Cited In for PMID: 21547522

Year	Number of Results
2011	2
2012	2
2013	6
2014	6
2015	3
2016	4
2017	1
2018	2
2020	1
2021	3
2022	1
2023	0

1

Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets.

Ekins S, Freundlich JS. Ekins S, et al. Pharm Res. 2011 Aug;28(8):1859-69. doi: 10.1007/s11095-011-0413-x. Epub 2011 Mar 10. Pharm Res. 2011. PMID: 21547522

2

Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization.

Lane TR, Urbina F, Rank L, Gerlach J, Riabova O, Lepioshkin A, Kazakova E, Vocat A, Tkachenko V, Cole S, Makarov V, Ekins S. Lane TR, et al. Mol Pharm. 2022 Feb 7;19(2):674-689. doi: 10.1021/acs.molpharmaceut.1c00791. Epub 2021 Dec 29. Mol Pharm. 2022. PMID: 34964633

3

Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus.

Patel JS, Norambuena J, Al-Tameemi H, Ahn YM, Perryman AL, Wang X, Daher SS, Occi J, Russo R, Park S, Zimmerman M, Ho HP, Perlin DS, Dartois V, Ekins S, Kumar P, Connell N, Boyd JM, Freundlich JS. Patel JS, et al. ACS Infect Dis. 2021 Aug 13;7(8):2508-2521. doi: 10.1021/acsinfecdis.1c00265. Epub 2021 Aug 3. ACS Infect Dis. 2021. PMID: 34342426 Free PMC article.

4

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era.

Minias A, Żukowska L, Lechowicz E, Gąsior F, Knast A, Podlewska S, Zygała D, Dziadek J. Minias A, et al. Front Microbiol. 2021 Feb 2;11:618168. doi: 10.3389/fmicb.2020.618168. eCollection 2020. Front Microbiol. 2021. PMID: 33603720 Free PMC article. Review.

5

A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction.

Paulose R, Jegatheesan K, Balakrishnan GS. Paulose R, et al. Indian J Pharmacol. 2018 Jul-Aug;50(4):169-176. doi: 10.4103/ijp.IJP_304_17. Indian J Pharmacol. 2018. PMID: 30505052 Free PMC article.

6

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins S. Lane T, et al. Mol Pharm. 2018 Oct 1;15(10):4346-4360. doi: 10.1021/acs.molpharmaceut.8b00083. Epub 2018 Apr 26. Mol Pharm. 2018. PMID: 29672063 Free PMC article.

7

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).

Ekins S, Spektor AC, Clark AM, Dole K, Bunin BA. Ekins S, et al. Drug Discov Today. 2017 Mar;22(3):555-565. doi: 10.1016/j.drudis.2016.10.009. Epub 2016 Nov 22. Drug Discov Today. 2017. PMID: 27884746 Free PMC article. Review.

8

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).

Ekins S, Perryman AL, Clark AM, Reynolds RC, Freundlich JS. Ekins S, et al. J Chem Inf Model. 2016 Jul 25;56(7):1332-43. doi: 10.1021/acs.jcim.6b00004. Epub 2016 Jul 1. J Chem Inf Model. 2016. PMID: 27335215 Free PMC article.

9

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses.

Clark AM, Dole K, Ekins S. Clark AM, et al. J Chem Inf Model. 2016 Feb 22;56(2):275-85. doi: 10.1021/acs.jcim.5b00555. Epub 2016 Jan 19. J Chem Inf Model. 2016. PMID: 26750305 Free PMC article.

10

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Ekins S, Madrid PB, Sarker M, Li SG, Mittal N, Kumar P, Wang X, Stratton TP, Zimmerman M, Talcott C, Bourbon P, Travers M, Yadav M, Freundlich JS. Ekins S, et al. PLoS One. 2015 Oct 30;10(10):e0141076. doi: 10.1371/journal.pone.0141076. eCollection 2015. PLoS One. 2015. PMID: 26517557 Free PMC article.

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