QSAR MODELS FOR ALGAL TOXICITY FOR CONGENERIC AND NON-CONGENERIC COMPOUNDS P. Gramatica1, F. Consolaro1, M. Vighi2, A. Finizio2 and M. Faust3 1QSAR Research Unit , Dept. of Structural and Functional Biology, University of Insubria, Varese, Italy. 2Dept. of Environmental Sciences, University of Milano, Milano, Italy. 3Dept. of Biology/Chemistry, University of Bremen, Bremen, Germany. Web-site: http://andromeda.varbio.unimi.it/~QSAR/ INTRODUCTION The pollution of surface waters is rarely a matter of a single toxicant but aquatic organisms are typically exposed to numerous chemicals simultaneously or in sequence. Therefore, the assessment to hazardous potentials in aquatic toxicology cannot be restricted to considerations on individual compounds, but has to account for combined effects. QSAR models were developed on algal toxicity data for two groups of congeneric photosynthesis inhibitors (phenylureas and triazines) and a third group of heterogeneous chemicals with different mode of action. METHODS Independent variables were selected among a set of 172 molecular descriptors (count, topological, 3D-WHIM descriptors and log kow), by a Genetic Algorithm approach. QSAR models were developed by Ordinary Least Square regression (OLS) method and predictive capability was validated by the leave-more-out procedure. Models were produced for the three individual classes, for the two classes of photosynthesis inhibitors together and for the whole heterogeneous set of chemicals. QSAR MODELS Good models, with satisfying predictive capability, were obtained, nevertheless relevant differences were observed in the selection of variables. The role of specific parameters, such as directional WHIMs, capable to describe particular molecular features relevant for explaining the specific mode of action, is always relevant in QSAR models for congeneric chemicals. Increasing heterogeneity increases the role of structural descriptors, accounting for general molecular features, not related to specific mode of action. CONCLUSIONS The approach seems a promising tool, not only for the development of predictive QSAR but also for the interpretation of the relationships between molecular features and toxicological mode of action.