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Items: 1 to 50 of 243

1.

Assessment of Substrate Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates.

Sandoval PJ, Zorn KM, Clark AM, Ekins S, Wright SH.

Mol Pharmacol. 2018 Jun 8. pii: mol.117.111443. doi: 10.1124/mol.117.111443. [Epub ahead of print]

2.

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.

Mol Pharm. 2018 Apr 26. doi: 10.1021/acs.molpharmaceut.8b00083. [Epub ahead of print]

PMID:
29672063
3.

Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Ekins S, Clark AM, Dole K, Gregory K, Mcnutt AM, Spektor AC, Weatherall C, Litterman NK, Bunin BA.

Methods Mol Biol. 2018;1755:197-221. doi: 10.1007/978-1-4939-7724-6_14.

PMID:
29671272
4.

A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK.

Chiarelli LR, Mori G, Orena BS, Esposito M, Lane T, de Jesus Lopes Ribeiro AL, Degiacomi G, Zemanová J, Szádocka S, Huszár S, Palčeková Z, Manfredi M, Gosetti F, Lelièvre J, Ballell L, Kazakova E, Makarov V, Marengo E, Mikusova K, Cole ST, Riccardi G, Ekins S, Pasca MR.

Sci Rep. 2018 Feb 16;8(1):3187. doi: 10.1038/s41598-018-21614-4.

5.

A bibliometric review of drug repurposing.

Baker NC, Ekins S, Williams AJ, Tropsha A.

Drug Discov Today. 2018 Mar;23(3):661-672. doi: 10.1016/j.drudis.2018.01.018. Epub 2018 Jan 9. Review.

PMID:
29330123
6.

Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection.

Ekins S, Lingerfelt MA, Comer JE, Freiberg AN, Mirsalis JC, O'Loughlin K, Harutyunyan A, McFarlane C, Green CE, Madrid PB.

Antimicrob Agents Chemother. 2018 Jan 25;62(2). pii: e01711-17. doi: 10.1128/AAC.01711-17. Print 2018 Feb.

PMID:
29133569
7.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Korotcov A, Tkachenko V, Russo DP, Ekins S.

Mol Pharm. 2017 Dec 4;14(12):4462-4475. doi: 10.1021/acs.molpharmaceut.7b00578. Epub 2017 Nov 13.

PMID:
29096442
8.

Addressing the Metabolic Stability of Antituberculars through Machine Learning.

Stratton TP, Perryman AL, Vilchèze C, Russo R, Li SG, Patel JS, Singleton E, Ekins S, Connell N, Jacobs WR Jr, Freundlich JS.

ACS Med Chem Lett. 2017 Sep 14;8(10):1099-1104. doi: 10.1021/acsmedchemlett.7b00299. eCollection 2017 Oct 12.

PMID:
29057058
9.

The new alchemy: Online networking, data sharing and research activity distribution tools for scientists.

Williams AJ, Peck L, Ekins S.

F1000Res. 2017 Aug 3;6:1315. doi: 10.12688/f1000research.12185.1. eCollection 2017.

10.

Ahead of Our Time: Collaboration in Modeling Then and Now.

Arnold RJG, Ekins S.

Pharmacoeconomics. 2017 Sep;35(9):975-976. doi: 10.1007/s40273-017-0532-2. No abstract available.

PMID:
28660474
11.

Rosuvastatin and Atorvastatin Are Ligands of the Human Constitutive Androstane Receptor/Retinoid X Receptor α Complex.

Režen T, Hafner M, Kortagere S, Ekins S, Hodnik V, Rozman D.

Drug Metab Dispos. 2017 Aug;45(8):974-976. doi: 10.1124/dmd.117.075523. Epub 2017 May 23.

PMID:
28536098
12.

A Phenotypic Based Target Screening Approach Delivers New Antitubercular CTP Synthetase Inhibitors.

Esposito M, Szadocka S, Degiacomi G, Orena BS, Mori G, Piano V, Boldrin F, Zemanová J, Huszár S, Barros D, Ekins S, Lelièvre J, Manganelli R, Mattevi A, Pasca MR, Riccardi G, Ballell L, Mikušová K, Chiarelli LR.

ACS Infect Dis. 2017 Jun 9;3(6):428-437. doi: 10.1021/acsinfecdis.7b00006. Epub 2017 May 11.

PMID:
28475832
13.

α7-Nicotinic acetylcholine receptor inhibition by indinavir: implications for cognitive dysfunction in treated HIV disease.

Ekins S, Mathews P, Saito EK, Diaz N, Naylor D, Chung J, McMurtray AM.

AIDS. 2017 May 15;31(8):1083-1089. doi: 10.1097/QAD.0000000000001488.

PMID:
28358738
14.

Molecular dynamics simulations of Zika virus NS3 helicase: Insights into RNA binding site activity.

Mottin M, Braga RC, da Silva RA, Silva JHMD, Perryman AL, Ekins S, Andrade CH.

Biochem Biophys Res Commun. 2017 Oct 28;492(4):643-651. doi: 10.1016/j.bbrc.2017.03.070. Epub 2017 Mar 21.

PMID:
28341122
15.

A summary of some EU funded Tuberculosis drug discovery collaborations.

Ekins S.

Drug Discov Today. 2017 Mar;22(3):479-480. doi: 10.1016/j.drudis.2017.03.002. No abstract available.

PMID:
28325272
16.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

Ekins S, Godbole AA, Kéri G, Orfi L, Pato J, Bhat RS, Verma R, Bradley EK, Nagaraja V.

Tuberculosis (Edinb). 2017 Mar;103:52-60. doi: 10.1016/j.tube.2017.01.005. Epub 2017 Jan 20.

PMID:
28237034
17.

Industrializing rare disease therapy discovery and development.

Ekins S.

Nat Biotechnol. 2017 Feb 8;35(2):117-118. doi: 10.1038/nbt.3787. No abstract available.

18.

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

Ekins S, Spektor AC, Clark AM, Dole K, Bunin BA.

Drug Discov Today. 2017 Mar;22(3):555-565. doi: 10.1016/j.drudis.2016.10.009. Epub 2016 Nov 22. Review.

19.

Non-classical transpeptidases yield insight into new antibacterials.

Kumar P, Kaushik A, Lloyd EP, Li SG, Mattoo R, Ammerman NC, Bell DT, Perryman AL, Zandi TA, Ekins S, Ginell SL, Townsend CA, Freundlich JS, Lamichhane G.

Nat Chem Biol. 2017 Jan;13(1):54-61. doi: 10.1038/nchembio.2237. Epub 2016 Nov 7.

20.

Learning from the past for TB drug discovery in the future.

Mikušová K, Ekins S.

Drug Discov Today. 2017 Mar;22(3):534-545. doi: 10.1016/j.drudis.2016.09.025. Epub 2016 Oct 4. Review.

21.

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery.

Ekins S, Perryman AL, Horta Andrade C.

PLoS Negl Trop Dis. 2016 Oct 20;10(10):e0005023. doi: 10.1371/journal.pntd.0005023. eCollection 2016 Oct.

22.

Illustrating and homology modeling the proteins of the Zika virus.

Ekins S, Liebler J, Neves BJ, Lewis WG, Coffee M, Bienstock R, Southan C, Andrade CH.

Version 2. F1000Res. 2016 Mar 3 [revised 2016 Jan 1];5:275. eCollection 2016.

23.

Raising awareness of the importance of funding for tuberculosis small-molecule research.

Riccardi G, Old IG, Ekins S.

Drug Discov Today. 2017 Mar;22(3):487-491. doi: 10.1016/j.drudis.2016.09.012. Epub 2016 Sep 21.

PMID:
27664546
24.

Enabling Anyone to Translate Clinically Relevant Ideas to Therapies.

Ekins S, Diaz N, Chung J, Mathews P, McMurtray A.

Pharm Res. 2017 Jan;34(1):1-6. doi: 10.1007/s11095-016-2039-5. Epub 2016 Sep 12.

PMID:
27620174
25.

The Next Era: Deep Learning in Pharmaceutical Research.

Ekins S.

Pharm Res. 2016 Nov;33(11):2594-603. doi: 10.1007/s11095-016-2029-7. Epub 2016 Sep 6.

26.

Lack of Influence of Substrate on Ligand Interaction with the Human Multidrug and Toxin Extruder, MATE1.

Martínez-Guerrero LJ, Morales M, Ekins S, Wright SH.

Mol Pharmacol. 2016 Sep;90(3):254-64. doi: 10.1124/mol.116.105056. Epub 2016 Jul 14.

27.

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.

J Chem Inf Model. 2016 Jul 25;56(7):1332-43. doi: 10.1021/acs.jcim.6b00004. Epub 2016 Jul 1.

28.

Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis.

Djaout K, Singh V, Boum Y, Katawera V, Becker HF, Bush NG, Hearnshaw SJ, Pritchard JE, Bourbon P, Madrid PB, Maxwell A, Mizrahi V, Myllykallio H, Ekins S.

Sci Rep. 2016 Jun 10;6:27792. doi: 10.1038/srep27792.

29.

Shedding Light on Synergistic Chemical Genetic Connections with Machine Learning.

Ekins S, Siqueira-Neto JL.

Cell Syst. 2015 Dec 23;1(6):377-9. doi: 10.1016/j.cels.2015.12.005. Epub 2015 Dec 23.

30.

Open drug discovery for the Zika virus.

Ekins S, Mietchen D, Coffee M, Stratton TP, Freundlich JS, Freitas-Junior L, Muratov E, Siqueira-Neto J, Williams AJ, Andrade C.

F1000Res. 2016 Feb 9;5:150. doi: 10.12688/f1000research.8013.1. eCollection 2016.

31.

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

Clark AM, Dole K, Ekins S.

J Chem Inf Model. 2016 Feb 22;56(2):275-85. doi: 10.1021/acs.jcim.5b00555. Epub 2016 Jan 19.

32.

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Hanson SM, Ekins S, Chodera JD.

J Comput Aided Mol Des. 2015 Dec;29(12):1073-86. doi: 10.1007/s10822-015-9888-6. Epub 2015 Dec 17.

33.

Incentives for Starting Small Companies Focused on Rare and Neglected Diseases.

Ekins S, Wood J.

Pharm Res. 2016 Apr;33(4):809-15. doi: 10.1007/s11095-015-1841-9. Epub 2015 Dec 14.

34.

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.

PLoS One. 2015 Oct 30;10(10):e0141076. doi: 10.1371/journal.pone.0141076. eCollection 2015.

35.

Machine learning models identify molecules active against the Ebola virus in vitro.

Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P.

Version 3. F1000Res. 2015 Oct 20 [revised 2017 Jan 1];4:1091. doi: 10.12688/f1000research.7217.3. eCollection 2015.

36.

Kelch Domain of Gigaxonin Interacts with Intermediate Filament Proteins Affected in Giant Axonal Neuropathy.

Johnson-Kerner BL, Garcia Diaz A, Ekins S, Wichterle H.

PLoS One. 2015 Oct 13;10(10):e0140157. doi: 10.1371/journal.pone.0140157. eCollection 2015.

37.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

Perryman AL, Stratton TP, Ekins S, Freundlich JS.

Pharm Res. 2016 Feb;33(2):433-49. doi: 10.1007/s11095-015-1800-5. Epub 2015 Sep 28.

38.

Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods.

Ekins S, Litterman NK, Lipinski CA, Bunin BA.

Pharm Res. 2016 Jan;33(1):194-205. doi: 10.1007/s11095-015-1779-y. Epub 2015 Aug 27.

PMID:
26311555
39.

Evolution of a thienopyrimidine antitubercular relying on medicinal chemistry and metabolomics insights.

Li SG, Vilchèze C, Chakraborty S, Wang X, Kim H, Anisetti M, Ekins S, Rhee KY, Jacobs WR Jr, Freundlich JS.

Tetrahedron Lett. 2015 Jun 3;56(23):3246-3250.

40.

Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

Ekins S, Clark AM, Wright SH.

Drug Metab Dispos. 2015 Oct;43(10):1642-5. doi: 10.1124/dmd.115.064956. Epub 2015 Jul 21.

41.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.

Ekins S, de Siqueira-Neto JL, McCall LI, Sarker M, Yadav M, Ponder EL, Kallel EA, Kellar D, Chen S, Arkin M, Bunin BA, McKerrow JH, Talcott C.

PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878. doi: 10.1371/journal.pntd.0003878. eCollection 2015.

42.

Thiophenecarboxamide Derivatives Activated by EthA Kill Mycobacterium tuberculosis by Inhibiting the CTP Synthetase PyrG.

Mori G, Chiarelli LR, Esposito M, Makarov V, Bellinzoni M, Hartkoorn RC, Degiacomi G, Boldrin F, Ekins S, de Jesus Lopes Ribeiro AL, Marino LB, Centárová I, Svetlíková Z, Blaško J, Kazakova E, Lepioshkin A, Barilone N, Zanoni G, Porta A, Fondi M, Fani R, Baulard AR, Mikušová K, Alzari PM, Manganelli R, de Carvalho LP, Riccardi G, Cole ST, Pasca MR.

Chem Biol. 2015 Jul 23;22(7):917-27. doi: 10.1016/j.chembiol.2015.05.016. Epub 2015 Jun 18.

43.

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL.

Clark AM, Ekins S.

J Chem Inf Model. 2015 Jun 22;55(6):1246-60. doi: 10.1021/acs.jcim.5b00144. Epub 2015 Jun 3.

PMID:
25995041
44.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.

Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, Reynolds RC, Ekins S.

J Chem Inf Model. 2015 Jun 22;55(6):1231-45. doi: 10.1021/acs.jcim.5b00143. Epub 2015 Jun 3.

45.

Finding small molecules for the 'next Ebola'.

Ekins S, Southan C, Coffee M.

Version 2. F1000Res. 2015 Feb 27 [revised 2015 Jan 1];4:58. doi: 10.12688/f1000research.6181.2. eCollection 2015.

46.

A brief review of recent Charcot-Marie-Tooth research and priorities.

Ekins S, Litterman NK, Arnold RJ, Burgess RW, Freundlich JS, Gray SJ, Higgins JJ, Langley B, Willis DE, Notterpek L, Pleasure D, Sereda MW, Moore A.

F1000Res. 2015 Feb 26;4:53. doi: 10.12688/f1000research.6160.1. eCollection 2015. Review.

47.

Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data.

Clark AM, Williams AJ, Ekins S.

J Cheminform. 2015 Mar 22;7:9. doi: 10.1186/s13321-015-0057-7. eCollection 2015.

48.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond.

Ai N, Fan X, Ekins S.

Adv Drug Deliv Rev. 2015 Jun 23;86:46-60. doi: 10.1016/j.addr.2015.03.006. Epub 2015 Mar 19. Review.

PMID:
25796619
49.

FDA approved drugs as potential Ebola treatments.

Ekins S, Coffee M.

Version 2. F1000Res. 2015 Feb 19 [revised 2015 Jan 1];4:48. doi: 10.12688/f1000research.6164.2. eCollection 2015.

50.

Small molecules with antiviral activity against the Ebola virus.

Litterman N, Lipinski C, Ekins S.

F1000Res. 2015 Feb 9;4:38. doi: 10.12688/f1000research.6120.1. eCollection 2015. Review.

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