QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors

Abstract

Bioconcentration factor (BCF) is an important step in the uptake of environmental pollutants in the food chain. It is expensive and time-consuming to measure, so predictive methods are of value. We have used an artificial neural network QSAR approach involving descriptors for hydrophobicity, hydrogen bonding and molecular topology, obtained from commercially available software, to predict the fish BCF values of a diverse data set of 624 chemicals. The training set statistics were: r ²= 0.765, q ²= 0.763, s = 0.610, and those of the external test set were: r ²= 0.739, s = 0.627. The model complies with the OECD Principles for the Validation of (Q)SARs.

Keywords:

• bioconcentration factor
• QSAR
• hydrophobicity
• hydrogen bonding
• topological descriptors
• artificial neural network