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1.
Voinarovska, V.* ; Kabeshov, M.* ; Dudenko, D.* ; Genheden, S.* & Tetko, I.V.: When yield prediction does not yield prediction: An overview of the current challenges. J. Chem. Inf. Model. 64, 42-56 (2024)
2.
Cardoso Micu Menezes, F.M. & Popowicz, G.M.: ULYSSES: An efficient and easy to use semiempirical library for C+. J. Chem. Inf. Model. 62, 3685-3694 (2022)
3.
Fino, R. et al.: Computer-aided design and synthesis of a new class of PEX14 inhibitors: substituted 2,3,4,5-tetrahydrobenzo[F][1,4]oxazepines as potential new trypanocidal agents. J. Chem. Inf. Model. 61, 5256-5268 (2021)
4.
Tetko, I.V. & Tropsha, A.*: Joint virtual special issue on computational toxicology. J. Chem. Inf. Model. 60, 1069-1071 (2020)
5.
Sosnin, S.* ; Karlov, D.* ; Tetko, I.V. & Fedorov, M.V.*: Comparative study of multitask toxicity modeling on a broad chemical space. J. Chem. Inf. Model. 59, 1062-1072 (2019)
6.
Ghosh, D. ; Koch, U.* ; Hadian, K. ; Sattler, M.* & Tetko, I.V.: Luciferase advisor: High-accuracy model to flag false positive hits in luciferase HTS assays. J. Chem. Inf. Model. 58, 933-942 (2018)
7.
Zarzycka, B.* et al.: Discovery of small molecule CD40-TRAF6 inhibitors. J. Chem. Inf. Model. 55, 294-307 (2015)
8.
Arciniega, M.* & Lange, O.F.: Improvement of virtual screening results by docking data feature analysis. J. Chem. Inf. Model. 54, 1401-1411 (2014)
9.
Tetko, I.V. et al.: How accurately can we predict the melting points of drug-like compounds? J. Chem. Inf. Model. 54, 3320-3329 (2014)
10.
Tetko, I.V. et al.: Development of dimethyl sulfoxide solubility models using 163 000 molecules: Using a domain applicability metric to select more reliable predictions. J. Chem. Inf. Model. 53, 1990-2000 (2013)
11.
Brandmaier, S. ; Sahlin, U.* ; Tetko, I.V. & Öberg, T.*: PLS-optimal: A stepwise D-optimal design based on latent variables. J. Chem. Inf. Model. 52, 975-983 (2012)
12.
Sushko, I.* ; Salmina, E.* ; Potemkin, V.A.* ; Poda, G.* & Tetko, I.V.: ToxAlerts: A web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. J. Chem. Inf. Model. 52, 2310-2316 (2012)
13.
Novotarskyi, S. ; Sushko, I. ; Körner, R. ; Pandey, A.K. & Tetko, I.V.: A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J. Chem. Inf. Model. 51, 1271-1280 (2011)
14.
Sushko, I. et al.: Applicability domains for classification problems: Benchmarking of distance to models for ames mutagenicity set. J. Chem. Inf. Model. 50, 2094-2111 (2010)
15.
Varnek, A.* et al.: Inductive transfer of knowledge: Application of multi-task learning and feature net approaches to model tissue-air partition coefficients. J. Chem. Inf. Model. 49, 133-144 (2009)
16.
Karthikeyan, M.* ; Krishnan, S.* ; Pandey, A.K. ; Bender, A.* & Tropsha, A.*: Distributed chemical computing using ChemStar: An open source java remote method invocation architecture applied to large scale molecular data from PubChem. J. Chem. Inf. Model. 48, 691-703 (2008)
17.
Tetko, I.V. et al.: Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model. 48, 1733-1746 (2008)
18.
Zhu, H.* et al.: Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J. Chem. Inf. Model. 48, 766-784 (2008)
19.
Varnek, A.* ; Kireeva, N.* ; Tetko, I.V. ; Baskin, I.I.* & Solov'ev, V.P.*: Exhaustive QSPR studies of a large diverse set of ionic liquids: How accurately can we predict melting points? J. Chem. Inf. Model. 47, 1111-1122 (2007)
20.
Brüggemann, R.* ; Restrepo, G.* & Voigt, K.: Structure-fate relationships of organic chemicals derived from the software packages E4CHEM and WHASSE. J. Chem. Inf. Model. 46, 894-902 (2006)
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