Int. J. Mol. Sci. 2005, 6(1), 63-86; doi:10.3390/i6010063
Article

Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Division of Infectious Diseases, Faculty of Medicine, University of British Columbia, 2733, Heather street, Vancouver, British Columbia, V5Z 3J5, Canada
Received: 20 September 2004; in revised form: 14 January 2005 / Accepted: 15 January 2005 / Published: 31 January 2005
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Abstract: On the basis of the previous models of inductive and steric effects, ‘inductive’ electronegativity and molecular capacitance, a range of new ‘inductive’ QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intra- and intermolecular interactions. Using 34 ‘inductive’ QSAR descriptors alone we have been able to achieve 93% correct separation of compounds with- and without antibacterial activity (in the set of 657). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned antibacterial character to a number of trial antibiotics from the literature.
Keywords: QSAR; antibiotics; descriptors; substituent effect; electronegativity

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MDPI and ACS Style

Cherkasov, A. Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks. Int. J. Mol. Sci. 2005, 6, 63-86.

AMA Style

Cherkasov A. Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks. International Journal of Molecular Sciences. 2005; 6(1):63-86.

Chicago/Turabian Style

Cherkasov, Artem. 2005. "Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks." Int. J. Mol. Sci. 6, no. 1: 63-86.

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