# Simulation of the Impact of Pesticides on Pollinators Under Different Conditions Using Correlation Weighting of Quasi-SMILES Components Together with the Index of Ideality of Correlation (IIC)

## TL;DR

This paper explores how to model pesticide toxicity to pollinators using a statistical method called Monte Carlo QSAR and a new correlation quality measure.

## Contribution

The study introduces the use of the Index of Ideality of Correlation (IIC) to improve QSAR models for pesticide toxicity prediction in pollinators.

## Key findings

- Monte Carlo QSAR models effectively predict pesticide toxicity to pollinators.
- The IIC improves the statistical quality of QSAR models for both acute and chronic toxicity.
- The models capture toxicity effects across different pollinator species.

## Abstract

Background: Pesticide toxicity to insects is an important adverse effect with a potentially large ecological impact when considering the effect on beneficial insects, as pollinators. The assessment of this endpoint is necessary to avoid applying ecologically dangerous pesticides. Aim of the study: Assessment of the availability of the Monte Carlo method for the development of a model for toxicity (pLD50) towards bees and other pollinators. In addition, the index of ideality of correlation is examined as a possibility to increase the statistical quality of quantitative structure–activity relationships (QSARs) for the toxicity of pesticides to pollinators. Main results and novelty: models with good performance on the toxic effect of pesticides towards different pollinators, wrapping acute and chronic effects, using the Monte Carlo method for QSAR analysis.