Cheminformatics, chemical databases,machine learning, web services

2020

  • Kochev, Nikolay, Nina Jeliazkova, Vesselina Paskaleva, Gergana Tancheva, Luchesar Iliev, Peter Ritchie, and Vedrin Jeliazkov. 2020. Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the ENanoMapper Database. Nanomaterials 10 (10): 1908. https://doi.org/10.3390/nano10101908.
  • Terziyski, Atanas, Stoyan Tenev, Vedrin Jeliazkov, Nina Jeliazkova, and Nikolay Kochev. 2020. METER.AC: Live Open Access Atmospheric Monitoring Data for Bulgaria with High Spatiotemporal Resolution. Data 5 (2): 36. https://doi.org/10.3390/data5020036.
  • Sturm, Noe, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, et al. 2020. Industry-Scale Application and Evaluation of Deep Learning for Drug Target Prediction.” Journal of Cheminformatics 12 (1): 26. https://doi.org/10.1186/s13321-020-00428-5.
  • Mansouri, Kamel, Nicole Kleinstreuer, Ahmed M. Abdelaziz, Domenico Alberga, Vinicius M. Alves, Patrik L. Andersson, Carolina H. Andrade, et al. 2020. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environmental Health Perspectives 128 (2): 027002. https://doi.org/10.1289/EHP5580.
  • Stone, Vicki, Stefania Gottardo, Eric A.J. Bleeker, Hedwig Braakhuis, Susan Dekkers, Teresa Fernandes, Andrea Haase, et al. 2020. A Framework for Grouping and Read-across of Nanomaterials- Supporting Innovation and Risk Assessment. Nano Today 35 (December): 100941. https://doi.org/10.1016/j.nantod.2020.100941.

2019

  • de Bruyn Kops C, Stork C, Sícho M, Kochev N, Svozil D, Jeliazkova N, Johannes Kirchmair: GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism. Front. Chem. 7, 2019. DOI:10.3389/fchem.2019.00402
  • Kochev N, Paskaleva V, Pukalov O, Jeliazkova N: Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling. Mol Inform 2019. DOI:10.1002/minf.201800138
  • Basei G, Hristozov D, Lamon L, Zabeo A, Jeliazkova N, Tsiliki G, Marcomini A, Torsello A: Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review. NanoImpact 2019, 13:76–99. DOI:10.1016/j.impact.2019.01.003
  • N. Jeliazkova and V. Jeliazkov, Making Big Data Available: Integrating Technologies for Toxicology Applications, in Big Data in Predictive Toxicology, D. Neagu and A. Richarz, Eds. RSC Publishing, 1 edition 2019 .
  • N. Kochev, I. Tsakovska, and N. Jeliazkova, Cheminformatics representation of chemical structures - a milestone for successful big data modelling, in Big Data in Predictive Toxicology, D. Neagu and A. Richarz, Eds. RSC Publishing, 1 edition 2019.

2018

  • Jeliazkova N. Hendren C.O., Hristozov D., Farcal L., Kochev N., Doganis P., Ritchie P., Hardy B., Claus Svendsen C., Klaessig F., Willighagen E., Cohen Y.; EU US Nanoinformatics Roadmap 2030, Chapter 5 Data collection and curation 10.5281/zenodo.1486012
  • Kochev N, Avramova S, Jeliazkova N: Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation. J Cheminform 2018, 10:42. DOI:10.1186/s13321-018-0295-6
  • Mech A, Rasmussen K, Jantunen P, Aicher L, Alessandrelli M, Bernauer U, Bleeker EAJ, Bouillard J, Di Prospero Fanghella P, Draisci R, Dusinska M, Encheva G, Flament G, Haase A, Handzhiyski Y, Herzberg F, Huwyler J, Jacobsen NR, Jeliazkov V, Jeliazkova N, Nymark P, Grafström R, Oomen AG, Polci ML, Riebeling C, Sandström J, Shivachev B, Stateva S, Tanasescu S, Tsekovska R, et al.: Insights into possibilities for grouping and read-across for nanomaterials in EU chemicals legislation. Nanotoxicology 2018:1–23.
  • Honma M, Kitazawa A, Cayley A, Williams R V, Barber C, Hanser T, Saiakhov R, Chakravarti S, Myatt GJ, Cross KP, Benfenati E, Raitano G, Mekenyan O, Petkov P, Bossa C, Benigni R, Battistelli CL, Giuliani A, Tcheremenskaia O, DeMeo C, Norinder U, Koga H, Jose C, Jeliazkova N, Kochev N, Paskaleva V, Yang C, Daga PR, Clark RD, Rathman J: Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project. Mutagenesis 2018. 10.1093/mutage/gey031

2017

2016

2015

2014

2013

2012

2011

2010

  • Jeliazkova N., Jaworska J., Worth A. Chapter 17. Open Source Tools for Read-Across and Category Formation, In M. Cronin, & Madden J. (Eds.), In Silico Toxicology : Principles and Applications (pp. 408-445). Cambridge, UK: RSC Publishing ,2010
  • B. Hardy, N. Douglas, C. Helma, M. Rautenberg, N. Jeliazkova, V. Jeliazkov, I. Nikolova, R. Benigni, O. Tcheremenskaia, S. Kramer, T. Girschick, F. Buchwald, J. Wicker, A. Karwath, M. Gütlein, A. Maunz, H. Sarimveis, G. Melagraki, A. Afantitis, P. Sopasakis, D. Gallagher, V. Poroikov, D. Filimonov, A. Zakharov, A. Lagunin, T. Gloriozova, S. Novikov, N. Skvortsova, D. Druzhilovsky , S. Chawla, I. Ghosh, S. Ray, H. Patel, S. Escher, Collaborative Development of Predictive Toxicology Applications, Journal of Cheminformatics 2010, 2:7doi:10.1186/1758-2946-2-7.

2009

2008

2007

2006

2005

2004

2003 and before

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Version: 3.14159. Last Published: 2019-03-24.