BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021;38:346-61. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Cited by in Crossref: 26] [Cited by in F6Publishing: 27] [Article Influence: 13.0] [Reference Citation Analysis]
Number Citing Articles
1 Shin SH, Hur G, Kim NR, Park JHY, Lee KW, Yang H. A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor. Food Funct 2023. [PMID: 36723137 DOI: 10.1039/d2fo03466b] [Reference Citation Analysis]
2 Gonzalez-pastor R, Carrera-pacheco SE, Zúñiga-miranda J, Rodríguez-pólit C, Mayorga-ramos A, Guamán LP, Barba-ostria C. Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts. Molecules 2023;28:1068. [DOI: 10.3390/molecules28031068] [Reference Citation Analysis]
3 Biswas A, Ghosh I, Rakshit G, Murtuja S, Dagur P, Jayaprakash V. Application of artificial intelligence and machine learning in natural products-based drug discovery. Phytochemistry, Computational Tools and Databases in Drug Discovery 2023. [DOI: 10.1016/b978-0-323-90593-0.00016-2] [Reference Citation Analysis]
4 Bai M, Shi Y, Cui N, Liao Y, Zhao C, Shanshan C, Sun K, Wang J, Ye W, Ding Y. Mapping the knowledge of machine learning in pharmacy: a scientometric analysis in CiteSpace and VOSviewer. Asia-Pac J Pharmacother Toxicol 2022. [DOI: 10.32948/ajpt.2022.12.10] [Reference Citation Analysis]
5 Xie Y, Sattari K, Zhang C, Lin J. Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation. Progress in Materials Science 2022. [DOI: 10.1016/j.pmatsci.2022.101043] [Reference Citation Analysis]
6 Xia Z, Kong F, Wang K, Zhang X. Role of N6-Methyladenosine Methylation Regulators in the Drug Therapy of Digestive System Tumours. Front Pharmacol 2022;13:908079. [PMID: 35754499 DOI: 10.3389/fphar.2022.908079] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Zhang R, Ren S, Dai Q, Shen T, Li X, Li J, Xiao W. InflamNat: web-based database and predictor of anti-inflammatory natural products. J Cheminform 2022;14:30. [PMID: 35659771 DOI: 10.1186/s13321-022-00608-5] [Reference Citation Analysis]
8 Hassam M, Shamsi JA, Khan A, Al-harrasi A, Uddin R. Prediction of inhibitory activities of small molecules against Pantothenate synthetase from Mycobacterium tuberculosis using Machine Learning models. Computers in Biology and Medicine 2022;145:105453. [DOI: 10.1016/j.compbiomed.2022.105453] [Reference Citation Analysis]
9 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]
10 Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [Reference Citation Analysis]
11 Tao Xue H, Stanley-baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discovery Today 2022. [DOI: 10.1016/j.drudis.2022.05.009] [Reference Citation Analysis]
12 Xu T, Xu M, Zhu W, Chen CZ, Zhang Q, Zheng W, Huang R. Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling. J Med Chem 2022. [PMID: 35275639 DOI: 10.1021/acs.jmedchem.1c01372] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
13 Vivek-Ananth RP, Sahoo AK, Srivastava A, Samal A. Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase. RSC Adv 2022;12:6234-47. [PMID: 35424542 DOI: 10.1039/d1ra06702h] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Deng L, Deng W, Fan S, Chen M, Qi M, Lyu W, Qi Q, Tiwari AK, Chen J, Zhang D, Chen Z. m6A modification: recent advances, anticancer targeted drug discovery and beyond. Mol Cancer 2022;21. [DOI: 10.1186/s12943-022-01510-2] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 15.0] [Reference Citation Analysis]
15 Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022;13:1526-46. [PMID: 35282622 DOI: 10.1039/d1sc04471k] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 11.0] [Reference Citation Analysis]
16 Wang Z, Zhang W, Liu B. Computational Analysis of Synthetic Planning: Past and Future. Chin J Chem 2021;39:3127-3143. [DOI: 10.1002/cjoc.202100273] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Nishimura S. Marine natural products targeting the eukaryotic cell membrane. J Antibiot (Tokyo) 2021;74:769-85. [PMID: 34493848 DOI: 10.1038/s41429-021-00468-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Santana K, do Nascimento LD, Lima E Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021;9:662688. [PMID: 33996755 DOI: 10.3389/fchem.2021.662688] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [Reference Citation Analysis]
19 Daley S, Cordell GA. Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Natural Product Communications 2021;16:1934578X2110030. [DOI: 10.1177/1934578x211003029] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Huang M, Lu JJ, Ding J. Natural Products in Cancer Therapy: Past, Present and Future. Nat Prod Bioprospect 2021;11:5-13. [PMID: 33389713 DOI: 10.1007/s13659-020-00293-7] [Cited by in Crossref: 58] [Cited by in F6Publishing: 71] [Article Influence: 29.0] [Reference Citation Analysis]
21 Prihoda D, Maritz JM, Klempir O, Dzamba D, Woelk CH, Hazuda DJ, Bitton DA, Hannigan GD. The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability. Nat Prod Rep 2021;38:1100-8. [PMID: 33245088 DOI: 10.1039/d0np00055h] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
22 Maglangit F, Yu Y, Deng H. Bacterial pathogens: threat or treat (a review on bioactive natural products from bacterial pathogens). Nat Prod Rep 2021;38:782-821. [PMID: 33119013 DOI: 10.1039/d0np00061b] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 7.5] [Reference Citation Analysis]
23 Yoo S, Yang HC, Lee S, Shin J, Min S, Lee E, Song M, Lee D. A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds. Front Pharmacol 2020;11:584875. [PMID: 33519445 DOI: 10.3389/fphar.2020.584875] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
24 Medina-Franco JL, Saldívar-González FI. Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules 2020;10:E1566. [PMID: 33213003 DOI: 10.3390/biom10111566] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 6.7] [Reference Citation Analysis]
25 Periwal V, Bassler S, Andrejev S, Gabrielli N, Patil KR, Typas A, Patil KR. Bioactivity assessment of natural compounds using machine learning models based on drug target similarity.. [DOI: 10.1101/2020.11.06.371112] [Reference Citation Analysis]
26 Capecchi A, Reymond JL. Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules 2020;10:E1385. [PMID: 32998475 DOI: 10.3390/biom10101385] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]