Work

Predicting pesticide vapour pressures: the power of functional groups

Year: 2025

Type: article

Abstract: Explainable machine learning can aid in deriving chemical rules which in combination with inverse molecular design methods can support humans to optimise classes of molecules such as pesticides. This study demonstrates that pesticide vapour pressures can be predicted (77.1% and 83.3% within one order of magnitude) using kernel ridge regression (krr) and XGBoost on quantum chemical molecular properties but the model lacks easy interpretability. However, insights (via Shapley additive explanations (SHAP)) can be gained when a framework of functional groups is employed instead. Functional group-based models (krr: 66.7% within one order of magnitude) reveal that aromatic compounds, sulfonic acid derivatives, and carboxylic acid derivatives influence the vapour pressure the most. SHAP value trends indicate a linear relationship between reduced vapour pressure and the frequency of functional groups. A provided list of functional group contributions enables molecular modifications to optimise pesticide vapour pressures.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d5cp01965f

Authors: Mark Heezen, Manuel Alcamí, Clemens Rauer, Freija De Vleeschouwer

Institutions: Vrije Universiteit Brussel, Universidad Autónoma de Madrid

Language: English

Topic: Chemical Thermodynamics and Molecular Structure

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: green

Funders: Comunidad de Madrid, Ministerio de Ciencia, Innovación y Universidades

Awards: G2288656406, G2407235752, G3740804616, G7033951688