Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches

Nitroaromatic compounds (NAs) are widely used in industrial
applications
but pose significant genotoxic risks, necessitating accurate mutagenicity
prediction for chemical safety assessments. This study integrates
conceptual density functional theory (CDFT) descriptors with explainable
no-code machine learning (ML) models to predict NA mutagenicity based
on Ames test results. Following OECD QSAR guidelines, feature selection
and model development were performed using decision-tree-based algorithms
(Random Tree, JCHAID*, SPAARC) and multilayer perceptrons (MLPs).
These models exhibited high predictive accuracy (internal: >80%,
κ
= 0.21–0.37; external: ∼90%, κ = 0.41–0.62)
with strong interpretability. The study also explores the role of
metabolic activation and aqueous-phase descriptors, evaluating a novel
electronic analog to LogP (LogQP) to assess hydrophobicity–mutagenicity
relationships. Results demonstrate that aqueous-phase electronic properties
and electrophilicity descriptors outperform vacuum-based methods in
mutagenicity prediction. The combination of CDFT descriptors with
shallow ML models proves to be a robust, interpretable, and accessible
framework for predictive toxicology. This approach enhances chemical
risk assessment and bridges computational chemistry with toxicology
for regulatory applications.