A dual-objective QSAR framework integrating quantum reactivity and structural descriptors for predicting antifungal activity and honey bee toxicity of pesticide mixtures

This study presents a unified descriptor-based QSAR framework for analyzing pesticide mixtures from the dual perspective of antifungal efficacy and honey bee toxicity. Rather than developing independent predictive models optimized for individual endpoints, the framework is designed to enable cross-endpoint comparison within a consistent chemical representation space, thereby supporting selectivity-oriented interpretation. Two conceptually linked but independently developed QSAR models were constructed to describe (i) antifungal activity against Macrophomina phaseolina and (ii) acute contact toxicity toward Apis mellifera . Molecular representation was achieved by integrating quantum reactivity descriptors (QRDs), derived from density functional theory (DFT), with a curated subset of structural descriptors (CaPS) calculated using the PaDEL platform. Mixture properties were encoded using a composition-weighted descriptor scheme, and both endpoints were modeled using an identical genetic algorithm–multiple linear regression (GA–MLR) workflow to ensure methodological consistency at the descriptor level. The results reveal clear divergence in descriptor contributions across endpoints. Antifungal efficacy is primarily associated with structural features such as molecular size, polarity, and topological organization, whereas honey bee toxicity is more strongly governed by electronic reactivity, charge-transfer behavior, and polarity-dependent transport properties. This distinction indicates that efficacy and non-target toxicity are not intrinsically coupled, and that a descriptor-level selectivity window may be identified.