The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual template-based design was carried out to evaluate thirty-four compounds from derivatives of thiophene, pyrimidine, coumarin, pyrazole and pyridine with anti-breast cancer activities. The chemical structures of the compounds were drawn with chem draw v.12.0.2 and they were optimized using Spartan 14 software. The molecular descriptors were calculated with the aid of PaDel descriptor software. The dataset was curated and then divided into training and test set that was used to generate and validate the model. The first out of the four models generated was chosen as the paramount model with statistical validations of R2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ = 0.9814, $$Q_{{{\text{cv}}}}^{2}$$ = 0.9763, min expt. error for non-significant LOF (95%) = 0.0679, an external validation $$R_{{{\text{test}}}}^{2}$$ of 0.8240 and coefficient of Y-randomization ( $${\text{cR}}_{{\text{p}}}^{2}$$ ) = 0.8200, which confirm the robustness of the model. The high predictive power of the generated model describes the models’ reliability and the designed compounds pointed out compound 2 with pGI50 = 4.2504 as the best designed compound to inhibit breast cancer, compared to its co-designed compounds and the template. The results of this research provide vital information to the pharmaceutical chemists and the pharmacologist in the course of developing new breast cancer drugs.

中文翻译：

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一些化合物作为潜在抗乳腺癌药物的计算评估

现有抗乳腺癌药物的高耐药性和毒性的出现，要求设计具有更高抗乳腺癌活性的新药物。进行了一种结合定量构效关系和基于虚拟模板设计的计算技术，以评估来自噻吩、嘧啶、香豆素、吡唑和吡啶衍生物的 34 种具有抗乳腺癌活性的化合物。化合物的化学结构使用chem draw v.12.0.2绘制，并使用Spartan 14软件进行优化。借助 PaDel 描述符软件计算分子描述符。数据集经过整理，然后分为训练集和测试集，用于生成和验证模型。生成的四个模型中的第一个被选为最重要的模型，其统计验证为 R2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ = 0.9814, $$Q_{{ {\text{cv}}}}^{2}$$ = 0.9763, min ext. 非显着 LOF (95%) 的误差 = 0.0679，外部验证 $$R_{{{\text{test}}}}^{2}$$ 为 0.8240 和 Y 随机化系数（ $${\text {cR}}_{{\text{p}}}^{2}$$ ) = 0.8200，证实了模型的稳健性。生成的模型的高预测能力描述了模型的可靠性，并且设计的化合物指出，与其共同设计的化合物和模板相比，pGI50 = 4.2504 的化合物 2 是抑制乳腺癌的最佳设计化合物。