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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Bioinformatics

Background:

  • Industrial chemicals pose human health risks due to carcinogenicity.
  • Predictive models for carcinogenicity are crucial for risk assessment.
  • Rat carcinogenicity data serves as a valuable proxy for human relevance.

Purpose of the Study:

  • To develop robust predictive models for binary carcinogenicity data in rats.
  • To associate rat carcinogenicity with human carcinogenicity.
  • To identify structural features influencing chemical carcinogenicity.

Main Methods:

  • Feature-based and chemical language modeling approaches were employed.
  • Classification read-across structure-activity relationship (c-RASAR) models were developed using machine learning algorithms, including artificial neural networks (ANN).
  • Long short-term memory (LSTM) architecture was utilized for models based on SMILES strings, alongside logistic regression with ARKA descriptors.

Main Results:

  • The logistic regression RASAR-ARKA model demonstrated the best performance.
  • The ANN c-RASAR model also showed efficient prediction capabilities for external data.
  • The ARKA framework facilitated the identification of activity cliffs and explained prediction errors.

Conclusions:

  • The developed models provide an efficient framework for predicting chemical carcinogenicity.
  • Structure-function analysis revealed that nitrogen atoms (hydrazine derivatives, nitrosamines) and branching increase carcinogenicity.
  • Increased molecular size was found to reduce carcinogenic potency.