USA. 101 Suppl 1, 5228-5235.?
Grisoni, F. and Schneider, G. 2019. De novo molecular?design with generative long short-term memory. Chimia?(Aarau) 73, 1006-1011.?
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta,?R. K. and Kumar, P. 2021. Artificial intelligence to deep?learning: Machine intelligence approach for drug discovery. Mol. Divers 25, 1315-1360.?
Gupta, R. R. 2022. Application of artificial intelligence?and machine learning in drug discovery. Methods Mol.?Biol. 2390, 113-124.?
He, X., Zhao, L., Zhong, W., Chen, H. Y., Shan, X., Tang,?N. and Chen, C. Y. 2020. Insight into potent leads for?alzheimer's disease by using several artificial intelligence?algorithms. Biomed. Pharmacother. 129, 110360.?
Heikel, E. and Espinosa-Leal, L. 2022. Indoor scene recognition via object detection and tf-idf. J. Imaging 8, 209.?
Hermansyah, O., Bustamam, A. and Yanuar, A. 2021.?Virtual screening of dipeptidyl peptidase-4 inhibitors using?quantitative structure-activity relationship-based artificial?intelligence and molecular docking of hit compounds.?Comput. Biol. Chem. 95, 107597.?
Hu, F., Wang, L., Hu, Y., Wang, D., Wang, W., Jiang,?J., Li, N. and Yin, P. 2021. A novel framework integrating?ai model and enzymological experiments promotes identification of sars-cov-2 3cl protease inhibitors and activity-based probe. Brief. Bioinform. 22, bbab301.?
Hulsen, T. 2022. Literature analysis of artificial intelligence in biomedicine. Ann. Transl. Med. 10, 1284.?
Hung, T. N. K., Le, N. Q. K., Le, N. H., Van Tuan, L.,?Nguyen, T. P., Thi, C. and Kang, J. H. 2022. An ai-based?prediction model for drug-drug interactions in osteoporosis?and paget's diseases from smiles. Mol. Inform. 41, e2100 264.?
Iftikhar, S., Karim, A. M., Karim, A. M., Karim, M. A.,?Aslam, M., Rubab, F., Malik, S. K., Kwon, J. E., Hussain,?I., Azhar, E. I., Kang, S. C. and Yasir, M. 2023. Prediction?and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models. J.?Environ. Manage. 328, 116969.?
Ishii, S., Takamatsu, M., Ninomiya, H., Inamura, K.,?Horai, T., Iyoda, A., Honma, N., Hoshi, R., Sugiyama,?Y., Yanagitani, N., Mun, M., Abe, H., Mikami, T. and?Takeuchi, K. 2022. Machine learning-based gene alteration prediction model for primary lung cancer using cytologic images. Cancer Cytopathol. 130, 812-823.?
Jamal, S. and Scaria, V. 2013. Cheminformatic models?based on machine learning for pyruvate kinase inhibitors?of leishmania mexicana. BMC Bioinformatics 14, 329.?
Jayaprakash, V., Saravanan, T., Ravindran, K., Prabha, T.,?Selvaraj, J., Jayapalan, S., Chaitanya, M. and Sivakumar,?T. 2023. Relevance of machine learning to predict the inhibitory activity of small thiazole chemicals on estrogen?receptor. Curr. Comput. Aided Drug Des. 19, 37-50.?
Ji, Y. A., Nam, S. J., Kim, H. G., Lee, J. and Lee, S.?K. 2018. Research topics and trends in medical education?by social network analysis. BMC Med. Educ. 18, 222.?
Jiang, J., Ouyang, D. and Williams, R. O. 3rd. 2023.?Predicting glass-forming ability of pharmaceutical com- pounds by using machine learning technologies. AAPS?PharmSciTech 24, 103.?
Kang, H., Yu, Z. and Gong, Y. 2017. Initializing and?growing a database of health information technology (hit)?events by using tf-idf and biterm topic modeling. AMIA?Annu. Symp. Proc. 2017, 1024-1033.?
Kang, H. J., Han, J. and Kwon, G. H. 2021. Determining?the intellectual structure and academic trends of smart?home health care research: Coword and topic analyses.?J. Med. Internet Res. 23, e19625.
Karim, M., Saad Missen, M. M., Umer, M., Fida, A.,?Eshmawi, A. A., Mohamed, A. and Ashraf, I. 2022.?Comprehension of polarity of articles by citation sentiment?analysis using tf-idf and ml classifiers. PeerJ Comput. Sci.?8, e1107.?
Kaushal, K., Sarma, P., Rana, S. V., Medhi, B. and Naithani, M. 2022. Emerging role of artificial intelligence in?therapeutics for covid-19: A systematic review. J. Biomol.?Struct. Dyn. 40, 4750-4765.?
Kaushik, A. C., Li, M., Mehmood, A., Dai, X. and Wei,?D. Q. 2021. Acps: An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data.?Chem. Biol. Drug Des. 97, 372-382.?
Kha, Q. H., Le, V. H., Hung, T. N. K., Nguyen, N. T.?K. and Le, N. Q. K. 2023. Development and validation?of an explainable machine learning-based prediction model for drug-food interactions from chemical structures.?Sensors(Basel) 23, 3962.?
Kimani, S. W., Owen, J., Green, S. R., Li, F., Li, Y., Dong, A., Brown, P. J., Ackloo, S., Kuter, D., Yang, C.,?MacAskill, M., MacKinnon, S. S., Arrowsmith, C. H.,?Schapira, M., Shahani, V. and Halabelian, L. 2023.?Discovery of a novel dcaf1 ligand using a drug-target interaction prediction model: Generalizing machine learning?to new drug targets. J. Chem. Inf. Model 63, 4070-4078.?
Koromina, M., Pandi, M. T. and Patrinos, G. P. 2019.?Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. OMICS?23, 539-548.?
Koutroumpa, N. M., Papavasileiou, K. D., Papadiamantis,?A. G., Melagraki, G. and Afantitis, A. 2023. A systematic?review of deep learning methodologies used in the drug?discovery process with emphasis on in vivo validation. Int.?J. Mol. Sci. 24, 6573.?
Kumar, R., Yadav, G., Kuddus, M., Ashraf, G. M. and?Singh, R. 2023. Unlocking the microbial studies through?computational approaches: How far have we reached??Environ. Sci. Pollut. Res. Int. 30, 48929-48947.?
Kumar, S. A., Ananda Kumar, T. D., Beeraka, N. M.,?Pujar, G. V., Singh, M., Narayana Akshatha, H. S. and?Bhagyalalitha, M. 2022. Machine learning and deep learning in data-driven decision making of drug discovery and?challenges in high-quality data acquisition in the pharmaceutical industry. Future Med. Chem. 14, 245-270.?
Li, D., Hu, J., Zhang, L., Li, L., Yin, Q., Shi, J., Guo,?H., Zhang, Y. and Zhuang, P. 2022. Deep learning and?machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of traditional chinese medicine.?Eur. J. Pharmacol. 933, 175260.?
Li, G., Lin, P., Wang, K., Gu, C. C. and Kusari, S. 2022.?Artificial intelligence-guided discovery of anticancer lead?compounds from plants and associated microorganisms.?Trends Cancer 8, 65-80.?
Li, J. Y., Chen, H. Y., Dai, W. J., Lv, Q. J. and Chen,?C. Y. 2019. Artificial intelligence approach to investigate?the longevity drug. J. Phys. Chem. Lett. 10, 4947-4961.?
Li, L., Xiong, Y., Zhang, Z. Y., Guo, Q., Xu, Q., Liow,?H. H., Zhang, Y. H. and Wei, D. Q. 2015. Improved feature-based prediction of snps in human cytochrome p450?enzymes. Interdiscip. Sci. 7, 65-77.?
Li, X., Cheng, W., Yang, S., Liang, F., Wang, H., Feng,?Y. and Wang, Y. 2022. Establishment of a 13 genes-based?molecular prediction score model to discriminate the neurotoxic potential of food relevant-chemicals. Toxicol. Lett.?355, 1-18.?
Liang, G., Fan, W., Luo, H. and Zhu, X. 2020. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed. Pharmacother. 128, 110255.?
Lien, S. T., Lin, T. E., Hsieh, J. H., Sung, T. Y., Chen,?J. H. and Hsu, K. C. 2023. Establishment of extensive?artificial intelligence models for kinase inhibitor prediction: Identification of novel pdgfrb inhibitors. Comput.?Biol. Med. 156, 106722.?
Lin, Z., Cheng, Y. T. and Cheung, B. M. Y. 2023. Machine?learning algorithms identify hypokalaemia risk in people?with hypertension in the united states national health and?nutrition examination survey 1999-2018. Ann. Med. 55, 2209336.?
Liu, Y., Lim, H. and Xie, L. 2022. Exploration of chemical?space with partial labeled noisy student self-training and?self-supervised graph embedding. BMC Bioinformatics 23, 158.?
Lossio-Ventura, J. A., Gonzales, S., Morzan, J., Alatrista-Salas, H., Hernandez-Boussard, T. and Bian, J. 2021.?Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artif. Intell. Med. 117, 102096.?
Lu, W. W., Chen, X., Ni, J. L., Cai, W. J., Zhu, S. L.,?Fei, A. H. and Wang, X. S. 2021. Study on the medication?rule of traditional chinese medicine in the treatment of?acute pancreatitis based on machine learning technology.?Ann. Palliat. Med. 10, 10616-10625.?
Maassen, O., Fritsch, S., Palm, J., Deffge, S., Kunze, J.,?Marx, G., Riedel, M., Schuppert, A. and Bickenbach, J. 2021. Future medical artificial intelligence application requirements and expectations of physicians in german university hospitals: Web-based survey. J. Med. Internet Res.?23, e26646.?
Malone, B., Simovski, B., Moline, C., Cheng, J., Gheorghe,?M., Fontenelle, H., Vardaxis, I., Tennoe, S., Malmberg, J.?A., Stratford, R. and Clancy, T. 2020. Artificial intelligence predicts the immunogenic landscape of sars-cov-2?leading to universal blueprints for vaccine designs. Sci.?Rep. 10, 22375.?
Marechal, E. 2008. Chemogenomics: A discipline at the?crossroad of high throughput technologies, biomarker research, combinatorial chemistry, genomics, cheminformatics, bioinformatics and artificial intelligence. Comb. Chem.?High Throughput Screen 11, 583-586.?
Martin, R. and Yu, K. 2006. Assessing performance of?prediction rules in machine learning. Pharmacogenomics?7, 543-550.?
Martinelli, D. D. 2022. Generative machine learning for?de novo drug discovery: A systematic review. Comput.?Biol. Med. 145, 105403.?
McDonagh, J. L., Nath, N., De Ferrari, L., van Mourik,?T. and Mitchell, J. B. 2014. Uniting cheminformatics and?chemical theory to predict the intrinsic aqueous solubility?of crystalline druglike molecules. J. Chem. Inf. Model 54, 844-856.?
McNair, D. 2023. Artificial intelligence and machine?learning for lead-to-candidate decision-making and beyond.?Annu. Rev. Pharmacol. Toxicol. 63, 77-97.?
Medina-Franco, J. L., Martinez-Mayorga, K., Fernandez-de?Gortari, E., Kirchmair, J. and Bajorath, J. 2021. Rationality over fashion and hype in drug design. F1000Res. 10, Chem Inf Sci-397.?
Mishra, N. K. 2011. Computational modeling of p450s for?toxicity prediction. Expert Opin. Drug Metab. Toxicol. 7, 1211-1231.?
Mohammed, M. and Omar, N. 2020. Question classification based on bloom's taxonomy cognitive domain using modified tf-idf and word2vec. PLoS One 15, e0230 442.?
Momtazmanesh, S., Nowroozi, A. and Rezaei, N. 2022.?Artificial intelligence in rheumatoid arthritis: Current status and future perspectives: A state-of-the-art review.?Rheumatol. Ther. 9, 1249-1304.?
Morales Pantoja, I. E., Smirnova, L., Muotri, A. R., Wahlin, K. J., Kahn, J., Boyd, J. L., Gracias, D. H., Harris,?T. D., Cohen-Karni, T., Caffo, B. S., Szalay, A. S., Han,?F., Zack, D. J., Etienne-Cummings, R., Akwaboah, A.,?Romero, J. C., Alam El Din, D. M., Plotkin, J. D., Paulhamus, B. L., Johnson, E. C., Gilbert, F., Curley, J. L.,?Cappiello, B., Schwamborn, J. C., Hill, E. J., Roach, P.,?Tornero, D., Krall, C., Parri, R., Sille, F., Levchenko, A., Jabbour, R. E., Kagan, B. J., Berlinicke, C. A., Huang,?Q., Maertens, A., Herrmann, K., Tsaioun, K., Dastgheyb,?R., Habela, C. W., Vogelstein, J. T. and Hartung, T. 2023. First organoid intelligence (oi) workshop to form an oi community. Front. Artif. Intell. 6, 1116870.?
Moshawih, S., Goh, H. P., Kifli, N., Idris, A. C., Yassin,?H., Kotra, V., Goh, K. W., Liew, K. B. and Ming, L.?C. 2022. Synergy between machine learning and natural?products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem. Biol. Drug?Des. 100, 185-217.?
Moussa, M. and Mandoiu, II. 2018. Single cell rna-seq?data clustering using tf-idf based methods. BMC Genomics 19, 569.?
Nag, S., Baidya, A. T. K., Mandal, A., Mathew, A. T.,?Das, B., Devi, B. and Kumar, R. 2022. Deep learning?tools for advancing drug discovery and development. 3?Biotech. 12, 110.?
Nagarajan, N., Yapp, E. K. Y., Le, N. Q. K., Kamaraj,?B., Al-Subaie, A. M. and Yeh, H. Y. 2019. Application?of computational biology and artificial intelligence technologies in cancer precision drug discovery. Biomed. Res.?Int. 2019, 8427042.?
Najmi, M., Ayari, M. A., Sadeghsalehi, H., Vaferi, B.,?Khandakar, A., Chowdhury, M. E. H., Rahman, T. and?Jawhar, Z. H. 2022. Estimating the dissolution of anti- cancer drugs in supercritical carbon dioxide with a stacked?machine learning model. Pharmaceutics 14, 1632.?
Nayarisseri, A., Khandelwal, R., Madhavi, M., Selvaraj,?C., Panwar, U., Sharma, K., Hussain, T. and Singh, S.?K. 2020. Shape-based machine learning models for the?potential novel covid-19 protease inhibitors assisted by?molecular dynamics simulation. Curr. Top. Med. Chem.?20, 2146-2167.?
Nayarisseri, A., Khandelwal, R., Tanwar, P., Madhavi,?M., Sharma, D., Thakur, G., Speck-Planche, A. and Singh,?S. K. 2021. Artificial intelligence, big data and machine?learning approaches in precision medicine & drug discovery. Curr. Drug Targets 22, 631-655.?
Nigam, A. K., Ojha, A. A., Li, J. G., Shi, D., Bhatnagar,?V., Nigam, K. B., Abagyan, R. and Nigam, S. K. 2021.?Molecular properties of drugs handled by kidney oats and?liver oatps revealed by chemoinformatics and machine?learning: Implications for kidney and liver disease.?Pharmaceutics 13, 1720.?
Niu, Q., Li, H., Tong, L., Liu, S., Zong, W., Zhang, S.,?Tian, S., Wang, J., Liu, J., Li, B., Wang, Z. and Zhang,?H. 2023. Tcmfp: A novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms. Brief Bioinform.?24, bbad102.?
Noorain, L., Nguyen, V., Kim, H. W. and Nguyen, L.?T. B. 2023. A machine learning approach for plga nanoparticles in antiviral drug delivery. Pharmaceutics 15, 495.?
Nowak, D., Bachorz, R. A. and Hoffmann, M. 2023.?Neural networks in the design of molecules with affinity?to selected protein domains. Int. J. Mol. Sci. 24, 1762.?
Nussinov, R., Zhang, M., Liu, Y. and Jang, H. 2023.?Alphafold, allosteric, and orthosteric drug discovery:?Ways forward. Drug Discov. Today 28, 103551.?
Overduin, M., Kervin, T. A., Klarenbach, Z., Adra, T.?R. C. and Bhat, R. K. 2023. Comprehensive classification?of proteins based on structures that engage lipids by?composel. Biophys. Chem. 295, 106971.?
Ozcelik, R., van Tilborg, D., Jimenez-Luna, J. and Grisoni,?F. 2023. Structure-based drug discovery with deep learning. Chembiochem 24, e202200776.?
Pandiyan, S. and Wang, L. 2022. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput. Biol. Med.?150, 106140.?
Pirzada, R. H., Ahmad, B., Qayyum, N. and Choi, S.?2023. Modeling structure-activity relationships with machine learning to identify gsk3-targeted small molecules as potential covid-19 therapeutics. Front. Endocrinol.?(Lausanne) 14, 1084327.?
Popa, S. L., Pop, C., Dita, M. O., Brata, V. D., Bolchis,?R., Czako, Z., Saadani, M. M., Ismaiel, A., Dumitrascu,?D. I., Grad, S., David, L., Cismaru, G. and Padureanu,?A. M. 2022. Deep learning and antibiotic resistance.?Antibiotics(Basel) 11, 1674.?
Poweleit, E. A., Vinks, A. A. and Mizuno, T. 2023.?Artificial intelligence and machine learning approaches?to facilitate therapeutic drug management and model-informed precision dosing. Ther. Drug Monit. 45, 143-150.?
Prabakaran, P., Rao, S. P. and Wendt, M. 2021. Animal?immunization merges with innovative technologies: A new paradigm shift in antibody discovery. MAbs. 13, 1924347.?
Priya, S., Tripathi, G., Singh, D. B., Jain, P. and Kumar,?A. 2022. Machine learning approaches and their applications in drug discovery and design. Chem. Biol. Drug?Des. 100, 136-153.?
Purpura, A., Giorgianni, D., Orru, G., Melis, G. and Sartori, G. 2022. Identifying single-item faked responses in?personality tests: A new tf-idf-based method. PLoS One?17, e0272970.?
Qiu, H. Y., Clausen, R. P., He, Y. and Zhu, H. L. 2021.?Artificial intelligence and cheminformatics-guided mod- ern privileged scaffold research. Curr. Top. Med. Chem.?21, 2593-2608.?
Ranjan, A., Fernandez-Baca, D., Tripathi, S. and Deepak,?A. 2022. An ensemble tf-idf based approach to protein?function prediction via sequence segmentation. IEEE/ACM Trans. Comput. Biol. Bioinform. 19, 2685-2696.?
Ranson, J. M., Bucholc, M., Lyall, D., Newby, D.,?Winchester, L., Oxtoby, N. P., Veldsman, M., Rittman,?T., Marzi, S., Skene, N., Al Khleifat, A., Foote, I. F.,?Orgeta, V., Kormilitzin, A., Lourida, I. and Llewellyn,?D. J. 2023. Harnessing the potential of machine learning?and artificial intelligence for dementia research. Brain?Inform. 10, 6.?
Rema, J., Novais, F. and Telles-Correia, D. 2022. Precision psychiatry: Machine learning as a tool to find new pharmacological targets. Curr. Top. Med. Chem. 22, 1261-1269.?
Sahu, A., Mishra, J. and Kushwaha, N. 2022. Artificial?intelligence (ai) in drugs and pharmaceuticals. Comb.?Chem. High Throughput Screen 25, 1818-1837.?
Sanawar, R., Sahayasheela, V. J., Sarath, P. and Dan,?V. M. 2023. Discoidin domain receptor 1 inhibitors:?Advances and future directions for novel therapeutics?with aid of DNA encoded library screens and artificial?intelligence. Mini Rev. Med. Chem. 23, 1507-1513.?
Sasahara, K., Shibata, M., Sasabe, H., Suzuki, T., Takeuchi, K., Umehara, K. and Kashiyama, E. 2021. Predicting?drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.?Drug Metab. Pharmacokinet. 39, 100395.?
Selvaraj, C., Chandra, I. and Singh, S. K. 2022. Artificial?intelligence and machine learning approaches for drug?design: Challenges and opportunities for the pharmaceutical industries. Mol. Divers. 26, 1893-1913.?
Serafim, M. S. M., Gertrudes, J. C., Costa, D. M. A.,?Oliveira, P. R., Maltarollo, V. G. and Honorio, K. M.?2021. Knowing and combating the enemy: A brief review on sars-cov-2 and computational approaches applied to?the discovery of drug candidates. Biosci. Rep. 41, BSR 20202616.?
Seyedtabib, M. and Kamyari, N. 2023. Predicting polypharmacy in half a million adults in the iranian population: Comparison of machine learning algorithms. BMC?Med. Inform. Decis. Mak. 23, 84.?
Sharma, P., Dahiya, S., Kaur, P. and Kapil, A. 2023.?Computational biology: Role and scope in taming antimicrobial resistance. Indian J. Med. Microbiol. 41, 33-38.?
Shimazaki, T. and Tachikawa, M. 2022. Collaborative?approach between explainable artificial intelligence and?simplified chemical interactions to explore active ligands?for cyclin-dependent kinase 2. ACS Omega 7, 10372-10381.?
Sicular, S., Alpaslan, M., Ortega, F. A., Keathley, N.,?Venkatesh, S., Jones, R. M. and Lindsey, R. V. 2022.?Reevaluation of missed lung cancer with artificial?intelligence. Respir. Med. Case Rep. 39, 101733.?
Singh, A. K., Ling, J. and Malviya, R. 2023. Prediction?of cancer treatment using advancements in machine?learning. Recent Pat. Anticancer Drug Discov. 18, 364-378.?
Singh, V., Shrivastava, S., Kumar Singh, S., Kumar, A.?and Saxena, S. 2022. Accelerating the discovery of antifungal peptides using deep temporal convolutional net- works. Brief Bioinform. 23, bbac008.?
Singla, R., Aggarwal, S., Bindra, J., Garg, A. and Singla,?A. 2022. Developing clinical decision support system using machine learning methods for type 2 diabetes drug?management. Indian J. Endocrinol. Metab. 26, 44-49.?
Singla, R. K., Joon, S., Sinha, B., Kamal, M. A., Simal-Gandara, J., Xiao, J. and Shen, B. 2023. Current trends?in natural products for the treatment and management?of dementia: Computational to clinical studies. Neurosci.?Biobehav. Rev. 147, 105106.?
Srisongkram, T. and Weerapreeyakul, N. 2022. Drug repurposing against kras mutant g12c: A machine learning,?molecular docking, and molecular dynamics study. Int.?J. Mol. Sci. 24, 669.?
Srivathsa, A. V., Sadashivappa, N. M., Hegde, A. K.,?Radha, S., Mahesh, A. R., Ammunje, D. N., Sen, D.,?Theivendren, P., Govindaraj, S., Kunjiappan, S. and?Pavadai, P. 2023. A review on artificial intelligence approaches and rational approaches in drug discovery. Curr.?Pharm. Des. 29, 1180-1192.?
Tang, K., Zhu, R., Li, Y. and Cao, Z. 2011. Discrimination?of approved drugs from experimental drugs by learning?methods. BMC Bioinformatics 12, 157.?
Tanoli, Z., Vaha-Koskela, M. and Aittokallio, T. 2021.?Artificial intelligence, machine learning, and drug re- purposing in cancer. Expert Opin. Drug Discov. 16, 977-989.?
Terranova, N., Venkatakrishnan, K. and Benincosa, L.?J. 2021. Application of machine learning in translational?medicine: Current status and future opportunities. AAPS?J. 23, 74.?
Tian, S., Wang, J., Li, Y., Xu, X. and Hou, T. 2012.?Drug-likeness analysis of traditional chinese medicines:?Prediction of drug-likeness using machine learning approaches. Mol. Pharm. 9, 2875-2886.?
Tran, T. T. V., Tayara, H. and Chong, K. T. 2023. Recent?studies of artificial intelligence on in silico drug distribution prediction. Int. J. Mol. Sci. 24, 1815.?
Tripathi, A., Misra, K., Dhanuka, R. and Singh, J. P. 2023. Artificial intelligence in accelerating drug discovery and development. Recent Pat. Biotechnol. 17, 9-23.?
Trisciuzzi, D., Villoutreix, B. O., Siragusa, L., Baroni,?M., Cruciani, G. and Nicolotti, O. 2023. Targeting protein-protein interactions with low molecular weight and short peptide modulators: Insights on disease pathways?and starting points for drug discovery. Expert Opin. Drug?Discov. 18, 737-752.?
Tseng, Y. J., Chuang, P. J. and Appell, M. 2023. When?machine learning and deep learning come to the big data?in food chemistry. ACS Omega 8, 15854-15864.?
Uesawa, Y. 2020. [Ai-based qsar modeling for prediction?of active compounds in mie/aop]. Yakugaku Zasshi 140, 499-505.?
Vemula, D., Jayasurya, P., Sushmitha, V., Kumar, Y. N.?and Bhandari, V. 2023. Cadd, ai and ml in drug discovery: A comprehensive review. Eur. J. Pharm. Sci. 181, 106324.?
Veselkov, K., Gonzalez, G., Aljifri, S., Galea, D., Mirnezami, R., Youssef, J., Bronstein, M. and Laponogov, I. 2019. Hyperfoods: Machine intelligent mapping of cancer-beating molecules in foods. Sci. Rep. 9, 9237.?
Vidovic, T., Dakhovnik, A., Hrabovskyi, O., MacArthur,?M. R. and Ewald, C. Y. 2023. Ai-predicted mtor inhibitor?reduces cancer cell proliferation and extends the lifespan?of c. Elegans. Int. J. Mol. Sci. 24, 7850.?
Villalobos-Alva, J., Ochoa-Toledo, L., Villalobos-Alva,?M. J., Aliseda, A., Perez-Escamirosa, F., Altamirano-Bustamante, N. F., Ochoa-Fernandez, F., Zamora-Solis,?R., Villalobos-Alva, S., Revilla-Monsalve, C., KemperValverde, N. and Altamirano-Bustamante, M. M. 2022.?Protein science meets artificial intelligence: A systematic?review and a biochemical meta-analysis of an inter-field.?Front. Bioeng. Biotechnol. 10, 788300.?
Vishnoi, S., Matre, H., Garg, P. and Pandey, S. K. 2020.?Artificial intelligence and machine learning for protein?toxicity prediction using proteomics data. Chem. Biol.?Drug Des. 96, 902-920.?
Vo, D., Ghosh, P. and Sahoo, D. 2023. Artificial intelligence-guided discovery of gastric cancer continuum.?Gastric Cancer 26, 286-297.?
Wang, L., Song, Y., Wang, H., Zhang, X., Wang, M.,?He, J., Li, S., Zhang, L., Li, K. and Cao, L. 2023.?Advances of artificial intelligence in anti-cancer drug design: A review of the past decade. Pharmaceuticals?(Basel) 16, 253.?
Wang, M., Wang, J., Weng, G., Kang, Y., Pan, P., Li,?D., Deng, Y., Li, H., Hsieh, C. Y. and Hou, T. 2022.?Remode: A deep learning-based web server for target-specific drug design. J. Cheminform. 14, 84.?
Wang, M., Zhou, X., King, R. W. and Wong, S. T. 2007.?Context based mixture model for cell phase identification?in automated fluorescence microscopy. BMC Bioinformatics 8, 32.?
Wang, Y. Y. and Acero, A. 2007. Maximum entropy?model parameterization with TF*IDF weighted vector?space model. 2007 Ieee Workshop on Automatic Speech?Recognition and Understanding. December 9-13. Kyoto,?Japan. Vols 1 and 2, 213-218.?
Wu, Y. and Wang, G. 2018. Machine learning based toxicity prediction: From chemical structural description to?transcriptome analysis. Int. J. Mol. Sci. 19, 2358.?
Wu, Z., Lei, T., Shen, C., Wang, Z., Cao, D. and Hou,?T. 2019. Admet evaluation in drug discovery. 19. Reliable?prediction of human cytochrome p450 inhibition using?artificial intelligence approaches. J. Chem. Inf. Model.?59, 4587-4601.?
Xing, G., Liang, L., Deng, C., Hua, Y., Chen, X., Yang,?Y., Liu, H., Lu, T., Chen, Y. and Zhang, Y. 2020.?Activity prediction of small molecule inhibitors for anti-rheumatoid arthritis targets based on artificial intelligence.?ACS Comb. Sci. 22, 873-886.?
Xu, D., Liu, B., Wang, J. and Zhang, Z. 2022. Bibliometric analysis of artificial intelligence for biotechnology?and applied microbiology: Exploring research hotspots?and frontiers. Front. Bioeng. Biotechnol. 10, 998298.?
Xu, R. and Wang, Q. 2014. Automatic construction of a large-scale and accurate drug-side-effect association?knowledge base from biomedical literature. J. Biomed.?Inform. 51, 191-199.?
Xu, S., Leng, Y., Feng, G., Zhang, C. and Chen, M. 2023.?A gene pathway enrichment method based on improved?tf-idf algorithm. Biochem. Biophys. Rep. 34, 101421.?
Yang, F., Darsey, J. A., Ghosh, A., Li, H. Y., Yang, M.?Q. and Wang, S. 2022. Artificial intelligence and cancer drug development. Recent Pat. Anticancer Drug Discov.?17, 2-8.?
Yang, J., Li, Z., Wu, W. K. K., Yu, S., Xu, Z., Chu,?Q. and Zhang, Q. 2022. Deep learning identifies explainable reasoning paths of mechanism of action for drug?repurposing from multilayer biological network. Brief?Bioinform. 23, bbac469.?
Yang, X. D. and Jia, B. 2010. Vector space model based on lucene index and tf-idf weighting algorithm. Proceedings of 2010 Asia-Pacific Youth Conference on Communication. August 7-8, Kunming, China. Vols 1 and 2, 20-23.?
Ye, J., Li, A., Zheng, H., Yang, B. and Lu, Y. 2023.?Machine learning advances in predicting peptide/protein-protein interactions based on sequence information?for lead peptides discovery. Adv. Biol.(Weinh) 7, e2200 232.?
Yeruva, V. K., Junaid, S. and Lee, Y. 2019. Contextual?word embeddings and topic modeling in healthy dieting?and obesity. J. Healthc. Inform. Res. 3, 159-183.?
Yuan, H., Tang, Y., Sun, W. and Liu, L. 2020. A detection method for android application security based on tf-idf and machine learning. PLoS One 15, e0238694.?
Yuba, M. and Iwasaki, K. 2023. Performance evaluation?methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the united states. PLOS Digit.?Health 2, e0000209.?
Zaizar-Fregoso, S. A., Lara-Esqueda, A., Hernandez-Suarez, C. M., Delgado-Enciso, J., Garcia-Nevares, A.,?Canseco-Avila, L. M., Guzman-Esquivel, J., Rodriguez-Sanchez, I. P., Martinez-Fierro, M. L., Ceja-Espiritu, G., Ochoa-Diaz-Lopez, H., Espinoza-Gomez, F., Sanchez-Diaz, I. and Delgado-Enciso, I. 2023. Using artificial intelligence to develop a multivariate model with a machine learning model to predict complications in mexican?diabetic patients without arterial hypertension (national?nested case-control study): Metformin and elevated normal blood pressure are risk factors, and obesity is?protective. J. Diabetes Res. 2023, 8898958.?
Zhang, C., Cheng, F., Li, W., Liu, G., Lee, P. W. and?Tang, Y. 2016. In silico prediction of drug induced liver?toxicity using substructure pattern recognition method.?Mol. Inform. 35, 136-144.?
Zhang, H., Guo, J., Li, H. and Guan, Y. 2022. Machine?learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms. iScience 25, 103910.?
Zhang, N., Zhang, H., Liu, Z., Dai, Z., Wu, W., Zhou,?R., Li, S., Wang, Z., Liang, X., Wen, J., Zhang, X.,?Zhang, B., Ouyang, S., Zhang, J., Luo, P., Li, X. and?Cheng, Q. 2023. An artificial intelligence network-guided?signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms. Cell Prolif. 56, e13409.?
Zhang, X., Shen, C., Guo, X., Wang, Z., Weng, G., Ye,?Q., Wang, G., He, Q., Yang, B., Cao, D. and Hou, T.?2021. Asfp (artificial intelligence based scoring function?platform): A web server for the development of customized scoring functions. J. Cheminform. 13, 6.?
Zulqarnain, F., Rhoads, S. F. and Syed, S. 2023. Machine?and deep learning in inflammatory bowel disease. Curr.?Opin. Gastroenterol. 39, 294-300.?
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