Nine novel 4-aminoquinazoline derivatives were designed and synthesized. Biochemical and cellular analyses demonstrated that most of the derivatives exhibited a strong activity to inhibit Aurora A and B kinases and to suppress the proliferation of a panel of human tumor cell lines (U937, K562, A549, LoVo, and HT29). Quantum chemical studies were also carried out to determine the structural features of these compounds engaged in the inhibition of Aurora kinases.

Here, we describe the design and synthesis of two series of 4-pyrrylamino quinazolines as new analogs of the epidermal growth factor receptor inhibitor gefitinib. In vitro antitumor activity of these novel compounds against pancreatic (Miapaca2) and prostate (DU145) cancer cell lines was evaluated. Compared with the parental gefitinib, all 18 derivatives show a greatly increased cytotoxicity to cancer cells. In vitro kinase inhibitory activity on epidermal growth factor receptor was also investigated. Among them, compounds GI-6, GII-4, GII-6, GII-8, and GII-9 are more potential receptor tyrosine kinase (RTK) inhibitors. Based on these results, we propose simple structure–activity relationship to provide information for designing and developing more potent antitumor agents.

Considering the fact that majority of support vector regression models have not been fully optimized in the realm of quantitative structure-activity relationship, an idea of simultaneous optimization has been proposed and evaluated on a set of novel kinase insert domain receptor/vascular endothelial growth factor receptor-2 inhibitors including naphthalene and indazole-based compounds in this study. After the powerful feature searching process using genetic algorithm, the final support vector regression model was constructed on an optimal set of six descriptors, based on which simultaneous optimization was carried out. Specifically, the global optimum is grid searched in the joint parametric space defined by cost (C), γ and ε, where performance of support vector regression using each combination of (C, γ, ε) is evaluated and recorded, resulting in bulky information. Based on the data decomposition strategies provided in the main paper, the best performance was achieved for C = 1.2, γ = 0.15 and ε = 0.065. As a comparison, a linear model based on genetic algorithm-multiple linear regression has also been developed and compared. Performances of these models are rigorously validated using both leave-one-out cross-validation and also external validation. The significant higher R² (0.908, 0.837) and lower root-mean-square error (0.237, 0.311) for 45 training and 16 test samples compared to that of genetic algorithm-multiple linear regression (0.764, 0.700 and 0.402, 0.421) confirm the superior performance of genetic algorithm-support vector regression. Robustness and predictive ability of this model is further prudently evaluated. The resulting models introduced not only the idea of simultaneous optimization in support vector regression, but also an efficient strategy for estimating the vascular endothelial growth factor receptor-2 inhibitory activity of novel naphthalene and indazole-based compounds. Moreover, some insights into the structural features related to the biological activity of these compounds have also been provided, which might be of great help for further designing novel vascular endothelial growth factor receptor-2/kinase insert domain receptor inhibitors with potent activity.

Several novel quinazolino [3, 4-a] thieno [3, 2-d] pyrimidin-8-one derivatives were synthesized. All of the compounds were determined against MiaPaCa2 and DU145 cells in vitro, and the crystal structures of analog 8 and 20 in the active site of the EGFR complexes were presented. The entire compounds had been identified by ¹HNMR, ¹³CNMR, IR, MS and EA.

Quantitative structure–activity relationship analysis has been carried out for 74 diaryl ureas including aminobenzoisoxazole ureas, aminoindazole ureas, aminopyrazolopyridine ureas against vascular endothelial growth factor receptor-2 kinase using both linear and non-linear models. Considering simplicity and predictivity, multivariate linear regression was first employed in combination with various variable selection methods, including forward selection, genetic algorithm and enhanced replacement method based on descriptors generated by e-dragon software. Another model using support vector regression has also been constructed and compared. Performances of these models are rigorously validated by leave-one-out cross-validation, fivefold cross-validation and external validation. The enhanced replacement method model significantly outperforms the others with R² = 0.813 and  = 0.809. Robustness and predictive ability of this model is prudently evaluated. Moreover, to find out the most significant features associated with the difference between highly active compounds and moderate ones, two classification models using linear discriminant analysis and support vector machine were further developed. The performance of support vector machine significantly outperforms linear discriminant analysis, with leave-one-out cross-validation and external validation prediction accuracy reaching 0.838 and 0.857, respectively. The resulting models could act as an efficient strategy for estimating the vascular endothelial growth factor receptor-2 inhibiting activity of novel diaryl ureas and provide some insights into the structural features related to the biological activity of these compounds.