Rational Design for Antioxidant Diphenylamine Derivatives Using Quantitative Structure–Activity Relationships and Quantum Mechanics Calculations

Diphenylamine (DPA) derivatives, used as antioxidants in rubber-based products, inhibit autoxidation by donating hydrogen atoms to peroxyl radicals. Octanol-water partition coefficient (LogK<sub>ow</sub>), an antioxidant index, helps predict their distribution in hydrophobic polymer matrices. Therefore, this study aimed to investigate the relationship between the structure of DPA derivatives and their antioxidant activities, using machine learning with quantitative structure-activity relationships (QSAR) and quantum mechanics (QM). The structure of DPA derivatives was optimized using Density Functional Theory and analyzed for molecular properties. The QSAR models were trained using important descriptors identified through permutation importance. Among the models developed, the Gradient Boosting Regressor (GBR) showed the best performance, with R<sup>2</sup> of 0.983 and root mean square error (RMSE) of 0.642 for the test set. SHAP analysis revealed that molecular weight and electronic properties significantly influenced LogK<sub>ow</sub> predictions. For instance, a higher molecular weight was associated with increased LogK<sub>ow</sub>, and a higher positive charge of C2 correlated with higher LogK<sub>ow</sub> predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogK<sub>ow</sub> values of 9.789 and 7.102 for D1 and D2, respectively, which are higher than the training compounds in the model. To gain molecular insight, the quantum chemical calculations with M062X/6-311++G(d,p)//M062X/6-31G(d,p) were performed to investigate the bond dissociation enthalpy (BDE). The results showed that D1 (79.50 kcal/mol) and D2 (72.43 kcal/mol) exhibited lower BDEs than the reference compounds, suggesting that the designed compounds have the potential for enhanced antioxidant activity. In addition, the antioxidant reaction mechanism was studied by using M062X/6-311++G(d,p)//M062X/6-31G(d,p) which found that the hydrogen atom transfer is the key step, where D1 and D2 showed activation energy barriers of 10.38 and 6.29 kcal/mol, respectively, compared to reference compounds of R3 (10.39 kcal/mol), R1 (10.40 kcal/mol), and R2 (18.26 kcal/mol). Therefore, our findings demonstrate that integrating QSAR with quantum chemical calculations can effectively guide the design of DPA derivatives with improved antioxidant properties.

Authors

Institutions

Publication Details

Journal
Journal of Computational Chemistry
Published
2025-02-03
DOI
https://doi.org/10.1002/jcc.70055
Primary Topic
Free Radicals and Antioxidants
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rational Design for Antioxidant Diphenylamine Derivatives Using Quantitative Structure–Activity Relationships and Quantum Mechanics Calculations

Ismail Dwi Putra, Phiphob Naweephattana, Thanyada Rungrotmongkol, Phornphimon Maitarad et al.
Journal of Computational Chemistry
Free Radicals and Antioxidants
article

Rational Design for Antioxidant Diphenylamine Derivatives Using Quantitative Structure–Activity Relationships and Quantum Mechanics Calculations

Ismail Dwi Putra, Phiphob Naweephattana, Thanyada Rungrotmongkol, Phornphimon Maitarad, Borwornlak Toopradab, Ayokanmi Joseph Aremu
article en

Abstract

Diphenylamine (DPA) derivatives, used as antioxidants in rubber-based products, inhibit autoxidation by donating hydrogen atoms to peroxyl radicals. Octanol-water partition coefficient (LogK<sub>ow</sub>), an antioxidant index, helps predict their distribution in hydrophobic polymer matrices. Therefore, this study aimed to investigate the relationship between the structure of DPA derivatives and their antioxidant activities, using machine learning with quantitative structure-activity relationships (QSAR) and quantum mechanics (QM). The structure of DPA derivatives was optimized using Density Functional Theory and analyzed for molecular properties. The QSAR models were trained using important descriptors identified through permutation importance. Among the models developed, the Gradient Boosting Regressor (GBR) showed the best performance, with R<sup>2</sup> of 0.983 and root mean square error (RMSE) of 0.642 for the test set. SHAP analysis revealed that molecular weight and electronic properties significantly influenced LogK<sub>ow</sub> predictions. For instance, a higher molecular weight was associated with increased LogK<sub>ow</sub>, and a higher positive charge of C2 correlated with higher LogK<sub>ow</sub> predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogK<sub>ow</sub> values of 9.789 and 7.102 for D1 and D2, respectively, which are higher than the training compounds in the model. To gain molecular insight, the quantum chemical calculations with M062X/6-311++G(d,p)//M062X/6-31G(d,p) were performed to investigate the bond dissociation enthalpy (BDE). The results showed that D1 (79.50 kcal/mol) and D2 (72.43 kcal/mol) exhibited lower BDEs than the reference compounds, suggesting that the designed compounds have the potential for enhanced antioxidant activity. In addition, the antioxidant reaction mechanism was studied by using M062X/6-311++G(d,p)//M062X/6-31G(d,p) which found that the hydrogen atom transfer is the key step, where D1 and D2 showed activation energy barriers of 10.38 and 6.29 kcal/mol, respectively, compared to reference compounds of R3 (10.39 kcal/mol), R1 (10.40 kcal/mol), and R2 (18.26 kcal/mol). Therefore, our findings demonstrate that integrating QSAR with quantum chemical calculations can effectively guide the design of DPA derivatives with improved antioxidant properties.

Journal of Computational ChemistryVol. 46(4)
Shanghai University (CN), Shanghai University of Engineering Science (CN), University of Shanghai for Science and Technology (CN), Chulalongkorn University (TH)
Chulalongkorn University, National Research Council of Thailand, Faculty of Science, Chulalongkorn University
Clean water and sanitation
Openalex Percentile: Top 98%
Free Radicals and Antioxidants
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.