N. Fjodorova

发表

Directions in QSAR Modeling for Regulatory Uses in OECD Member Countries, EU and in Russia

V. Poroikov, D. Filimonov, E. Benfenati, 2008, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method

Marjana Novic, Natalja Fjodorova, Agnieszka Gajewicz, 2017, Nanotoxicology.

Searching for optimal setting conditions in technological processes using parametric estimation models and neural network mapping approach: a tutorial.

M. Novič, N. Fjodorova, 2015, Analytica chimica acta.

The Nano-Sized TiO2 Dispersions for Mass Coloration of Polyimide Fibers: The Nano-Sized TiO2 for Mass Coloration

M. Novič, N. Fjodorova, T. Diankova, 2016 .

Quantum Chemical Simulation of Cytochrome P450 Catalyzed Oxidation and Carcinogenic potency of Benzene Derivatives

N. Fjodorova, M. Novich, M. Vrachko, 2008 .

Regulatory Assessment of Chemicals within OECD Member Countries, EU and in Russia

E. Benfenati, N. Fjodorova, M. Novich, 2008, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

In silico assessment of adverse effects of a large set of 6-fluoroquinolones obtained from a study of tuberculosis chemotherapy.

M. Novič, N. Minovski, N. Fjodorova, 2012, Current drug safety.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

M. Novič, A. Toropova, A. Toropov, 2022, Computational and structural biotechnology journal.

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques.

Jerzy Leszczynski, Natalja Fjodorova, Natalia Sizochenko, 2019, Ecotoxicology and environmental safety.

Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses

Ralph Kühne, Natalja Fjodorova, Gerrit Schüürmann, 2010, Molecular Diversity.

New public QSAR model for carcinogenicity

Emilio Benfenati, Alessandra Roncaglioni, Natalja Fjodorova, 2010, Chemistry Central journal.

A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives

Natalja Fjodorova, Marjana Novič, Bakhtiyor Rasulev, 2020, Nanomaterials.

Counter-Propagation Artificial Neural Network Models for Prediction of Carcinogenicity of Non-congeneric Chemicals for Regulatory Uses

Marjana Novič, N. Fjodorova, S. Zuperl, 2017 .

Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree

N Fjodorova, M Novič, M. Novič, 2014, SAR and QSAR in environmental research.

New Approach for Optimising the Impregnations of Individual Batches of Aramid Fabrics Nov pristop pri optimizaciji različnih serij aramidnih tkanin

N. Fjodorova, T. Diankova, O. Baskova, 2015 .

Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals

M Vracko, N Fjodorova, M Novic, 2010, SAR and QSAR in environmental research.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES

A. Toropova, A. Toropov, N. Fjodorova, 2023, International journal of molecular sciences.

QSPR and Nano-QSPR: Which One Is Common? The Case of Fullerenes Solubility

A. Toropova, A. Toropov, N. Fjodorova, 2023, Inorganics.

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

M. Novič, A. Toropova, A. Toropov, 2023, International journal of molecular sciences.

Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network

Emilio Benfenati, Marjana Novic, Alessandra Roncaglioni, 2011, J. Comput. Aided Mol. Des..