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For: Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021;25:1315-60. [PMID: 33844136 DOI: 10.1007/s11030-021-10217-3] [Cited by in Crossref: 83] [Cited by in F6Publishing: 48] [Article Influence: 41.5] [Reference Citation Analysis]
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6 Luna IS, Souza TA, da Silva MS, Franca Rodrigues KAD, Scotti L, Scotti MT, Mendonça-Junior FJB. Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents. Eur J Med Chem 2023;250:115223. [PMID: 36848847 DOI: 10.1016/j.ejmech.2023.115223] [Reference Citation Analysis]
7 Mirza Z, Karim S. Structure-Based Profiling of Potential Phytomolecules with AKT1 a Key Cancer Drug Target. Molecules 2023;28:2597. [DOI: 10.3390/molecules28062597] [Reference Citation Analysis]
8 Sailer V, von Amsberg G, Duensing S, Kirfel J, Lieb V, Metzger E, Offermann A, Pantel K, Schuele R, Taubert H, Wach S, Perner S, Werner S, Aigner A. Experimental in vitro, ex vivo and in vivo models in prostate cancer research. Nat Rev Urol 2023;20:158-78. [PMID: 36451039 DOI: 10.1038/s41585-022-00677-z] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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12 Moraes J, Figueiró Longo JP. Can nanomedicine improve the effectiveness of drugs used to treat neglected tropical diseases? Nanomedicine (Lond) 2023. [PMID: 36852980 DOI: 10.2217/nnm-2023-0027] [Reference Citation Analysis]
13 Khadela A, Popat S, Ajabiya J, Valu D, Savale S, Chavda VP. AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products. Bioinformatics Tools for Pharmaceutical Drug Product Development 2023. [DOI: 10.1002/9781119865728.ch12] [Reference Citation Analysis]
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15 Siddiqui AJ, Jahan S, Patel M, Abdelgadir A, Alturaiki W, Bardakci F, Sachidanandan M, Badraoui R, Snoussi M, Adnan M. Identifying novel and potent inhibitors of EGFR protein for the drug development against the breast cancer. J Biomol Struct Dyn 2023;:1-13. [PMID: 36826428 DOI: 10.1080/07391102.2023.2181646] [Reference Citation Analysis]
16 Diniz JM, Vasconcelos H, Souza J, Rb-silva R, Ameijeiras-rodriguez C, Freitas A. Comparing Decentralized Learning Methods for Health Data Models to Non-Decentralized Alternatives: A Systematic Review Protocol (Preprint).. [DOI: 10.2196/preprints.45823] [Reference Citation Analysis]
17 Alzughaibi S, El Khediri S. A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset. Applied Sciences 2023;13:2276. [DOI: 10.3390/app13042276] [Reference Citation Analysis]
18 Choudhary R, Walhekar V, Muthal A, Kumar D, Bagul C, Kulkarni R. Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR. J Biomol Struct Dyn 2023;:1-19. [PMID: 36762704 DOI: 10.1080/07391102.2023.2175263] [Reference Citation Analysis]
19 Liu J, Lei X, Zhang Y, Pan Y. The prediction of molecular toxicity based on BiGRU and GraphSAGE. Comput Biol Med 2023;153:106524. [PMID: 36623439 DOI: 10.1016/j.compbiomed.2022.106524] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, Lall B, Anees S, Pollmann K, Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. Bioresour Technol 2023;370:128523. [PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523] [Reference Citation Analysis]
21 Budak C, Mençik V, Gider V. Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method. J Biomol Struct Dyn 2023;41:659-71. [PMID: 34877907 DOI: 10.1080/07391102.2021.2010601] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
22 Rossin G, Zorzi F, Ongaro L, Piasentin A, Vedovo F, Liguori G, Zucchi A, Simonato A, Bartoletti R, Trombetta C, Pavan N, Claps F. Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives. BioMedInformatics 2023;3:104-114. [DOI: 10.3390/biomedinformatics3010008] [Reference Citation Analysis]
23 Sixto-López Y, Ordaz-Pichardo C, Gómez-Vidal JA, Rosales-Hernández MC, Correa-Basurto J. Cytotoxic evaluation of YSL-109 in a triple negative breast cancer cell line and toxicological evaluations. Naunyn Schmiedebergs Arch Pharmacol 2023. [PMID: 36694011 DOI: 10.1007/s00210-023-02396-7] [Reference Citation Analysis]
24 Li Z, Wang S, Zhao H, Yan P, Yuan H, Zhao M, Wan R, Yu G, Wang L. Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis. Sci Rep 2023;13:1225. [PMID: 36681777 DOI: 10.1038/s41598-023-28536-w] [Reference Citation Analysis]
25 Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29(3): 508-520 [PMID: 36688019 DOI: 10.3748/wjg.v29.i3.508] [Reference Citation Analysis]
26 Sui F, Yue W, Zhang Z, Guo R, Lin L. Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning. 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) 2023. [DOI: 10.1109/mems49605.2023.10052277] [Reference Citation Analysis]
27 Kumar K, Kumar P, Deb D, Unguresan ML, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel) 2023;11. [PMID: 36673575 DOI: 10.3390/healthcare11020207] [Reference Citation Analysis]
28 Hayes AJ, Melrose J. HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease. Int J Mol Sci 2023;24. [PMID: 36674659 DOI: 10.3390/ijms24021148] [Reference Citation Analysis]
29 Rastogi R, Rastogi Y, Rathaur SK, Srivastava V. Identification of Drug Compound Bio-Activities Through Artificial Intelligence. International Journal of Health Systems and Translational Medicine 2023;3:1-34. [DOI: 10.4018/ijhstm.315800] [Reference Citation Analysis]
30 Chenguel MB. Is Artificial Intelligence the Ideal Partner for Blockchain and Crypto Currencies? From the Internet of Things to the Internet of Ideas: The Role of Artificial Intelligence 2023. [DOI: 10.1007/978-3-031-17746-0_27] [Reference Citation Analysis]
31 Varshney R, Gangal C, Sharique M, Ansari MS. Deep Learning based Wireless Channel Prediction: 5G Scenario. Procedia Computer Science 2023;218:2626-2635. [DOI: 10.1016/j.procs.2023.01.236] [Reference Citation Analysis]
32 Chen Y, Yan D, Xu J, Xiong H, Luan S, Xiao C, Huang Q. The importance of selecting crystal form for triazole fungicide tebuconazole to enhance its botryticidal activity. Science of The Total Environment 2023;854:158778. [DOI: 10.1016/j.scitotenv.2022.158778] [Reference Citation Analysis]
33 Mittal P, Goyal R, Kapoor R, Gautam RK. Artificial intelligence (AI) and machine learning in the treatment of various diseases. Computational Approaches in Drug Discovery, Development and Systems Pharmacology 2023. [DOI: 10.1016/b978-0-323-99137-7.00010-1] [Reference Citation Analysis]
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35 Tsagkaris C, Corriero AC, Rayan RA, Moysidis DV, Papazoglou AS, Alexiou A. Success stories in computer-aided drug design. Computational Approaches in Drug Discovery, Development and Systems Pharmacology 2023. [DOI: 10.1016/b978-0-323-99137-7.00001-0] [Reference Citation Analysis]
36 Sheikh K, Sayeed S, Asif A, Siddiqui MF, Rafeeq MM, Sahu A, Ahmad S. Consequential Innovations in Nature-Inspired Intelligent Computing Techniques for Biomarkers and Potential Therapeutics Identification. Nature-Inspired Intelligent Computing Techniques in Bioinformatics 2023. [DOI: 10.1007/978-981-19-6379-7_13] [Reference Citation Analysis]
37 Siddiqui MF, Alam A, Kalmatov R, Mouna A, Villela R, Mitalipova A, Mrad YN, Rahat SAA, Magarde BK, Muhammad W, Sherbaevna SR, Tashmatova N, Islamovna UG, Abuassi MA, Parween Z. Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective. Nature-Inspired Intelligent Computing Techniques in Bioinformatics 2023. [DOI: 10.1007/978-981-19-6379-7_2] [Reference Citation Analysis]
38 Qi R, Zou Q. Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level. Research (Wash D C) 2023;6:0050. [PMID: 36930772 DOI: 10.34133/research.0050] [Reference Citation Analysis]
39 Duan X, Yang H, Wang C, Liu H, Lu X, Tian Y. Microbial synthesis of cordycepin, current systems and future perspectives. Trends in Food Science & Technology 2023. [DOI: 10.1016/j.tifs.2023.01.006] [Reference Citation Analysis]
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41 Lei Y, Guo J, He S, Yan H. Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases. Cells 2022;12. [PMID: 36611897 DOI: 10.3390/cells12010103] [Reference Citation Analysis]
42 Fountzilas E, Vo HH, Mueller P, Kurzrock R, Tsimberidou A. Biomarkers and Outcomes in Diverse Cancers: Meta-Analysis of Early Phase Immuno-Oncology Trials.. [DOI: 10.21203/rs.3.rs-2386222/v1] [Reference Citation Analysis]
43 Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J. Deep Learning in Diverse Intelligent Sensor Based Systems. Sensors (Basel) 2022;23. [PMID: 36616657 DOI: 10.3390/s23010062] [Reference Citation Analysis]
44 Zhao J, Zhu X, Tan S, Chen C, Kaddoumi A, Guo XL, Lin YW, Cheung SYA. Editorial: Model-informed drug development and evidence-based translational pharmacology. Front Pharmacol 2022;13:1086551. [PMID: 36578539 DOI: 10.3389/fphar.2022.1086551] [Reference Citation Analysis]
45 Guerrisi A, Falcone I, Valenti F, Rao M, Gallo E, Ungania S, Maccallini MT, Fanciulli M, Frascione P, Morrone A, Caterino M. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications. Cells 2022;11. [PMID: 36552729 DOI: 10.3390/cells11243965] [Reference Citation Analysis]
46 Cascini F, Beccia F, Causio FA, Muscat NA, Ricciardi W. Editorial: Digitalization for precision healthcare. Front Public Health 2022;10:1078610. [PMID: 36530708 DOI: 10.3389/fpubh.2022.1078610] [Reference Citation Analysis]
47 He Y, Liu G, Li C, Yan X. Reaching the Full Potential of Machine Learning in Mitigating Environmental Impacts of Functional Materials. Reviews Env Contamination (formerly:Residue Reviews) 2022;260:21. [DOI: 10.1007/s44169-022-00024-8] [Reference Citation Analysis]
48 Bui HM, Ha MH, Pham HG, Dao TP, Nguyen TT, Nguyen ML, Vuong NT, Hoang XHT, Do LT, Dao TX, Le CQ. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Sci Rep 2022;12:20160. [PMID: 36418408 DOI: 10.1038/s41598-022-24181-x] [Reference Citation Analysis]
49 Huang Z, Chen Z, Wang R, Li Y. Identification of key genes in hepatitis B and Pan-Cancer Analysis of RHOB.. [DOI: 10.21203/rs.3.rs-2255720/v1] [Reference Citation Analysis]
50 Popa SL, Pop C, Dita MO, Brata VD, Bolchis R, Czako Z, Saadani MM, Ismaiel A, Dumitrascu DI, Grad S, David L, Cismaru G, Padureanu AM. Deep Learning and Antibiotic Resistance. Antibiotics 2022;11:1674. [DOI: 10.3390/antibiotics11111674] [Reference Citation Analysis]
51 Yang R, Zhao G, Zhang L, Xia Y, Yu H, Yan B, Cheng B. Identification of potential extracellular signal-regulated protein kinase 2 inhibitors based on multiple virtual screening strategies. Front Pharmacol 2022;13. [DOI: 10.3389/fphar.2022.1077550] [Reference Citation Analysis]
52 Burlacu CM, Gosav S, Burlacu BA, Praisler M. Artificial Neural Networks Screening for JWH Synthetic Cannabinoids: a Comparative Analysis Regarding their Specificity and Accuracy. 2022 E-Health and Bioengineering Conference (EHB) 2022. [DOI: 10.1109/ehb55594.2022.9991354] [Reference Citation Analysis]
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54 Connor S, Li T, Roberts R, Thakkar S, Liu Z, Tong W. Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury. Front Artif Intell 2022;5. [DOI: 10.3389/frai.2022.1034631] [Reference Citation Analysis]
55 Namba-nzanguim CT, Turon G, Simoben CV, Tietjen I, Montaner LJ, Efange SMN, Duran-frigola M, Ntie-kang F. Artificial intelligence for antiviral drug discovery in low resourced settings: A perspective. Front Drug Discov 2022;2. [DOI: 10.3389/fddsv.2022.1013285] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
56 Obaido G, Ogbuokiri B, Swart TG, Ayawei N, Kasongo SM, Aruleba K, Mienye ID, Aruleba I, Chukwu W, Osaye F, Egbelowo OF, Simphiwe S, Esenogho E. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences 2022;12:11127. [DOI: 10.3390/app122111127] [Reference Citation Analysis]
57 Ahmed F, Gi Ho S, Samantasinghar A, Memon FH, Rahim CSA, Soomro AM, Pratibha, Sunildutt N, Kim KH, Choi KH. Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.10.046] [Reference Citation Analysis]
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60 Liu JY, Liu LP, Li Z, Luo YW, Liang F. The role of cuproptosis-related gene in the classification and prognosis of melanoma. Front Immunol 2022;13:986214. [PMID: 36341437 DOI: 10.3389/fimmu.2022.986214] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Zameer R, Tariq S, Noreen S, Sadaqat M, Azeem F. Role of Transcriptomics and Artificial Intelligence Approaches for the Selection of Bioactive Compounds. Drug Design Using Machine Learning 2022. [DOI: 10.1002/9781394167258.ch10] [Reference Citation Analysis]
62 Balatti, Galo E, Barletta, Patricio G, Perez, Andres D, Giudicessi, Silvana L, Martínez‐ceron, María C. Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions. Drug Design Using Machine Learning 2022. [DOI: 10.1002/9781394167258.ch2] [Reference Citation Analysis]
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66 Kamaraj R, Drastik M, Maixnerova J, Pavek P. Allosteric Antagonism of the Pregnane X Receptor (PXR): Current-State-of-the-Art and Prediction of Novel Allosteric Sites. Cells 2022;11:2974. [DOI: 10.3390/cells11192974] [Reference Citation Analysis]
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68 Sharma T, Saralamma VVG, Lee DC, Imran MA, Choi J, Baig MH, Dong JJ. Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors. Int J Biol Macromol 2022;222:239-50. [PMID: 36130643 DOI: 10.1016/j.ijbiomac.2022.09.151] [Reference Citation Analysis]
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71 Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. 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 2022;933:175260. [PMID: 36116517 DOI: 10.1016/j.ejphar.2022.175260] [Reference Citation Analysis]
72 Burlacu CM, Burlacu AC, Praisler M. Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods. Inventions 2022;7:82. [DOI: 10.3390/inventions7030082] [Reference Citation Analysis]
73 Herrera-bravo J, Farías JG, Sandoval C, Herrera-belén L, Quiñones J, Díaz R, Beltrán JF. nAChR-PEP-PRED: A Robust Tool for Predicting Peptide Inhibitors of Acetylcholine Receptors Using the Random Forest Classifier. Int J Pept Res Ther 2022;28. [DOI: 10.1007/s10989-022-10460-8] [Reference Citation Analysis]
74 Wen M, Chen Q, Chen W, Yang J, Zhou X, Zhang C, Wu A, Lai J, Chen J, Mei Q, Yang S, Lan C, Wu J, Huang F, Wang L. A comprehensive review of Rubia cordifolia L.: Traditional uses, phytochemistry, pharmacological activities, and clinical applications. Front Pharmacol 2022;13:965390. [DOI: 10.3389/fphar.2022.965390] [Reference Citation Analysis]
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76 Chen S, Li Z, Zhang S, Zhou Y, Xiao X, Cui P, Xu B, Zhao Q, Kong S, Dai Y. Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses. Acta Pharmaceutica Sinica B 2022. [DOI: 10.1016/j.apsb.2022.08.025] [Reference Citation Analysis]
77 Bustamante-filho IC, Pasini M, Moura AA. Spermatozoa and seminal plasma proteomics: too many molecules, too few markers. The case of bovine and porcine semen. Animal Reproduction Science 2022. [DOI: 10.1016/j.anireprosci.2022.107075] [Reference Citation Analysis]
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79 Li Q, Li Q. A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction. Advances in Multimedia 2022;2022:1-10. [DOI: 10.1155/2022/5902199] [Reference Citation Analysis]
80 Perni S, Prokopovich P. Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-18332-3] [Reference Citation Analysis]
81 Alegría-arcos M, Barbosa T, Sepúlveda F, Combariza G, González J, Gil C, Martínez A, Ramírez D. Network pharmacology reveals multitarget mechanism of action of drugs to be repurposed for COVID-19. Front Pharmacol 2022;13:952192. [DOI: 10.3389/fphar.2022.952192] [Reference Citation Analysis]
82 Ahmed SA, Monalisa, Hussain M, Khan ZU. Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan. Front Earth Sci 2022;10:919130. [DOI: 10.3389/feart.2022.919130] [Reference Citation Analysis]
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