A review on machine learning methods for in silico toxicity prediction.

Area of Science:

• Computational toxicology
• Drug discovery and development
• Machine learning applications

Background:

• In silico toxicity prediction is crucial for regulatory decisions and drug design, overcoming limitations of in vitro/vivo methods.
• Computational approaches are widely used for predicting chemical toxicity profiles.
• Structure-Activity Relationship (SAR) modeling is a key area within predictive toxicology.

Purpose of the Study:

• To provide a comprehensive overview of machine learning applications in SAR-based predictive toxicology.
• To highlight the critical role of data quality from raw data to model validation.
• To discuss common challenges and potential solutions in predictive toxicology.

Main Methods:

• Review of machine learning algorithms applied to Structure-Activity Relationship (SAR) data.
• Analysis of the end-to-end process of predictive toxicology modeling.
• Discussion of data preprocessing, model development, and validation strategies.

Main Results:

• Data quality significantly impacts the predictive power of computational toxicology models.
• Common challenges include data imbalance, activity cliffs, model evaluation, and defining applicability domains.
• Plausible solutions for these challenges are presented to improve model reliability.

Conclusions:

• Machine learning offers powerful tools for in silico toxicity prediction.
• Addressing data quality and specific modeling challenges is essential for robust predictive toxicology.
• This review provides a roadmap for applying ML in SAR-based toxicology for safer drug development.