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Objectives <p>Predicting the carcinogenicity of polycyclic aromatic hydrocarbons (PAHs) is challenging due to their structural complexity and diverse biological activity. Carcinogenic potential is strongly influenced by molecular structure mainly ring arrangement and substitution patterns that affect metabolic activation and DNA reactivity. To improve predictive modeling, we compiled a dataset of 302 polycyclic aromatic hydrocarbons featuring novel topological indices-Bay, Fjord, Harbor, and Canyon (BFHC)-along with experimental carcinogenicity classifications, log P, and log Iball values. PAHs were further classified according to their fused-benzene ring topology as linear, angular (cata-condensed), or peri-condensed (clustered) structures. The BFHC indices were developed as part of a PhD dissertation (<a href="https://rcin.org.pl/publication/63491">https://rcin.org.pl/publication/63491</a>).</p> Data description <p>The dataset provides structural and experimental information for 302 PAHs, including both unsubstituted and hydrocarbon-substituted molecules (such as alkyl, benzyl, and small ring substituents). Each compound is characterized by XYZ Gaussian input files, IUPAC name, molecular structure, carbon and hydrogen counts, and counts of four topological indices - Bay, Fjord, Harbor, and Canyon -collectively referred to as BFHC indices. The dataset also includes experimental carcinogenicity classified into seven qualitative categories, physicochemical properties such as log P (131 compounds) and log Iball (117 compounds), and fused benzene ring-based topology classification (linear, angular, peri-condensed). The dataset is publicly available and suitable for Quantitative Structure-Activity Relationships (QSAR) and machine learning applications in toxicology and carcinogenic potency of PAHs.</p>

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Topological regions and experimental carcinogenicity data of polycyclic aromatic hydrocarbons: a comprehensive resource for prediction models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Meressa Welearegay,
  • Andrzej Holas,
  • Robert Balawender,
  • Paul W. Ayers,
  • Matthew Chan,
  • Farnaz Heidar-Zadeh

摘要

Objectives

Predicting the carcinogenicity of polycyclic aromatic hydrocarbons (PAHs) is challenging due to their structural complexity and diverse biological activity. Carcinogenic potential is strongly influenced by molecular structure mainly ring arrangement and substitution patterns that affect metabolic activation and DNA reactivity. To improve predictive modeling, we compiled a dataset of 302 polycyclic aromatic hydrocarbons featuring novel topological indices-Bay, Fjord, Harbor, and Canyon (BFHC)-along with experimental carcinogenicity classifications, log P, and log Iball values. PAHs were further classified according to their fused-benzene ring topology as linear, angular (cata-condensed), or peri-condensed (clustered) structures. The BFHC indices were developed as part of a PhD dissertation (https://rcin.org.pl/publication/63491).

Data description

The dataset provides structural and experimental information for 302 PAHs, including both unsubstituted and hydrocarbon-substituted molecules (such as alkyl, benzyl, and small ring substituents). Each compound is characterized by XYZ Gaussian input files, IUPAC name, molecular structure, carbon and hydrogen counts, and counts of four topological indices - Bay, Fjord, Harbor, and Canyon -collectively referred to as BFHC indices. The dataset also includes experimental carcinogenicity classified into seven qualitative categories, physicochemical properties such as log P (131 compounds) and log Iball (117 compounds), and fused benzene ring-based topology classification (linear, angular, peri-condensed). The dataset is publicly available and suitable for Quantitative Structure-Activity Relationships (QSAR) and machine learning applications in toxicology and carcinogenic potency of PAHs.