Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

Area of Science:

• Computational toxicology
• cheminformatics
• toxicology

Background:

• Predictive toxicology is increasingly relying on computational methods over traditional in vivo studies.
• In vitro and in silico approaches, including quantitative structure-activity relationship (QSAR) modeling and absorption, distribution, metabolism, and excretion (ADME) calculations, are now widely used.
• Machine learning (ML) offers powerful tools for advancing predictive toxicology.

Purpose of the Study:

• To provide an overview of machine learning applications in predictive toxicology.
• To summarize recent successes and compare various ML models used in the field.
• To identify current challenges and potential future improvements in ML for toxicology.

Main Methods:

• Review of machine learning algorithms applied to predictive toxicology.
• Inclusion of methods such as Support Vector Machines (SVMs), Random Forest (RF), Decision Trees (DTs), neural networks, regression models, Naïve Bayes, k-nearest neighbors, and ensemble learning.
• Comparative analysis of dominant ML models in the context of available toxicological data.

Main Results:

• Machine learning methods, particularly SVMs, RF, and DTs, have shown significant success in predictive toxicology.
• These models are favored due to the specific characteristics of toxicological datasets.
• A comparison of various ML models highlights their strengths and weaknesses in predicting toxicological endpoints.

Conclusions:

• Machine learning is a transformative technology in predictive toxicology, enabling more efficient and ethical assessments.
• SVMs, RF, and DTs are currently the leading ML approaches in this domain.
• Addressing current challenges and exploring new avenues for improvement will further enhance the utility of ML in toxicology.