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Results: 1 to 20 of 96

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1.

An active role for machine learning in drug development.

Murphy RF.

Nat Chem Biol. 2011 Jun;7(6):327-30. doi: 10.1038/nchembio.576. No abstract available.

2.

Challenges for academic drug discovery.

Jorgensen WL.

Angew Chem Int Ed Engl. 2012 Nov 19;51(47):11680-4. doi: 10.1002/anie.201204625. Epub 2012 Oct 24. No abstract available.

PMID:
23097176
3.

Virtual screening in drug design and development.

Sousa SF, Cerqueira NM, Fernandes PA, Ramos MJ.

Comb Chem High Throughput Screen. 2010 Jun;13(5):442-53. Review.

PMID:
20236061
4.

Artificial intelligence approaches for rational drug design and discovery.

Duch W, Swaminathan K, Meller J.

Curr Pharm Des. 2007;13(14):1497-508. Review.

PMID:
17504169
5.

Fragment-Based Drug Discovery--CHI's Fourth Annual Meeting. 6-7 April 2009, San Diego, CA, USA.

Seneci P.

IDrugs. 2009 Jun;12(6):353-7. No abstract available.

PMID:
19517314
6.

Drug discovery and the medicinal chemist.

Maryanoff BE.

Future Med Chem. 2009 Apr;1(1):11-5. doi: 10.4155/fmc.09.2. No abstract available.

7.

GPU accelerated support vector machines for mining high-throughput screening data.

Liao Q, Wang J, Webster Y, Watson IA.

J Chem Inf Model. 2009 Dec;49(12):2718-25. doi: 10.1021/ci900337f.

PMID:
19961205
8.

Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

Klon AE.

Expert Opin Drug Metab Toxicol. 2010 Jul;6(7):821-33. doi: 10.1517/17425255.2010.489550. Review.

PMID:
20465523
9.

Managing missing measurements in small-molecule screens.

Browning MR, Calhoun BT, Swamidass SJ.

J Comput Aided Mol Des. 2013 May;27(5):469-78. doi: 10.1007/s10822-013-9642-x. Epub 2013 Apr 13.

PMID:
23585219
10.

Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening.

Simmons K, Kinney J, Owens A, Kleier DA, Bloch K, Argentar D, Walsh A, Vaidyanathan G.

J Chem Inf Model. 2008 Nov;48(11):2196-206. doi: 10.1021/ci800164u.

PMID:
18983143
11.

The design and synthesis of libraries for the discovery of antibacterial and antifungal substances.

Edwards PJ.

Drug Discov Today. 2011 Mar;16(5-6):278-9. doi: 10.1016/j.drudis.2011.01.010. Epub 2011 Jan 28. No abstract available.

PMID:
21277383
12.

Human stem cells and drug screening: opportunities and challenges.

Ebert AD, Svendsen CN.

Nat Rev Drug Discov. 2010 May;9(5):367-72. doi: 10.1038/nrd3000. Epub 2010 Mar 26.

PMID:
20339370
13.

High throughput technologies in drug discovery.

Kundu B.

Comb Chem High Throughput Screen. 2011 Dec;14(10):829. No abstract available.

PMID:
21843140
14.

Weka machine learning for predicting the phospholipidosis inducing potential.

Ivanciuc O.

Curr Top Med Chem. 2008;8(18):1691-709.

PMID:
19075775
15.

Computational tools for polypharmacology and repurposing.

Achenbach J, Tiikkainen P, Franke L, Proschak E.

Future Med Chem. 2011 Jun;3(8):961-8. doi: 10.4155/fmc.11.62. Review.

PMID:
21707399
16.

From purified GPCRs to drug discovery: the promise of protein-based methodologies.

Alkhalfioui F, Magnin T, Wagner R.

Curr Opin Pharmacol. 2009 Oct;9(5):629-35. doi: 10.1016/j.coph.2009.04.002. Epub 2009 May 13. Review.

PMID:
19443270
17.

Genetic interventions in mammalian cells; applications and uses in high-throughput screening and drug discovery.

Hampton SL, Kinnaird AI.

Cell Biol Toxicol. 2010 Feb;26(1):43-55. doi: 10.1007/s10565-009-9140-z. Epub 2009 Nov 11. Review.

PMID:
19904619
18.

Efficient discovery of responses of proteins to compounds using active learning.

Kangas JD, Naik AW, Murphy RF.

BMC Bioinformatics. 2014 May 16;15:143. doi: 10.1186/1471-2105-15-143.

19.

Concise review: a high-content screening approach to stem cell research and drug discovery.

Xia X, Wong ST.

Stem Cells. 2012 Sep;30(9):1800-7. doi: 10.1002/stem.1168. Review.

20.

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.

Periwal V, Kishtapuram S; Open Source Drug Discovery Consortium, Scaria V.

BMC Pharmacol. 2012 Mar 31;12:1. doi: 10.1186/1471-2210-12-1.

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