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For: Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020;25:E5277. [PMID: 33198233 DOI: 10.3390/molecules25225277] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang H, Xu P, Song Y. Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma. J Oncol 2021;2021:1629318. [PMID: 34671397 DOI: 10.1155/2021/1629318] [Reference Citation Analysis]
2 Guttman Y, Kerem Z. Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier. J Agric Food Chem 2022. [PMID: 35104412 DOI: 10.1021/acs.jafc.2c00237] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 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: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Gunasinghe J, Hwang SS, Yam WK, Rahman T, Wezen XC. In-silico discovery of inhibitors against human papillomavirus E1 protein. J Biomol Struct Dyn 2022;:1-14. [PMID: 35751129 DOI: 10.1080/07391102.2022.2091659] [Reference Citation Analysis]
5 Mamada H, Nomura Y, Uesawa Y. Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega 2022;7:17055-62. [PMID: 35647436 DOI: 10.1021/acsomega.2c00261] [Reference Citation Analysis]
6 Kumar N, Acharya V. Machine intelligence-driven framework for optimized hit selection in virtual screening. J Cheminform 2022;14. [DOI: 10.1186/s13321-022-00630-7] [Reference Citation Analysis]
7 Grimberg H, Tiwari VS, Tam B, Gur-Arie L, Gingold D, Polachek L, Akabayov B. Machine learning approaches to optimize small-molecule inhibitors for RNA targeting. J Cheminform 2022;14:4. [PMID: 35109921 DOI: 10.1186/s13321-022-00583-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Nedyalkova M, Vasighi M, Sappati S, Kumar A, Madurga S, Simeonov V. Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach. Pharmaceuticals (Basel) 2021;14:1328. [PMID: 34959727 DOI: 10.3390/ph14121328] [Reference Citation Analysis]
9 Zhou Y, Xie S, Yang Y, Jiang L, Liu S, Li W, Abagna HB, Ning L, Huang J. SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody. Front Genet 2022;13:842127. [PMID: 35368659 DOI: 10.3389/fgene.2022.842127] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 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: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Baltrukevich H, Podlewska S. From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output. Front Pharmacol 2022;13:844293. [DOI: 10.3389/fphar.2022.844293] [Reference Citation Analysis]
12 Falconi M, Olds JL, Ramanathan A. Editorial: Interaction of Biomolecules and Bioactive Compounds With the SARS-CoV-2 Proteins: Molecular Simulations for the Fight Against Covid-19. Front Mol Biosci 2022;9:950891. [DOI: 10.3389/fmolb.2022.950891] [Reference Citation Analysis]
13 Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst 2021;12:522-37. [PMID: 34139164 DOI: 10.1016/j.cels.2021.05.016] [Reference Citation Analysis]
14 Priya S, Tripathi G, Singh DB, Jain P, Kumar A. Machine learning approaches and their applications in drug discovery and design. Chem Biol Drug Des 2022;100:136-53. [PMID: 35426249 DOI: 10.1111/cbdd.14057] [Reference Citation Analysis]
15 Xia W, Zheng L, Fang J, Li F, Zhou Y, Zeng Z, Zhang B, Li Z, Li H, Zhu F. PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods. Comput Biol Med 2022;145:105465. [PMID: 35366467 DOI: 10.1016/j.compbiomed.2022.105465] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
16 Du Y, Shi H, Yang X, Wu W. Machine learning for infection risk prediction in postoperative patients with non-mechanical ventilation and intravenous neurotargeted drugs. Front Neurol 2022;13:942023. [DOI: 10.3389/fneur.2022.942023] [Reference Citation Analysis]
17 Hiremath CN. Abbreviated Profile of Drugs (APOD): modeling drug safety profiles to prioritize investigational COVID-19 treatments. Heliyon 2021;7:e07666. [PMID: 34337170 DOI: 10.1016/j.heliyon.2021.e07666] [Reference Citation Analysis]
18 Alagumalai A, Shou W, Mahian O, Aghbashlo M, Tabatabaei M, Wongwises S, Liu Y, Zhan J, Torralba A, Chen J, Wang Z, Matusik W. Self-powered sensing systems with learning capability. Joule 2022. [DOI: 10.1016/j.joule.2022.06.001] [Reference Citation Analysis]
19 Li D, Wang Y, Hu W, Chen F, Zhao J, Chen X, Han L. Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis. Front Cell Infect Microbiol 2021;11:742062. [PMID: 34722336 DOI: 10.3389/fcimb.2021.742062] [Reference Citation Analysis]
20 Moriwaki H, Saito S, Matsumoto T, Serizawa T, Kunimoto R. Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data. ACS Omega 2022;7:18374-81. [PMID: 35694454 DOI: 10.1021/acsomega.2c00664] [Reference Citation Analysis]
21 Romero-Molina S, Ruiz-Blanco YB, Mieres-Perez J, Harms M, Münch J, Ehrmann M, Sanchez-Garcia E. PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity. J Proteome Res 2022. [PMID: 35654412 DOI: 10.1021/acs.jproteome.2c00020] [Reference Citation Analysis]
22 Jha N, Prashar D, Rashid M, Shafiq M, Khan R, Pruncu CI, Tabrez Siddiqui S, Saravana Kumar M. Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19. J Healthc Eng 2021;2021:6668985. [PMID: 34326978 DOI: 10.1155/2021/6668985] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
23 Sardina DS, Valenti G, Papia F, Uasuf CG. Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria. Diagnostics (Basel) 2021;11:2150. [PMID: 34829497 DOI: 10.3390/diagnostics11112150] [Reference Citation Analysis]
24 Vaškevičius M, Kapočiūtė-Dzikienė J, Šlepikas L. Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning. Molecules 2021;26:2474. [PMID: 33922736 DOI: 10.3390/molecules26092474] [Reference Citation Analysis]
25 Yu L, Xue L, Liu F, Li Y, Jing R, Luo J. The applications of deep learning algorithms on in silico druggable proteins identification. Journal of Advanced Research 2022. [DOI: 10.1016/j.jare.2022.01.009] [Reference Citation Analysis]
26 Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19. Biomed Res Int 2021;2021:8853056. [PMID: 34258282 DOI: 10.1155/2021/8853056] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Galati S, Di Stefano M, Martinelli E, Poli G, Tuccinardi T. Recent Advances in In Silico Target Fishing. Molecules 2021;26:5124. [PMID: 34500568 DOI: 10.3390/molecules26175124] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Dibia KT, Igbokwe PK, Ezemagu GI, Asadu CO. Exploration of the quantitative Structure-Activity relationships for predicting Cyclooxygenase-2 inhibition bioactivity by Machine learning approaches. Results in Chemistry 2022;4:100272. [DOI: 10.1016/j.rechem.2021.100272] [Reference Citation Analysis]
29 Overhoff B, Falls Z, Mangione W, Samudrala R. A Deep-Learning Proteomic-Scale Approach for Drug Design. Pharmaceuticals (Basel) 2021;14:1277. [PMID: 34959678 DOI: 10.3390/ph14121277] [Reference Citation Analysis]
30 Kenny SE, Antaw F, Locke WJ, Howard CB, Korbie D, Trau M. Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis. Life 2022;12:363. [DOI: 10.3390/life12030363] [Reference Citation Analysis]
31 Huo M, Peng S, Li J, Cao Y, Chen Z, Zhang Y, Qiao Y. Comparison of the clinical effect features of Han-Ku-Gan and Wen-Xin-Gan based on the efficacy of promoting blood circulation and removing blood stasis. Journal of Traditional Chinese Medical Sciences 2022. [DOI: 10.1016/j.jtcms.2022.05.001] [Reference Citation Analysis]