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Modely kvantitativní závislosti aktivity na struktuře (Czech Wikipedia)

Analysis of information sources in references of the Wikipedia article "Modely kvantitativní závislosti aktivity na struktuře" in Czech language version.

refsWebsite
Global rank Czech rank
15doi.org
2nd place
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10worldcat.org
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5wiley.com
222nd place
83rd place
3elsevier.com
610th place
203rd place
3sciencedirect.com
149th place
80th place
2nih.gov
4th place
8th place
2chemicke-listy.cz
low place
931st place
1tu-dortmund.de
low place
low place
1researchgate.net
120th place
103rd place
1ijsr.net
low place
low place
1springer.com
274th place
174th place
1acs.org
1,248th place
807th place

acs.org

pubs.acs.org

chemicke-listy.cz

  • ŠKUTA, SVOZIL. QSAR – MODELOVÁNÍ KVANTITATIVNÍCH VZTAHŮ MEZI STRUKTUROU A AKTIVITOU CHEMICKÝCH LÁTEK. Chemické listy [online]. 2017 [cit. 2020-12-23]. Dostupné online. 
  • NOVOTNÝ, J.; SVOZIL, D. Popis a určování podobnosti molekul s pomocí molekulárních deskriptorů. Chemické listy. 2017-11-15, roč. 111, čís. 11, s. 716–723. Dostupné online [cit. 2020-12-24]. ISSN 1213-7103. 

doi.org

dx.doi.org

  • GHASEMI, Fahimeh; MEHRIDEHNAVI, Alireza; PÉREZ-GARRIDO, Alfonso. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discovery Today. 2018-10, roč. 23, čís. 10, s. 1784–1790. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.drudis.2018.06.016. (anglicky) 
  • ISARANKURA-NA-AYUDHYA, Chartchalerm; NAENNA, Thanakorn; NANTASENAMAT, Chanin. A practical overview of quantitative structure-activity relationship. EXCLI Journal ; Vol. 8. 2009-07-08, s. 2009. Dostupné online [cit. 2019-12-24]. DOI 10.17877/DE290R-690. (anglicky) 
  • NANTASENAMAT, Chanin; ISARANKURA-NA-AYUDHYA, Chartchalerm; PRACHAYASITTIKUL, Virapong. Advances in computational methods to predict the biological activity of compounds. Expert Opinion on Drug Discovery. 2010-07, roč. 5, čís. 7, s. 633–654. PMID: 22823204. Dostupné online [cit. 2019-12-24]. ISSN 1746-0441. DOI 10.1517/17460441.2010.492827. PMID 22823204. 
  • YOUSEFINEJAD, Saeed; HEMMATEENEJAD, Bahram. Chemometrics tools in QSAR/QSPR studies: A historical perspective. Chemometrics and Intelligent Laboratory Systems. 2015-12, roč. 149, s. 177–204. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.chemolab.2015.06.016. (anglicky) 
  • GHASEMI, Fahimeh; FASSIHI, Afshin; PÉREZ-SÁNCHEZ, Horacio. The role of different sampling methods in improving biological activity prediction using deep belief network. Journal of Computational Chemistry. 2017-02-05, roč. 38, čís. 4, s. 195–203. Dostupné online [cit. 2019-12-24]. DOI 10.1002/jcc.24671. (anglicky) 
  • TROPSHA, Alexander; GRAMATICA, Paola; GOMBAR, Vijay?K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR & Combinatorial Science. 2003-04, roč. 22, čís. 1, s. 69–77. Dostupné online [cit. 2019-12-24]. ISSN 1611-020X. DOI 10.1002/qsar.200390007. (anglicky) 
  • GRAMATICA, Paola. Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science. 2007-05, roč. 26, čís. 5, s. 694–701. Dostupné online [cit. 2019-12-24]. DOI 10.1002/qsar.200610151. (anglicky) 
  • CHIRICO, Nicola; GRAMATICA, Paola. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. Journal of Chemical Information and Modeling. 2012-08-27, roč. 52, čís. 8, s. 2044–2058. PMID: 22721530. Dostupné online [cit. 2019-12-24]. ISSN 1549-960X. DOI 10.1021/ci300084j. PMID 22721530. 
  • GHASEMI, Fahimeh; MEHRIDEHNAVI, Alireza; FASSIHI, Afshin. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing. 2018-01, roč. 62, s. 251–258. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.asoc.2017.09.040. (anglicky) 
  • TROPSHA, Alexander. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 2010-07-06, roč. 29, čís. 6–7, s. 476–488. Dostupné online [cit. 2019-12-24]. DOI 10.1002/minf.201000061. (anglicky) 
  • TROPSHA, Alexander. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 2010, roč. 29, čís. 6–7, s. 476–488. Dostupné online [cit. 2020-12-24]. ISSN 1868-1751. DOI 10.1002/minf.201000061. (anglicky) 
  • KOTSIANTIS, S. B. Decision trees: a recent overview. Artificial Intelligence Review. 2013-04, roč. 39, čís. 4, s. 261–283. Dostupné online [cit. 2022-02-04]. ISSN 0269-2821. DOI 10.1007/s10462-011-9272-4. (anglicky) 
  • KING, Ross D.; HIRST, Jonathan D.; STERNBERG, Michael J. E. New approaches to QSAR: Neural networks and machine learning. Perspectives in Drug Discovery and Design. 1993-12-01, roč. 1, čís. 2, s. 279–290. Dostupné online [cit. 2022-02-04]. ISSN 1573-9023. DOI 10.1007/BF02174529. (anglicky) 
  • PATANI, George A.; LAVOIE, Edmond J. Bioisosterism: A Rational Approach in Drug Design. Chemical Reviews. 1996-01, roč. 96, čís. 8, s. 3147–3176. Dostupné online [cit. 2019-12-24]. ISSN 0009-2665. DOI 10.1021/cr950066q. (anglicky) 

doi.org

  • KING, Ross D.; HIRST, Jonathan D.; STERNBERG, Michael J. E. New approaches to QSAR: Neural networks and machine learning. Perspectives in Drug Discovery and Design. 1993-12-01, roč. 1, čís. 2, s. 279–290. Dostupné online [cit. 2022-02-04]. ISSN 1573-9023. DOI 10.1007/BF02174529. (anglicky) 

elsevier.com

linkinghub.elsevier.com

  • GHASEMI, Fahimeh; MEHRIDEHNAVI, Alireza; PÉREZ-GARRIDO, Alfonso. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discovery Today. 2018-10, roč. 23, čís. 10, s. 1784–1790. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.drudis.2018.06.016. (anglicky) 
  • YOUSEFINEJAD, Saeed; HEMMATEENEJAD, Bahram. Chemometrics tools in QSAR/QSPR studies: A historical perspective. Chemometrics and Intelligent Laboratory Systems. 2015-12, roč. 149, s. 177–204. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.chemolab.2015.06.016. (anglicky) 
  • GHASEMI, Fahimeh; MEHRIDEHNAVI, Alireza; FASSIHI, Afshin. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing. 2018-01, roč. 62, s. 251–258. Dostupné online [cit. 2019-12-24]. DOI 10.1016/j.asoc.2017.09.040. (anglicky) 

ijsr.net

  • IJSR, Batta Mahesh, International Journal of Science and Research (IJSR). Abstract of Machine Learning Algorithms - A Review - Count. International Journal of Science and Research (IJSR). Dostupné online [cit. 2022-02-04]. (English) 

nih.gov

ncbi.nlm.nih.gov

  • NANTASENAMAT, Chanin; ISARANKURA-NA-AYUDHYA, Chartchalerm; PRACHAYASITTIKUL, Virapong. Advances in computational methods to predict the biological activity of compounds. Expert Opinion on Drug Discovery. 2010-07, roč. 5, čís. 7, s. 633–654. PMID: 22823204. Dostupné online [cit. 2019-12-24]. ISSN 1746-0441. DOI 10.1517/17460441.2010.492827. PMID 22823204. 
  • CHIRICO, Nicola; GRAMATICA, Paola. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. Journal of Chemical Information and Modeling. 2012-08-27, roč. 52, čís. 8, s. 2044–2058. PMID: 22721530. Dostupné online [cit. 2019-12-24]. ISSN 1549-960X. DOI 10.1021/ci300084j. PMID 22721530. 

researchgate.net

  • (PDF) A Practical Overview of Quantitative Structure-Activity Relationship. ResearchGate [online]. [cit. 2022-02-04]. Dostupné online. (anglicky) 

sciencedirect.com

  • ROY, Kunal; KAR, Supratik; DAS, Rudra Narayan. Chapter 2 - Chemical Information and Descriptors. Příprava vydání Kunal Roy, Supratik Kar, Rudra Narayan Das. Boston: Academic Press Dostupné online. ISBN 978-0-12-801505-6. S. 47–80. (anglicky) DOI: 10.1016/B978-0-12-801505-6.00002-8. 
  • GEDECK, Peter; KRAMER, Christian; ERTL, Peter. 4 - Computational Analysis of Structure–Activity Relationships. Příprava vydání G. Lawton, D. R. Witty. Svazek 49. [s.l.]: Elsevier Dostupné online. S. 113–160. (anglicky) DOI: 10.1016/S0079-6468(10)49004-9. 
  • ROY, Kunal; KAR, Supratik; DAS, Rudra Narayan. Chapter 6 - Selected Statistical Methods in QSAR. Příprava vydání Kunal Roy, Supratik Kar, Rudra Narayan Das. Boston: Academic Press Dostupné online. ISBN 978-0-12-801505-6. S. 191–229. (anglicky) DOI: 10.1016/B978-0-12-801505-6.00006-5. 

springer.com

link.springer.com

tu-dortmund.de

eldorado.tu-dortmund.de

  • ISARANKURA-NA-AYUDHYA, Chartchalerm; NAENNA, Thanakorn; NANTASENAMAT, Chanin. A practical overview of quantitative structure-activity relationship. EXCLI Journal ; Vol. 8. 2009-07-08, s. 2009. Dostupné online [cit. 2019-12-24]. DOI 10.17877/DE290R-690. (anglicky) 

wiley.com

doi.wiley.com

  • GHASEMI, Fahimeh; FASSIHI, Afshin; PÉREZ-SÁNCHEZ, Horacio. The role of different sampling methods in improving biological activity prediction using deep belief network. Journal of Computational Chemistry. 2017-02-05, roč. 38, čís. 4, s. 195–203. Dostupné online [cit. 2019-12-24]. DOI 10.1002/jcc.24671. (anglicky) 
  • TROPSHA, Alexander; GRAMATICA, Paola; GOMBAR, Vijay?K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR & Combinatorial Science. 2003-04, roč. 22, čís. 1, s. 69–77. Dostupné online [cit. 2019-12-24]. ISSN 1611-020X. DOI 10.1002/qsar.200390007. (anglicky) 
  • GRAMATICA, Paola. Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science. 2007-05, roč. 26, čís. 5, s. 694–701. Dostupné online [cit. 2019-12-24]. DOI 10.1002/qsar.200610151. (anglicky) 
  • TROPSHA, Alexander. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 2010-07-06, roč. 29, čís. 6–7, s. 476–488. Dostupné online [cit. 2019-12-24]. DOI 10.1002/minf.201000061. (anglicky) 

onlinelibrary.wiley.com

worldcat.org