| 1 |
Zhang T, Mo Q, Jiang N, Wu Y, Yang X, Chen W, Li Q, Yang S, Yang J, Zeng J, Huang F, Huang Q, Luo J, Wu J, Wang L. The combination of machine learning and transcriptomics reveals a novel megakaryopoiesis inducer, MO-A, that promotes thrombopoiesis by activating FGF1/FGFR1/PI3K/Akt/NF-κB signaling. Eur J Pharmacol 2023;944:175604. [PMID: 36804544 DOI: 10.1016/j.ejphar.2023.175604] [Reference Citation Analysis]
|
| 2 |
Singla RK, Joon S, Sinha B, Kamal MA, Simal-Gandara J, Xiao J, Shen B. Current trends in natural products for the treatment and management of dementia: Computational to clinical studies. Neurosci Biobehav Rev 2023;147:105106. [PMID: 36828163 DOI: 10.1016/j.neubiorev.2023.105106] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 3 |
Singh AV, Varma M, Laux P, Choudhary S, Datusalia AK, Gupta N, Luch A, Gandhi A, Kulkarni P, Nath B. Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review. Arch Toxicol 2023;97:963-79. [PMID: 36878992 DOI: 10.1007/s00204-023-03471-x] [Reference Citation Analysis]
|
| 4 |
Wu B, Liu C, Jiang F, Li J, Yang Z. Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm. Front Environ Sci 2023;11. [DOI: 10.3389/fenvs.2023.1059217] [Reference Citation Analysis]
|
| 5 |
Belenahalli Shekarappa S, Kandagalla S, Lee J. Development of machine learning models based on molecular fingerprints for selection of small molecule inhibitors against JAK2 protein. J Comput Chem 2023. [PMID: 36929511 DOI: 10.1002/jcc.27103] [Reference Citation Analysis]
|
| 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]
|
| 9 |
Veiga N, Diesendruck Y, Peer D. Targeted nanomedicine: Lessons learned and future directions. J Control Release 2023;355:446-57. [PMID: 36773958 DOI: 10.1016/j.jconrel.2023.02.010] [Reference Citation Analysis]
|
| 10 |
De Vita S, Chini MG, Bifulco G, Lauro G. Target identification by structure-based computational approaches: Recent advances and perspectives. Bioorg Med Chem Lett 2023;83:129171. [PMID: 36739998 DOI: 10.1016/j.bmcl.2023.129171] [Reference Citation Analysis]
|
| 11 |
Miroshnichenko II, Valdman EA, Kuz'min II. Old Drugs, New Indications (Review). Razrabotka i registraciâ lekarstvennyh sredstv 2023;12:182-190. [DOI: 10.33380/2305-2066-2023-12-1-182-190] [Reference Citation Analysis]
|
| 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]
|
| 14 |
Chavda VP, Patel K, Patel S, Apostolopoulos V. Artificial Intelligence and Machine Learning in Healthcare Sector. Bioinformatics Tools for Pharmaceutical Drug Product Development 2023. [DOI: 10.1002/9781119865728.ch13] [Reference Citation Analysis]
|
| 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]
|
| 34 |
Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023;75:3-18. [PMID: 36624355 DOI: 10.1007/s43440-022-00445-1] [Reference Citation Analysis]
|
| 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]
|
| 40 |
Santa Maria JP Jr, Wang Y, Camargo LM. Perspective on the challenges and opportunities of accelerating drug discovery with artificial intelligence. Front Bioinform 2023;3:1121591. [PMID: 36909937 DOI: 10.3389/fbinf.2023.1121591] [Reference Citation Analysis]
|
| 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]
|
| 53 |
Luján MÁ, Mateo Sotos J, Torres A, Santos JL, Quevedo O, Borja AL. Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00758-9] [Reference Citation Analysis]
|
| 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]
|
| 58 |
Nascimento IJDS, de Aquino TM, da Silva-júnior EF. The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD). LDDD 2022;19:951-955. [DOI: 10.2174/1570180819666220405225817] [Reference Citation Analysis]
|
| 59 |
Latif K, Ullah A, Shkodina AD, Boiko DI, Rafique Z, Alghamdi BS, Alfaleh MA, Ashraf GM. Drug reprofiling history and potential therapies against Parkinson’s disease. Front Pharmacol 2022;13. [DOI: 10.3389/fphar.2022.1028356] [Reference Citation Analysis]
|
| 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]
|
| 63 |
Chugh H, Singh S. Machine Learning Applications in Rational Drug Discovery. Drug Design Using Machine Learning 2022. [DOI: 10.1002/9781394167258.ch3] [Reference Citation Analysis]
|
| 64 |
Deepa C, Balaji D, Bhuvaneswari V, Rajeshkumar L, Ramesh M, Priyadharshini M. Deep Learning for the Selection of Multiple Analogs. Drug Design Using Machine Learning 2022. [DOI: 10.1002/9781394167258.ch4] [Reference Citation Analysis]
|
| 65 |
Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling.. [DOI: 10.21203/rs.3.rs-2073134/v1] [Reference Citation Analysis]
|
| 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]
|
| 67 |
Li Z, Yu Q, Zhu Q, Yang X, Li Z, Fu J. Applications of machine learning in tumor-associated macrophages. Front Immunol 2022;13:985863. [DOI: 10.3389/fimmu.2022.985863] [Reference Citation Analysis]
|
| 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]
|
| 69 |
Nandakumar S, Khan SA, Ganesan P, Sweety P, Francis AP, Sekar M, Rajagopalan R, Meenakshi DU. Deep Learning and Precision Medicine. Deep Learning for Targeted Treatments 2022. [DOI: 10.1002/9781119857983.ch5] [Reference Citation Analysis]
|
| 70 |
Meenakshi DU, Nandakumar S, Francis AP, Sweety P, Fuloria S, Fuloria NK, Subramaniyan V, Khan SA. Deep Learning and Site‐Specific Drug Delivery. Deep Learning for Targeted Treatments 2022. [DOI: 10.1002/9781119857983.ch1] [Reference Citation Analysis]
|
| 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]
|
| 75 |
Chen G, Jiang X, Lv Q, Tan X, Yang Z, Chen CY. VAERHNN: Voting-averaged ensemble regression and hybrid neural network to investigate potent leads against colorectal cancer. Knowledge-Based Systems 2022. [DOI: 10.1016/j.knosys.2022.109925] [Reference Citation Analysis]
|
| 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]
|
| 78 |
Yousefi N, Yazdani-jahromi M, Tayebi A, Kolanthai E, Neal CJ, Banerjee T, Gosai A, Balasubramanian G, Seal S, Garibay OO. BindingSiteAugmentedDTA: Enabling A Next-Generation Pipeline for Interpretable Prediction Models in Drug-Repurposing.. [DOI: 10.1101/2022.08.30.505897] [Reference Citation Analysis]
|
| 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]
|
| 83 |
Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022:bbac326. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Reference Citation Analysis]
|
| 84 |
Bhardwaj A. Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare. J Healthc Leadersh 2022;14:113-8. [PMID: 35898671 DOI: 10.2147/JHL.S369498] [Reference Citation Analysis]
|
| 85 |
Hailing T, Yonghong P, Yufeng Z, Haitao T. Challenges for the application of EGFR-targeting peptide GE11 in tumor diagnosis and treatment. J Control Release 2022;349:592-605. [PMID: 35872181 DOI: 10.1016/j.jconrel.2022.07.018] [Reference Citation Analysis]
|
| 86 |
Czub N, Pacławski A, Szlęk J, Mendyk A. Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case. Pharmaceutics 2022;14:1415. [DOI: 10.3390/pharmaceutics14071415] [Reference Citation Analysis]
|
| 87 |
Sepúlveda N, Malato J, Sotzny F, Grabowska AD, Fonseca A, Cordeiro C, Graça L, Biecek P, Behrends U, Mautner J, Westermeier F, Lacerda EM, Scheibenbogen C. Revisiting IgG Antibody Reactivity to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Its Potential Application to Disease Diagnosis. Front Med 2022;9:921101. [DOI: 10.3389/fmed.2022.921101] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 88 |
Mujkic A, Baralic E, Ombasic A, Becirovic LS, Pokvic LG, Badnjevic A. Machine Intelligence in Biomedical Data Modeling, Processing, and Analysis. 2022 11th Mediterranean Conference on Embedded Computing (MECO) 2022. [DOI: 10.1109/meco55406.2022.9797164] [Reference Citation Analysis]
|
| 89 |
Lee M, Min K. MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder. J Chem Inf Model 2022. [PMID: 35666276 DOI: 10.1021/acs.jcim.2c00487] [Reference Citation Analysis]
|
| 90 |
Zhang R, Oerlemans R, Wang C, Zhang L, R. Groves M. Drug Repurposing Techniques in Viral Diseases. Drug Repurposing - Molecular Aspects and Therapeutic Applications 2022. [DOI: 10.5772/intechopen.101443] [Reference Citation Analysis]
|
| 91 |
Pohl L, Friedhoff J, Jurcic C, Teroerde M, Schindler I, Strepi K, Schneider F, Kaczorowski A, Hohenfellner M, Duensing A, Duensing S. Kidney Cancer Models for Pre-Clinical Drug Discovery: Challenges and Opportunities. Front Oncol 2022;12:889686. [PMID: 35619925 DOI: 10.3389/fonc.2022.889686] [Reference Citation Analysis]
|
| 92 |
Hsiao M, Hung M, Sun G. Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/1779131] [Reference Citation Analysis]
|
| 93 |
Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022;13:864412. [PMID: 35592425 DOI: 10.3389/fphar.2022.864412] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 94 |
López-López E, Fernández-de Gortari E, Medina-Franco JL. Yes SIR! On the structure-inactivity relationships in drug discovery. Drug Discov Today 2022:S1359-6446(22)00192-1. [PMID: 35561964 DOI: 10.1016/j.drudis.2022.05.005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 95 |
Keyvanpour MR, Haddadi F, Mehrmolaei S. DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Computational Biology and Chemistry 2022. [DOI: 10.1016/j.compbiolchem.2022.107707] [Reference Citation Analysis]
|
| 96 |
. The Role of Big Data Analytics in Drug Discovery and Vaccine Development Against COVID-19. Advances in Data Mining and Database Management 2022. [DOI: 10.4018/978-1-7998-8793-5.ch009] [Reference Citation Analysis]
|
| 97 |
Sepúlveda N, Malato J, Sotzny F, Grabowska AD, Fonseca A, Cordeiro C, Graça L, Biecek P, Behrends U, Mautner J, Westermeier F, Lacerda EM, Scheibenbogen C. Revisiting IgG antibody reactivity to Epstein-Barr virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and its potential application to disease diagnosis.. [DOI: 10.1101/2022.04.20.22273990] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 98 |
Bhatnagar R, Sardar S, Beheshti M, Podichetty JT. How can natural language processing help model informed drug development?: a review. JAMIA Open 2022;5. [DOI: 10.1093/jamiaopen/ooac043] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 99 |
Shin MK, Lee B, Kim ST, Yoo JS, Sung J. Designing a Novel Functional Peptide With Dual Antimicrobial and Anti-inflammatory Activities via in Silico Methods. Front Immunol 2022;13:821070. [DOI: 10.3389/fimmu.2022.821070] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 100 |
Shi R, Fan H, Yu X, Tang Y, Jiang J, Liang X. Advances of podophyllotoxin and its derivatives: patterns and mechanisms. Biochemical Pharmacology 2022. [DOI: 10.1016/j.bcp.2022.115039] [Reference Citation Analysis]
|
| 101 |
Singh DB, Pathak RK, Rai D. From Traditional Herbal Medicine to Rational Drug Discovery: Strategies, Challenges, and Future Perspectives. Rev Bras Farmacogn 2022;32:147-59. [DOI: 10.1007/s43450-022-00235-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 102 |
Chu H, Moon S, Park J, Bak S, Ko Y, Youn B. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022;13:826044. [DOI: 10.3389/fphar.2022.826044] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 103 |
Huang H, Lee Y, Chou C, Zheng C, Chiu H. Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.04.013] [Reference Citation Analysis]
|
| 104 |
Quazi S, Jangi R, Gavas S, Karpinski TM. Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets. Advancements in Controlled Drug Delivery Systems 2022. [DOI: 10.4018/978-1-7998-8908-3.ch002] [Reference Citation Analysis]
|
| 105 |
Khan HA, Jabeen I. Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors. Front Pharmacol 2022;13:825741. [PMID: 35300294 DOI: 10.3389/fphar.2022.825741] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 106 |
Chatanaka MK, Ulndreaj A, Sohaei D, Prassas I. Immunoinformatics: Pushing the boundaries of immunology research and medicine. ImmunoInformatics 2022;5:100007. [DOI: 10.1016/j.immuno.2021.100007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 107 |
Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022;182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
|
| 108 |
Beck H, Härter M, Haß B, Schmeck C, Baerfacker L. Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory. Drug Discov Today 2022:S1359-6446(22)00073-3. [PMID: 35202802 DOI: 10.1016/j.drudis.2022.02.015] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
|
| 109 |
Soni K, Hasija Y. Artificial Intelligence Assisted Drug Research and Development. 2022 IEEE Delhi Section Conference (DELCON) 2022. [DOI: 10.1109/delcon54057.2022.9753179] [Reference Citation Analysis]
|
| 110 |
Lee JW, Maria-solano MA, Vu TNL, Yoon S, Choi S. Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochemical Society Transactions 2022. [DOI: 10.1042/bst20211240] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 111 |
Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022;9:738838. [DOI: 10.3389/fcell.2021.738838] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 112 |
Chao CT, Tsai YT, Lee WT, Yeh HY, Chiang CK. Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation. Oxid Med Cell Longev 2022;2022:4378413. [PMID: 35035662 DOI: 10.1155/2022/4378413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 113 |
Isa A, Piyinkir Ndahi B. The Power of Computational Intelligence Methods in the Containment of COVID-19 Pandemic from Detection to Recovery. Current Perspectives on Viral Disease Outbreaks - Epidemiology, Detection and Control 2022. [DOI: 10.5772/intechopen.98931] [Reference Citation Analysis]
|
| 114 |
Kavidopoulou A, Syrigos KN, Makrogkikas S, Dlamini Z, Hull R, Marima R, Skepu A, Koumoulos EP, Bakas G, Vamvakaris I, Evangelou G, Lolas G. AI and Big Data for Drug Discovery. Trends of Artificial Intelligence and Big Data for E-Health 2022. [DOI: 10.1007/978-3-031-11199-0_7] [Reference Citation Analysis]
|
| 115 |
Adekoya OC, Yibowei ME, Adekoya GJ, Sadiku ER, Hamam Y, Ray SS. A mini-review on the application of machine learning in polymer nanogels for drug delivery. Materials Today: Proceedings 2022;62:S141-4. [DOI: 10.1016/j.matpr.2022.02.101] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 116 |
Thöming JG, Häussler S. Transcriptional Profiling of Pseudomonas aeruginosa Infections. Advances in Experimental Medicine and Biology 2022. [DOI: 10.1007/978-3-031-08491-1_11] [Reference Citation Analysis]
|
| 117 |
Akermi S, Sahoo S, Panesar R, Bernard J, Dey A, Biswas R, Johari S, Sinha S, Jana C, Puri R, Tarika M, Jayant S, Nigam A. Drug Response Prediction Using Machine Learning. Computational Intelligence in Oncology 2022. [DOI: 10.1007/978-981-16-9221-5_20] [Reference Citation Analysis]
|
| 118 |
Nagele J, Thamm A. How AI Can Help Avoid Catastrophic Overload of Healthcare System in Times of a Worldwide Pandemic. Life Science Management 2022. [DOI: 10.1007/978-3-030-98764-0_5] [Reference Citation Analysis]
|
| 119 |
Veerasamy R. QSAR—An Important In-Silico Tool in Drug Design and Discovery. Advances in Computational Modeling and Simulation 2022. [DOI: 10.1007/978-981-16-7857-8_16] [Reference Citation Analysis]
|
| 120 |
Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_340] [Reference Citation Analysis]
|
| 121 |
Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. J Optom 2022;15 Suppl 1:S50-7. [PMID: 36216736 DOI: 10.1016/j.optom.2022.08.001] [Reference Citation Analysis]
|
| 122 |
Kumar SA, Ananda Kumar TD, Beeraka NM, Pujar GV, Singh M, Narayana Akshatha HS, Bhagyalalitha M. Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 123 |
Karacosta LG. From imaging a single cell to implementing precision medicine: an exciting new era. Emerg Top Life Sci 2021;5:837-47. [PMID: 34889448 DOI: 10.1042/ETLS20210219] [Reference Citation Analysis]
|
| 124 |
Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules. Front Genet 2021;12:744170. [PMID: 34912370 DOI: 10.3389/fgene.2021.744170] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 125 |
Pikusa M, René O, Williams S, Chen Y, Martin E, Godinez WJ, Rao SPS, Guiguemde WA, Nigsch F. De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry.. [DOI: 10.1101/2021.12.10.472084] [Reference Citation Analysis]
|
| 126 |
Brooks-Warburton J, Ashton J, Dhar A, Tham T, Allen PB, Hoque S, Lovat LB, Sebastian S. Artificial intelligence and inflammatory bowel disease: practicalities and future prospects. Frontline Gastroenterol 2022;13:325-31. [PMID: 35722596 DOI: 10.1136/flgastro-2021-102003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 127 |
Zhang S, Lagutkina M, Akpinar KO, Akpinar M. Improving performance and data transmission security in VANETs. Computer Communications 2021;180:126-33. [DOI: 10.1016/j.comcom.2021.09.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 128 |
Dagenais S, Russo L, Madsen A, Webster J, Becnel L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther 2022;111:77-89. [PMID: 34839524 DOI: 10.1002/cpt.2480] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
|
| 129 |
Femi-Olabisi FJ, Ishola AA, Faokunla O, Agboola AO, Babalola BA. Evaluation of the inhibitory potentials of selected compounds from Costus spicatus (Jacq.) rhizome towards enzymes associated with insulin resistance in polycystic ovarian syndrome: an in silico study. J Genet Eng Biotechnol 2021;19:176. [PMID: 34812979 DOI: 10.1186/s43141-021-00276-2] [Reference Citation Analysis]
|
| 130 |
Brogi S, Calderone V. Artificial Intelligence in Translational Medicine. IJTM 2021;1:223-85. [DOI: 10.3390/ijtm1030016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 131 |
Joshi RP, Gebauer NWA, Bontha M, Khazaieli M, James RM, Brown JB, Kumar N. 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds. J Phys Chem B 2021;125:12166-76. [PMID: 34662142 DOI: 10.1021/acs.jpcb.1c06437] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
|
| 132 |
Singla RK, Sharma P, Dubey AK, Gundamaraju R, Kumar D, Kumar S, Madaan R, Shri R, Tsagkaris C, Parisi S, Joon S, Singla S, Kamal MA, Shen B. Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies. Front Pharmacol 2021;12:732266. [PMID: 34737700 DOI: 10.3389/fphar.2021.732266] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
|
| 133 |
Koryciński M, Ciecierski KA. Tractography Methods in Preoperative Neurosurgical Planning. JTIT 2021;3:78-85. [DOI: 10.26636/jtit.2021.154521] [Reference Citation Analysis]
|
| 134 |
Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10:1048. [PMID: 34451513 DOI: 10.3390/pathogens10081048] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
|
| 135 |
Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10:1048. [PMID: 34451513 DOI: 10.3390/pathogens10081048] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 136 |
Challa AP, Zaleski NM, Jerome RN, Lavieri RR, Shirey-Rice JK, Barnado A, Lindsell CJ, Aronoff DM, Crofford LJ, Harris RC, Alp Ikizler T, Mayer IA, Holroyd KJ, Pulley JM. Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases. Front Genet 2021;12:707836. [PMID: 34394194 DOI: 10.3389/fgene.2021.707836] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
|
| 137 |
Pinto GP, Hendrikse NM, Stourac J, Damborsky J, Bednar D. Virtual screening of potential anticancer drugs based on microbial products. Semin Cancer Biol 2021:S1044-579X(21)00207-8. [PMID: 34298109 DOI: 10.1016/j.semcancer.2021.07.012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 138 |
Gallego V, Naveiro R, Roca C, Ríos Insua D, Campillo NE. AI in drug development: a multidisciplinary perspective. Mol Divers 2021;25:1461-79. [PMID: 34251580 DOI: 10.1007/s11030-021-10266-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 139 |
Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess Control: Current Progress and Future Perspectives. Life (Basel) 2021;11:557. [PMID: 34199245 DOI: 10.3390/life11060557] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
|
| 140 |
Choi J, Yun JS, Song H, Shin YK, Kang YH, Munashingha PR, Yoon J, Kim NH, Kim HS, Yook JI, Tark D, Lim YS, Hwang SB. Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models. Molecules 2021;26:3592. [PMID: 34208385 DOI: 10.3390/molecules26123592] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 141 |
Soltani M, Moradi Kashkooli F, Souri M, Zare Harofte S, Harati T, Khadem A, Haeri Pour M, Raahemifar K. Enhancing Clinical Translation of Cancer Using Nanoinformatics. Cancers (Basel) 2021;13:2481. [PMID: 34069606 DOI: 10.3390/cancers13102481] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
|
| 142 |
Richardson P, Abdiaj I. Drug Discovery Automation and Library Synthesis in Flow. Topics in Medicinal Chemistry 2021. [DOI: 10.1007/7355_2021_135] [Reference Citation Analysis]
|
| 143 |
Lidströmer N, Davids J, Sood HS, Ashrafian H. AIM in Primary Healthcare. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_340-1] [Reference Citation Analysis]
|
