Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models

Area of Science:

• Pharmacology
• Toxicology
• Computational Chemistry

Background:

• Reproductive toxicity is a major concern for drug safety, impacting fertility and offspring health.
• Identifying reproductive toxicants early in drug development is critical.
• Traditional methods struggle with the complex mechanisms of reproductive toxicity.

Purpose of the Study:

• To conduct an in silico investigation of reproductive toxic molecules.
• To identify and categorize compounds exhibiting reproductive toxicity.
• To evaluate the efficacy of computational models for predicting reproductive toxicity.

Main Methods:

• In silico analysis of physicochemical properties, target prediction, and pathway analyses (KEGG, GO).
• Development and application of Support Vector Machines (SVMs) with molecular descriptors.
• Implementation of a custom deep learning model utilizing molecular SMILES and graphs.

Main Results:

• Three distinct categories of reproductive toxic molecules were identified: Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate.
• SVM models achieved 0.85 accuracy, while deep learning models reached 0.88 accuracy in predicting reproductive toxicity.
• Computational models demonstrated effectiveness in identifying complex toxicity mechanisms.

Conclusions:

• In silico methods, particularly machine learning, offer a powerful approach for pharmaceutical safety evaluation.
• The developed models provide a robust framework for enhancing the prediction of reproductive toxicity.
• This study underscores the potential of computational toxicology in drug development.