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 machin...

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Bibliographic Details
Main Author:	Andrés Halabi Diaz (20798460) (author)
Other Authors:	Mario Duque-Noreña (1562824) (author), Elizabeth Rincón (8776136) (author), Eduardo Chamorro (1403923) (author)
Published:	2025
Subjects:	Biochemistry Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified study also explores performed using decision novel electronic analog jchaid *, spaarc code machine learning chemical safety assessments bridges computational chemistry 80 %, κ phase electronic properties ames test results 41 – 0 21 – 0 results demonstrate phase descriptors widely used strong interpretability regulatory applications mutagenicity prediction mutagenic activity multilayer perceptrons model development mlps ). metabolic activation industrial applications feature selection based methods based algorithms accessible framework
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Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches

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