These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
5. Design strategies for building drug-like chemical libraries. Mitchell T; Showell GA Curr Opin Drug Discov Devel; 2001 May; 4(3):314-8. PubMed ID: 11560064 [TBL] [Abstract][Full Text] [Related]
6. Machine learning models for lipophilicity and their domain of applicability. Schroeter T; Schwaighofer A; Mika S; Laak AT; Suelzle D; Ganzer U; Heinrich N; Müller KR Mol Pharm; 2007; 4(4):524-38. PubMed ID: 17637064 [TBL] [Abstract][Full Text] [Related]
7. Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis. Luan F; Zhang R; Zhao C; Yao X; Liu M; Hu Z; Fan B Chem Res Toxicol; 2005 Feb; 18(2):198-203. PubMed ID: 15720123 [TBL] [Abstract][Full Text] [Related]
8. In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression. Chen HF Chem Biol Drug Des; 2009 Aug; 74(2):142-7. PubMed ID: 19549084 [TBL] [Abstract][Full Text] [Related]
9. A support vector machine using the lazy learning approach for multi-class classification. Comak E; Arslan A J Med Eng Technol; 2006; 30(2):73-7. PubMed ID: 16531345 [TBL] [Abstract][Full Text] [Related]
10. Active learning with support vector machines in the drug discovery process. Warmuth MK; Liao J; Rätsch G; Mathieson M; Putta S; Lemmen C J Chem Inf Comput Sci; 2003; 43(2):667-73. PubMed ID: 12653536 [TBL] [Abstract][Full Text] [Related]
11. Assessing synthetic accessibility of chemical compounds using machine learning methods. Podolyan Y; Walters MA; Karypis G J Chem Inf Model; 2010 Jun; 50(6):979-91. PubMed ID: 20536191 [TBL] [Abstract][Full Text] [Related]
12. In silico classification of adenosine receptor antagonists using Laplacian-modified naïve Bayesian, support vector machine, and recursive partitioning. Lee JH; Lee S; Choi S J Mol Graph Model; 2010 Jun; 28(8):883-90. PubMed ID: 20447849 [TBL] [Abstract][Full Text] [Related]
13. Prediction of antibacterial compounds by machine learning approaches. Yang XG; Chen D; Wang M; Xue Y; Chen YZ J Comput Chem; 2009 Jun; 30(8):1202-11. PubMed ID: 18988254 [TBL] [Abstract][Full Text] [Related]
14. Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Han LY; Zheng CJ; Xie B; Jia J; Ma XH; Zhu F; Lin HH; Chen X; Chen YZ Drug Discov Today; 2007 Apr; 12(7-8):304-13. PubMed ID: 17395090 [TBL] [Abstract][Full Text] [Related]
15. Predicting human liver microsomal stability with machine learning techniques. Sakiyama Y; Yuki H; Moriya T; Hattori K; Suzuki M; Shimada K; Honma T J Mol Graph Model; 2008 Feb; 26(6):907-15. PubMed ID: 17683964 [TBL] [Abstract][Full Text] [Related]
16. 'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries. Dobson PD; Patel Y; Kell DB Drug Discov Today; 2009 Jan; 14(1-2):31-40. PubMed ID: 19049901 [TBL] [Abstract][Full Text] [Related]
17. Importance of molecular computer modeling in anticancer drug development. Geromichalos GD J BUON; 2007 Sep; 12 Suppl 1():S101-18. PubMed ID: 17935268 [TBL] [Abstract][Full Text] [Related]
18. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. Geppert H; Humrich J; Stumpfe D; Gärtner T; Bajorath J J Chem Inf Model; 2009 Apr; 49(4):767-79. PubMed ID: 19309114 [TBL] [Abstract][Full Text] [Related]
19. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. Li H; Yap CW; Ung CY; Xue Y; Cao ZW; Chen YZ J Chem Inf Model; 2005; 45(5):1376-84. PubMed ID: 16180914 [TBL] [Abstract][Full Text] [Related]
20. Does cognitive science need kernels? Jäkel F; Schölkopf B; Wichmann FA Trends Cogn Sci; 2009 Sep; 13(9):381-8. PubMed ID: 19729333 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]