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1.
Gimadiev, T.* et al.: Bimolecular nucleophilic substitution reactions: Predictive models for rate constants and Molecular Reaction Pairs analysis. Mol. Inform. 38:1800104 (2019)
2.
Karlov, D.S.* ; Sosnin, S.* ; Tetko, I.V. & Fedorov, M.V.*: Chemical space exploration guided by deep neural networks. RSC Adv. 9, 5151-5157 (2019)
3.
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)
4.
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)
5.
Kovalishyn, V.V.* et al.: Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing. Chem. Biol. Drug Des. 92, 1272-1278 (2018)
6.
Sosnin, S.* et al.: A survey of multi-task learning methods in chemoinformatics. Mol. Inform. 37:1800108 (2018)
7.
Tetko, I.V. ; Yan, A.* & Gasteiger, J.*: Prediction of physicochemical properties of compounds. In: Applied Chemoinformatics: Achievements and Future Opportunities. 2018. accepted
8.
Kovalishyn, V.V.* et al.: Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform. Food Chem. Toxicol. 112, 507-517 (2017)
9.
Matveev, A.A.* ; Burnaev, E.V.* & Tetko, I.V.: Conformal classification in problems of predicting the physicochemical properties of molecules. Vortrag: Interdisciplinary School and Conference ITaS 2017, 14-17 September 2017, Ufa, Russia. (2017)
10.
Ratkova, E.L.* et al.: Empirical and physics-based calculations of physical–chemical properties. In: Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. 2017. 393-428
11.
Tetko, I.V. ; Maran, U.* & Tropsha, A.*: Public (Q)SAR services, integrated modeling environments, and model repositories on the web: State of the art and perspectives for future development. Mol. Inform. 36:1600082 (2017)
12.
Withnall, M.D. ; Chen, H.* & Tetko, I.V.: Matched molecular pair analysis on large melting point datasets: A big data perspective. ChemMedChem 13, 599-606 (2017)
13.
Abdelaziz, A.* ; Spahn-Langguth, H.* ; Schramm, K.-W. & Tetko, I.V.: Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge. Front. Env. Sci. 4:2 (2016)
14.
Baskin, I.I.* ; Winkler, D.* & Tetko, I.V.: A renaissance of neural networks in drug discovery. Expert Opin. Drug Discov. 11, 785-795 (2016)
15.
Brenke, J.K. et al.: Identification of small-molecule frequent hitters of glutathione S-transferase-glutathione interaction. J. Biomol. Screen. 21, 596-607 (2016)
16.
Mansouri, K.* et al.: CERAPP: Collaborative estrogen receptor activity prediction project. Environ. Health Perspect. 124, 1023-1033 (2016)
17.
Novotarskyi, S.* et al.: ToxCast EPA in vitro to in vivo challenge: Insight into the rank-I model. Chem. Res. Toxicol. 29, 768-775 (2016)
18.
Salmina, E.S.* ; Haider, N.* & Tetko, I.V.: Extended Functional Groups (EFG): An efficient set for chemical characterization and structure-activity relationship studies of chemical compounds. Molecules 21, DOI: 10.3390/molecules21010001 (2016)
19.
Tetko, I.V. et al.: Prediction of logP for Pt(II) and Pt(IV) complexes: Comparison of statistical and quantum-chemistry based approaches. J. Inorg. Biochem. 156, 1-13 (2016)
20.
Tetko, I.V. ; Lowe, D.* & Williams, A.J.*: The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS. J. Cheminformatics 8:2 (2016)