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For: Gayvert KM, Madhukar NS, Elemento O. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chem Biol 2016;23:1294-301. [PMID: 27642066 DOI: 10.1016/j.chembiol.2016.07.023] [Cited by in Crossref: 57] [Cited by in F6Publishing: 42] [Article Influence: 9.5] [Reference Citation Analysis]
Number Citing Articles
1 Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On Modeling and Utilizing Chemical Compound Information with Deep Learning Technologies: A Task-oriented Approach. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.07.049] [Reference Citation Analysis]
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6 Chen D, Zheng J, Wei GW, Pan F. Extracting Predictive Representations from Hundreds of Millions of Molecules. J Phys Chem Lett 2021;12:10793-801. [PMID: 34723543 DOI: 10.1021/acs.jpclett.1c03058] [Reference Citation Analysis]
7 Klutzny S, Kornhuber M, Morger A, Schönfelder G, Volkamer A, Oelgeschläger M, Dunst S. Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions. Environ Int 2021;158:106947. [PMID: 34717173 DOI: 10.1016/j.envint.2021.106947] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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16 Seo S, Kim Y, Han HJ, Son WC, Hong ZY, Sohn I, Shim J, Hwang C. Predicting Successes and Failures of Clinical Trials With Outer Product-Based Convolutional Neural Network. Front Pharmacol 2021;12:670670. [PMID: 34220508 DOI: 10.3389/fphar.2021.670670] [Reference Citation Analysis]
17 Ahmad F, Mahmood A, Muhmood T. Machine learning-integrated omics for the risk and safety assessment of nanomaterials. Biomater Sci 2021;9:1598-608. [PMID: 33443512 DOI: 10.1039/d0bm01672a] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
18 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: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
19 Chakravarty K, Antontsev V, Bundey Y, Varshney J. Driving success in personalized medicine through AI-enabled computational modeling. Drug Discov Today 2021;26:1459-65. [PMID: 33609781 DOI: 10.1016/j.drudis.2021.02.007] [Reference Citation Analysis]
20 Elkin ME, Zhu X. Predictive modeling of clinical trial terminations using feature engineering and embedding learning. Sci Rep 2021;11:3446. [PMID: 33568706 DOI: 10.1038/s41598-021-82840-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2021;34:217-39. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
22 Cavasotto CN, Di Filippo JI. Artificial intelligence in the early stages of drug discovery. Arch Biochem Biophys 2021;698:108730. [PMID: 33347838 DOI: 10.1016/j.abb.2020.108730] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
23 Robledo-Cadena DX, Gallardo-Pérez JC, Dávila-Borja V, Pacheco-Velázquez SC, Belmont-Díaz JA, Ralph SJ, Blanco-Carpintero BA, Moreno-Sánchez R, Rodríguez-Enríquez S. Non-Steroidal Anti-Inflammatory Drugs Increase Cisplatin, Paclitaxel, and Doxorubicin Efficacy against Human Cervix Cancer Cells. Pharmaceuticals (Basel) 2020;13:E463. [PMID: 33333716 DOI: 10.3390/ph13120463] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
24 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]
25 Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ. A Deep Learning Approach to Antibiotic Discovery. Cell 2020;180:688-702.e13. [PMID: 32084340 DOI: 10.1016/j.cell.2020.01.021] [Cited by in Crossref: 274] [Cited by in F6Publishing: 208] [Article Influence: 137.0] [Reference Citation Analysis]
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27 Ulm JW, Nelson SF. COVID-19 drug repurposing: Summary statistics on current clinical trials and promising untested candidates. Transbound Emerg Dis 2021;68:313-7. [PMID: 32619318 DOI: 10.1111/tbed.13710] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 6.5] [Reference Citation Analysis]
28 Dinakaran S, Anitha P. RETRACTED ARTICLE: Multi feature drug compound analysis model for efficient success rate prediction using fuzzy rules. J Ambient Intell Human Comput 2021;12:6557-65. [DOI: 10.1007/s12652-020-02275-6] [Reference Citation Analysis]
29 Timpe C, Stegemann S, Barrett A, Mujumdar S. Challenges and opportunities to include patient-centric product design in industrial medicines development to improve therapeutic goals. Br J Clin Pharmacol 2020;86:2020-7. [PMID: 32441052 DOI: 10.1111/bcp.14388] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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31 Liu Y, Gao H, He YD. A compound attributes-based predictive model for drug induced liver injury in humans. PLoS One 2020;15:e0231252. [PMID: 32294131 DOI: 10.1371/journal.pone.0231252] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Dezső Z, Ceccarelli M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinformatics 2020;21:104. [PMID: 32171238 DOI: 10.1186/s12859-020-3442-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
33 Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC, Kim S, Burton J, Brown JD, Winterstein AG, Schmidt S, White JK, Conrado DJ. Machine Learning in Drug Discovery and Development Part 1: A Primer. CPT Pharmacometrics Syst Pharmacol 2020;9:129-42. [PMID: 31905263 DOI: 10.1002/psp4.12491] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
34 Zhavoronkov A, Vanhaelen Q, Oprea TI. Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin Pharmacol Ther 2020;107:780-5. [PMID: 31957003 DOI: 10.1002/cpt.1795] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 7.5] [Reference Citation Analysis]
35 Griesenauer RH, Schillebeeckx C, Kinch MS. CDEK: Clinical Drug Experience Knowledgebase. Database (Oxford) 2019;2019:baz087. [PMID: 31411687 DOI: 10.1093/database/baz087] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
36 Feijoo F, Palopoli M, Bernstein J, Siddiqui S, Albright TE. Key indicators of phase transition for clinical trials through machine learning. Drug Discovery Today 2020;25:414-21. [DOI: 10.1016/j.drudis.2019.12.014] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
37 Matsuzaka Y, Uesawa Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance. Front Bioeng Biotechnol 2019;7:485. [PMID: 32039185 DOI: 10.3389/fbioe.2019.00485] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
38 Lin X, Quan Z, Wang Z, Huang H, Zeng X. A novel molecular representation with BiGRU neural networks for learning atom. Briefings in Bioinformatics 2020;21:2099-111. [DOI: 10.1093/bib/bbz125] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 6.3] [Reference Citation Analysis]
39 Giri AK, Ianevski A, Aittokallio T. Genome-wide off-targets of drugs: risks and opportunities. Cell Biol Toxicol 2019;35:485-7. [PMID: 31432301 DOI: 10.1007/s10565-019-09491-7] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
40 Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci 2019;40:624-35. [PMID: 31383376 DOI: 10.1016/j.tips.2019.07.005] [Cited by in Crossref: 37] [Cited by in F6Publishing: 17] [Article Influence: 12.3] [Reference Citation Analysis]
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