Scholarlink

<p>Quantitative Structure–Activity Relationships are established in this work to predict the anticarcinogenic activities of flavonoids and related compounds in the MCF-7 breast cancer cell line. The selected descriptors tend to faithfully predict the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(IC_{50}^{{\,48{\text{h}}}}\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(IC_{50}^{{\,72{\text{h}}}}\)</EquationSource> </InlineEquation> bioactivities, showing a straight-line trend in the correlation with the experimental data, even in this structurally diverse set of molecules. Finally, several structurally related compounds with unknown experimental anticarcinogenic activities are predicted. Therefore, the present study provides a guide for the rational design of potential new therapeutic molecules through the structure–activity parallelisms found. This work also demonstrates that QSAR models lead to evaluating whether the acquisition of experimental data is in agreement with the experimental protocol and methodologies, discriminating based on the domain of applicability and the fit to the QSAR model used.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From bioactivity prediction to experimental protocol evaluation: QSAR models on the anticarcinogenic activity of flavonoids and related compounds in MCF-7 breast cancer models

  • 期刊论文

  • 发表日期：

    2026年3月30日

查看全文
  • Nicolas A. Szewczuk,
  • Alicia B. Pomilio,
  • Pablo R. Duchowicz

摘要

Quantitative Structure–Activity Relationships are established in this work to predict the anticarcinogenic activities of flavonoids and related compounds in the MCF-7 breast cancer cell line. The selected descriptors tend to faithfully predict the \(IC_{50}^{{\,48{\text{h}}}}\) and \(IC_{50}^{{\,72{\text{h}}}}\) bioactivities, showing a straight-line trend in the correlation with the experimental data, even in this structurally diverse set of molecules. Finally, several structurally related compounds with unknown experimental anticarcinogenic activities are predicted. Therefore, the present study provides a guide for the rational design of potential new therapeutic molecules through the structure–activity parallelisms found. This work also demonstrates that QSAR models lead to evaluating whether the acquisition of experimental data is in agreement with the experimental protocol and methodologies, discriminating based on the domain of applicability and the fit to the QSAR model used.