Next Article in Journal
Argon: Systematic Review on Neuro- and Organoprotective Properties of an “Inert” Gas
Next Article in Special Issue
Computer-Aided Targeting of the PI3K/Akt/mTOR Pathway: Toxicity Reduction and Therapeutic Opportunities
Previous Article in Journal
TPX2 Is a Prognostic Marker and Contributes to Growth and Metastasis of Human Hepatocellular Carcinoma
Previous Article in Special Issue
Combinatorial Measurement of CDKN1A/p21 and KIF20A Expression for Discrimination of DNA Damage-Induced Clastogenicity
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2014, 15(10), 18162-18174;

Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden
Department of Chemistry-BMC, Uppsala University, Box 576, Uppsala SE-751 23, Sweden
Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy
Authors to whom correspondence should be addressed.
Received: 8 July 2014 / Revised: 9 September 2014 / Accepted: 17 September 2014 / Published: 9 October 2014
Full-Text   |   PDF [870 KB, uploaded 9 October 2014]   |  


A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity. View Full-Text
Keywords: k-nearest neighbor (k-NN); Munro database; genetic algorithm (GA); acute toxicity (LD50) k-nearest neighbor (k-NN); Munro database; genetic algorithm (GA); acute toxicity (LD50)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Chavan, S.; Nicholls, I.A.; Karlsson, B.C.G.; Rosengren, A.M.; Ballabio, D.; Consonni, V.; Todeschini, R. Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study. Int. J. Mol. Sci. 2014, 15, 18162-18174.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top