Heinrich N - Search Results

1

Efficiency of different measures for defining the applicability domain of classification models.

Klingspohn W, Mathea M, Ter Laak A, Heinrich N, Baumann K. Klingspohn W, et al. Among authors: heinrich n. J Cheminform. 2017 Aug 3;9(1):44. doi: 10.1186/s13321-017-0230-2. J Cheminform. 2017. PMID: 29086213 Free PMC article.

2

Benchmark data set for in silico prediction of Ames mutagenicity.

Hansen K, Mika S, Schroeter T, Sutter A, ter Laak A, Steger-Hartmann T, Heinrich N, Müller KR. Hansen K, et al. Among authors: heinrich n. J Chem Inf Model. 2009 Sep;49(9):2077-81. doi: 10.1021/ci900161g. J Chem Inf Model. 2009. PMID: 19702240

3

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

Schroeter TS, Schwaighofer A, Mika S, Ter Laak A, Suelzle D, Ganzer U, Heinrich N, Müller KR. Schroeter TS, et al. Among authors: heinrich n. J Comput Aided Mol Des. 2007 Dec;21(12):651-64. doi: 10.1007/s10822-007-9160-9. Epub 2007 Dec 1. J Comput Aided Mol Des. 2007. PMID: 18060505

4

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

Schroeter TS, Schwaighofer A, Mika S, Ter Laak A, Suelzle D, Ganzer U, Heinrich N, Müller KR. Schroeter TS, et al. Among authors: heinrich n. J Comput Aided Mol Des. 2007 Sep;21(9):485-98. doi: 10.1007/s10822-007-9125-z. Epub 2007 Jul 14. J Comput Aided Mol Des. 2007. PMID: 17632688

5

A probabilistic approach to classifying metabolic stability.

Schwaighofer A, Schroeter T, Mika S, Hansen K, Ter Laak A, Lienau P, Reichel A, Heinrich N, Müller KR. Schwaighofer A, et al. Among authors: heinrich n. J Chem Inf Model. 2008 Apr;48(4):785-96. doi: 10.1021/ci700142c. Epub 2008 Mar 8. J Chem Inf Model. 2008. PMID: 18327900

6

Accurate solubility prediction with error bars for electrolytes: a machine learning approach.

Schwaighofer A, Schroeter T, Mika S, Laub J, ter Laak A, Sülzle D, Ganzer U, Heinrich N, Müller KR. Schwaighofer A, et al. Among authors: heinrich n. J Chem Inf Model. 2007 Mar-Apr;47(2):407-24. doi: 10.1021/ci600205g. Epub 2007 Jan 23. J Chem Inf Model. 2007. PMID: 17243756

7

Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signalling.

Wichard JD, Ter Laak A, Krause G, Heinrich N, Kühne R, Kleinau G. Wichard JD, et al. Among authors: heinrich n. PLoS One. 2011 Feb 4;6(2):e16811. doi: 10.1371/journal.pone.0016811. PLoS One. 2011. PMID: 21326864 Free PMC article.

8

Predicting lipophilicity of drug-discovery molecules using Gaussian process models.

Schroeter TS, Schwaighofer A, Mika S, Ter Laak A, Suelzle D, Ganzer U, Heinrich N, Müller KR. Schroeter TS, et al. Among authors: heinrich n. ChemMedChem. 2007 Sep;2(9):1265-7. doi: 10.1002/cmdc.200700041. ChemMedChem. 2007. PMID: 17576646 No abstract available.

9

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. Schroeter T, et al. Among authors: heinrich n. Mol Pharm. 2007 Jul-Aug;4(4):524-38. doi: 10.1021/mp0700413. Epub 2007 Jul 19. Mol Pharm. 2007. PMID: 17637064

10

A maximum common subgraph kernel method for predicting the chromosome aberration test.

Mohr J, Jain B, Sutter A, Laak AT, Steger-Hartmann T, Heinrich N, Obermayer K. Mohr J, et al. Among authors: heinrich n. J Chem Inf Model. 2010 Oct 25;50(10):1821-38. doi: 10.1021/ci900367j. J Chem Inf Model. 2010. PMID: 20883013

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