IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2025-02-03 DOI:10.1002/jcc.70055
Ayokanmi Joseph Aremu, Phiphob Naweephattana, Ismail Dwi Putra, Borwornlak Toopradab, Phornphimon Maitarad, Thanyada Rungrotmongkol
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摘要

二苯胺(DPA)衍生物可用作橡胶制品的抗氧化剂,通过向过氧自由基捐献氢原子来抑制自氧化。辛醇-水分配系数(LogKow)是一种抗氧化指数,有助于预测它们在疏水性聚合物基质中的分布。因此,本研究旨在利用定量结构-活性关系(QSAR)和量子力学(QM)的机器学习方法,研究 DPA 衍生物的结构与其抗氧化活性之间的关系。利用密度泛函理论优化了 DPA 衍生物的结构,并分析了其分子特性。QSAR 模型是利用通过置换重要性确定的重要描述符进行训练的。在所开发的模型中,梯度提升回归器(GBR)表现最佳,测试集的 R2 为 0.983,均方根误差(RMSE)为 0.642。SHAP 分析表明,分子量和电子特性对 LogKow 预测有显著影响。例如,分子量越大,LogKow 值越高,C2 的正电荷越大,LogKow 预测值越高。因此,根据 QSAR 模型的指导设计了两种强效化合物(D1 和 D2)。GBR 模型预测 D1 和 D2 的 LogKow 值分别为 9.789 和 7.102,高于模型中的训练化合物。为了深入了解分子结构,我们用 M062X/6-311++G(d,p)//M062X/6-31G(d,p) 进行了量子化学计算,以研究键解离焓(BDE)。结果表明,D1(79.50 kcal/mol)和 D2(72.43 kcal/mol)的 BDE 值低于参考化合物,表明所设计的化合物具有增强抗氧化活性的潜力。此外,利用 M062X/6-311++G(d,p)//M062X/6-31G(d,p) 研究了抗氧化反应机理,发现氢原子转移是关键步骤,与 R3(10.39 kcal/mol)、R1(10.40 kcal/mol)和 R2(18.26 kcal/mol)等参考化合物相比,D1 和 D2 的活化能垒分别为 10.38 和 6.29 kcal/mol。因此,我们的研究结果表明,将 QSAR 与量子化学计算相结合可以有效地指导设计出具有更好抗氧化性的 DPA 衍生物。
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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 (LogKow), 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 R2 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 LogKow predictions. For instance, a higher molecular weight was associated with increased LogKow, and a higher positive charge of C2 correlated with higher LogKow predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogKow 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.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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