Mammary carcinogen-protein binding potentials: novel and biologically relevant structure–activity relationship model descriptors

Cunningham, Albert R.; Qamar, Shahid; Carrasquer, C.A.; Holt, Patrick A.; Maguire, Jane; Cunningham, Suzanne L.; Trent, John O.

doi:10.1080/1062936x.2010.501818

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…The cat-SAR learning set consists of the chemical name, its structure as a MOL2 file, and its categorical designation [27]. We reasoned that even if a particular fragment is associated with a ligand, there may yet be other reasons for the compound from which it is derived to be classified as a ligand (e.g., other fragments or chemicophysical properties), thus it would not be expected to be found in 100% of the ligand.…”

Section: Learning Set Developmentmentioning

confidence: 99%

Structure-activity relationship model for estrogen receptor ligands.

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Section: Learning Set Developmentmentioning

confidence: 99%

Structure-activity relationship model for estrogen receptor ligands.

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Global structure–activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

Cunningham¹,

Carrasquer²,

Qamar³

et al. 2012

Carcinogenesis

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Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

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Mammary carcinogen-protein binding potentials: novel and biologically relevant structure–activity relationship model descriptors

Cited by 2 publications

References 43 publications

Structure-activity relationship model for estrogen receptor ligands.

Structure-activity relationship model for estrogen receptor ligands.

Global structure–activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

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