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For: Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annu Rev Pharmacol Toxicol 2020;60:573-89. [PMID: 31518513 DOI: 10.1146/annurev-pharmtox-010919-023324] [Cited by in Crossref: 55] [Cited by in F6Publishing: 27] [Article Influence: 18.3] [Reference Citation Analysis]
Number Citing Articles
1 Danishuddin, Kumar V, Faheem M, Woo Lee K. A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges. Drug Discov Today 2021:S1359-6446(21)00407-4. [PMID: 34592448 DOI: 10.1016/j.drudis.2021.09.013] [Reference Citation Analysis]
2 Akbar A, Pillalamarri N, Jonnakuti S, Ullah M. Artificial intelligence and guidance of medicine in the bubble. Cell Biosci 2021;11:108. [PMID: 34108005 DOI: 10.1186/s13578-021-00623-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Zhu K, Shen C, Tang C, Zhou Y, He C, Zuo Z. Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning. Chemosphere 2021;265:129099. [PMID: 33272675 DOI: 10.1016/j.chemosphere.2020.129099] [Reference Citation Analysis]
4 Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, Zhu H. Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches. Lab Invest 2021;101:490-502. [PMID: 32778734 DOI: 10.1038/s41374-020-00477-2] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
5 Saleem H, Ashfaq UA, Nadeem H, Zubair M, Siddique MH, Rasul I. Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia. PLoS One 2021;16:e0261111. [PMID: 34910751 DOI: 10.1371/journal.pone.0261111] [Reference Citation Analysis]
6 Recanatini M, Cabrelle C. Drug Research Meets Network Science: Where Are We? J Med Chem 2020;63:8653-66. [PMID: 32338900 DOI: 10.1021/acs.jmedchem.9b01989] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
7 Liu B, Guo S, Ding B. Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation. Int J Environ Res Public Health 2020;17:E516. [PMID: 31947558 DOI: 10.3390/ijerph17020516] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
8 Wei YP, Yao LY, Wu YY, Liu X, Peng LH, Tian YL, Ding JH, Li KH, He QG. Critical Review of Synthesis, Toxicology and Detection of Acyclovir. Molecules 2021;26:6566. [PMID: 34770975 DOI: 10.3390/molecules26216566] [Reference Citation Analysis]
9 Chen C, Yaari Z, Apfelbaum E, Grodzinski P, Shamay Y, Heller DA. Merging Data Curation and Machine Learning to Improve Nanomedicines. Advanced Drug Delivery Reviews 2022. [DOI: 10.1016/j.addr.2022.114172] [Reference Citation Analysis]
10 Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci 2021;22:1676. [PMID: 33562347 DOI: 10.3390/ijms22041676] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
11 Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2021;22:1790-818. [PMID: 32187356 DOI: 10.1093/bib/bbaa034] [Cited by in Crossref: 18] [Cited by in F6Publishing: 17] [Article Influence: 9.0] [Reference Citation Analysis]
12 Pan L, Cai C, Liu C, Liu D, Li G, Linhardt RJ, Yu G. Recent progress and advanced technology in carbohydrate-based drug development. Curr Opin Biotechnol 2021;69:191-8. [PMID: 33530023 DOI: 10.1016/j.copbio.2020.12.023] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
13 Jia X, Ciallella HL, Russo DP, Zhao L, James MH, Zhu H. Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids. ACS Sustain Chem Eng 2021;9:3909-19. [PMID: 34239782 DOI: 10.1021/acssuschemeng.0c09139] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Yuan J, Jan MA. An Economic Decision-Making Model for Drugs Using Big Data and Convolution Neural Network in Healthcare. Mobile Information Systems 2022;2022:1-7. [DOI: 10.1155/2022/2034685] [Reference Citation Analysis]
15 Qin Z, Xu Q, Hu H, Yu L, Zeng S. Extracellular Vesicles in Renal Cell Carcinoma: Multifaceted Roles and Potential Applications Identified by Experimental and Computational Methods. Front Oncol 2020;10:724. [PMID: 32457844 DOI: 10.3389/fonc.2020.00724] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
16 Lu Y, Deng G, Shuai Z. Future directions of chemical theory and computation. Pure and Applied Chemistry 2021;93:1423-33. [DOI: 10.1515/pac-2020-1006] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Wu DD, Mitchell J, Lambert JH. Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19. Risk Anal 2021;41:701-4. [PMID: 34002395 DOI: 10.1111/risa.13746] [Reference Citation Analysis]
18 Sadat Shahabi M, Shalbaf A, Maghsoudi A. Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybernetics and Biomedical Engineering 2021;41:946-59. [DOI: 10.1016/j.bbe.2021.06.006] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
19 Li C, Wu X, Shan J, Liu J, Huang X. Preparation, Characterization of Graphitic Carbon Nitride Photo-Catalytic Nanocomposites and Their Application in Wastewater Remediation: A Review. Crystals 2021;11:723. [DOI: 10.3390/cryst11070723] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
20 Sicho M, Liu X, Svozil D, van Westen GJP. GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform 2021;13:73. [PMID: 34563271 DOI: 10.1186/s13321-021-00550-y] [Reference Citation Analysis]
21 Huang JS, Liew JX, Ademiloye AS, Liew KM. Artificial Intelligence in Materials Modeling and Design. Arch Computat Methods Eng 2021;28:3399-413. [DOI: 10.1007/s11831-020-09506-1] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
22 Slavov S, Beger RD. Quantitative structure–toxicity relationships in translational toxicology. Current Opinion in Toxicology 2020;23-24:46-9. [DOI: 10.1016/j.cotox.2020.04.002] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Huang DZ, Baber JC, Bahmanyar SS. The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction. Expert Opin Drug Discov 2021;16:1045-56. [PMID: 33739897 DOI: 10.1080/17460441.2021.1901685] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Wang Y, Russo DP, Liu C, Zhou Q, Zhu H, Zhang Y. Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches. J Agric Food Chem 2020;68:12132-40. [DOI: 10.1021/acs.jafc.0c04624] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
25 Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021;26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
26 Xing G, Liang L, Deng C, Hua Y, Chen X, Yang Y, Liu H, Lu T, Chen Y, Zhang Y. Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence. ACS Comb Sci 2020;22:873-86. [PMID: 33146518 DOI: 10.1021/acscombsci.0c00169] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 Mervin LH, Johansson S, Semenova E, Giblin KA, Engkvist O. Uncertainty quantification in drug design. Drug Discov Today 2021;26:474-89. [PMID: 33253918 DOI: 10.1016/j.drudis.2020.11.027] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Czub N, Pacławski A, Szlęk J, Mendyk A. Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor. Pharmaceutics 2021;13:1711. [PMID: 34684004 DOI: 10.3390/pharmaceutics13101711] [Reference Citation Analysis]
29 Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021;26:80-93. [PMID: 33099022 DOI: 10.1016/j.drudis.2020.10.010] [Cited by in Crossref: 28] [Cited by in F6Publishing: 22] [Article Influence: 14.0] [Reference Citation Analysis]
30 Vlachakis D, Vlamos P. Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents. Math Comput Sci 2021;15:877-88. [DOI: 10.1007/s11786-021-00517-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 2020;25:1624-38. [PMID: 32663517 DOI: 10.1016/j.drudis.2020.07.005] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
32 Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021;12:642383. [PMID: 34135888 DOI: 10.3389/fimmu.2021.642383] [Reference Citation Analysis]
33 Khalid H, Khalid S, Sufyan M, Ashfaq UA. In-silico elucidation reveals potential phytochemicals against angiotensin-converting enzyme 2 (ACE-2) receptor to fight coronavirus disease 2019 (COVID-19). Z Naturforsch C J Biosci 2022. [PMID: 35470645 DOI: 10.1515/znc-2021-0325] [Reference Citation Analysis]
34 Wang S, Hou Y, Li X, Meng X, Zhang Y, Wang X. Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis. Front Pharmacol 2021;12:765435. [PMID: 35002704 DOI: 10.3389/fphar.2021.765435] [Reference Citation Analysis]
35 Mazzolari A, Scaccabarozzi A, Vistoli G, Pedretti A. MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database. Molecules 2021;26:5857. [PMID: 34641400 DOI: 10.3390/molecules26195857] [Reference Citation Analysis]
36 Khan MT, Mahmud A, Iqbal A, Hoque SF, Hasan M. Subtractive genomics approach towards the identification of novel therapeutic targets against human Bartonella bacilliformis. Informatics in Medicine Unlocked 2020;20:100385. [DOI: 10.1016/j.imu.2020.100385] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
37 Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman MH. Blockchain and artificial intelligence technology in e-Health. Environ Sci Pollut Res Int 2021;28:52810-31. [PMID: 34476701 DOI: 10.1007/s11356-021-16223-0] [Reference Citation Analysis]
38 Ma S, Zhao H, Galan EA. Integrating Engineering, Automation, and Intelligence to Catalyze the Biomedical Translation of Organoids. Adv Biol (Weinh) 2021;5:e2100535. [PMID: 33984193 DOI: 10.1002/adbi.202100535] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Mohanty S, Rashid MHA, Mohanty C, Swayamsiddha S. Modern computational intelligence based drug repurposing for diabetes epidemic. Diabetes Metab Syndr 2021;15:102180. [PMID: 34186343 DOI: 10.1016/j.dsx.2021.06.017] [Reference Citation Analysis]
40 Mak KK, Balijepalli MK, Pichika MR. Success stories of AI in drug discovery - where do things stand? Expert Opin Drug Discov 2021;:1-14. [PMID: 34553659 DOI: 10.1080/17460441.2022.1985108] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Sabaghi M, Tavasoli S, Hoseyni SZ, Mozafari M, Degraeve P, Katouzian I. A critical review on approaches to regulate the release rate of bioactive compounds from biopolymeric matrices. Food Chemistry 2022;382:132411. [DOI: 10.1016/j.foodchem.2022.132411] [Reference Citation Analysis]
42 Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, Zhu H. Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ Sci Technol 2021;55:10875-87. [PMID: 34304572 DOI: 10.1021/acs.est.1c02656] [Reference Citation Analysis]
43 Yan X, Sedykh A, Wang W, Yan B, Zhu H. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nat Commun 2020;11:2519. [PMID: 32433469 DOI: 10.1038/s41467-020-16413-3] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 8.5] [Reference Citation Analysis]
44 Baran SW, Bratcher N, Dennis J, Gaburro S, Karlsson EM, Maguire S, Makidon P, Noldus LPJJ, Potier Y, Rosati G, Ruiter M, Schaevitz L, Sweeney P, Lafollette MR. Emerging Role of Translational Digital Biomarkers Within Home Cage Monitoring Technologies in Preclinical Drug Discovery and Development. Front Behav Neurosci 2022;15:758274. [DOI: 10.3389/fnbeh.2021.758274] [Reference Citation Analysis]
45 Castelli M, Serapian SA, Marchetti F, Triveri A, Pirota V, Torielli L, Collina S, Doria F, Freccero M, Colombo G. New perspectives in cancer drug development: computational advances with an eye to design. RSC Med Chem 2021;12:1491-502. [PMID: 34671733 DOI: 10.1039/d1md00192b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Zafar R, Shahid K, Wilson LD, Fahid M, Sartaj M, Waseem W, Saeed Jan M, Zubair M, Irfan A, Ullah S, Sadiq A. Organotin (IV) complexes with sulphonyl hydrazide moiety. Design, synthesis, characterization, docking studies, cytotoxic and anti-leishmanial activity. J Biomol Struct Dyn 2021;:1-11. [PMID: 34459711 DOI: 10.1080/07391102.2021.1970625] [Reference Citation Analysis]
47 Gu Y, Zheng S, Xu Z, Yin Q, Li L, Li J. An efficient curriculum learning-based strategy for molecular graph learning. Briefings in Bioinformatics. [DOI: 10.1093/bib/bbac099] [Reference Citation Analysis]