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

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). 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.

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Journal
BMC Research Notes
Published
2026-04-03
DOI
https://doi.org/10.1186/s13104-026-07791-w
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Robert Balawender, A. Holas, Meressa Abrha Welearegay, Farnaz Heidar-Zadeh et al.
BMC Research Notes
Computational Drug Discovery Methods
article

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

Robert Balawender, A. Holas, Meressa Abrha Welearegay, Farnaz Heidar-Zadeh, Paul W. Ayers, Matthew Chan
article en

Abstract

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). 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.

BMC Research Notes
Queen's University (CA), Institute of Physical Chemistry (PL), Mekelle University (ET), McMaster University (CA), Polish Academy of Sciences (PL)
Fundacja na rzecz Nauki Polskiej
Openalex Percentile: Top 8%
Computational Drug Discovery Methods
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