In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the pK_a’s of quinazoline derivatives, which constitute a very important class of natural and synthetic compounds in organic, pharmaceutical, agricultural and medicinal chemistry areas. Linear relationships between the experimental pK_a’s and nine different DFT descriptors (atomic charge on nitrogen atoms (Q(N)), ionization energy (I), electron affinity (A), chemical potential (μ), hardness (η), electrophilicity index (ω), fukui functions (f ⁺, f ⁻), condensed dual descriptor (Δf) and local hypersoftness ($s_k^{\left(2\right)}$) were considered. Several DFT methods (a combination of five DFT functionals and two basis sets) in conjunction with two different implicit solvent models were tested, and among them, M06L/6–311++G(d,p) level of theory employing the CPCM solvation model was found to give the strongest correlations between the DFT descriptors and the experimental pK_a’s of the quinazoline derivatives. The calculated atomic charge on N₁ atom (Q(N₁)) was shown to be the best descriptor to reproduce the experimental pK_a’s (R²=0.927), whereas strong correlations were also derived for A, ω, μ, $s_k^{\left(2\right)}$ and Δf. The QM-based protocols presented in this study will enable fast and accurate high-throughput pK_a predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for pK_a predictions.

在这项研究中，使用了几种基于量子力学的计算方法，以提出准确的方案来预测喹唑啉衍生物的 p K _a，喹唑啉衍生物构成有机、制药、农业和农业领域中一类非常重要的天然和合成化合物。药物化学领域。实验 p K _a与九种不同 DFT 描述符（氮原子上的原子电荷 ( Q (N))、电离能 ( I )、电子亲和力 ( A )、化学势 (μ)、硬度 (η)之间的线性关系、亲电指数 (ω)、福井函数 ( f ⁺ , f ⁻ )、压缩对偶描述符 (Δ f ) 和局部超柔软度 ($s_k^{（2）}$） 被认为是。测试了几种 DFT 方法（五个 DFT 泛函和两个基组的组合）与两种不同隐式溶剂模型的结合，其中 M06L/6–311++ G (d,p) 级别的理论采用 CPCM 溶剂化发现该模型在 DFT 描述符和喹唑啉衍生物的_实验pKa之间具有最强的相关性。计算出的 N ₁原子上的原子电荷 ( Q (N ₁ )) 被证明是重现实验 p Ka 的最佳描述符₍ R 2 ^{= 0.927)，而}A、 ω 、 μ也得出了强相关性,$s_k^{（2）}$和Δf。本研究中提出的基于 QM 的协议将能够快速、准确地对喹唑啉衍生物进行高通量 p K _a预测，并且所得出的关系可以有效地用于数据生成，以实现 p K _a预测的成功机器学习模型。