Machine Learning Methods in Computational Toxicology.

Area of Science:

• Computational toxicology
• Machine learning applications
• Toxicology informatics

Background:

• Computational toxicology leverages computational methods to predict chemical toxicity.
• Diverse machine learning (ML) approaches are increasingly vital for analyzing complex toxicological data.
• Traditional methods are often complemented by advanced ML for enhanced predictive power.

Purpose of the Study:

• To review various supervised and unsupervised machine learning methods applicable to computational toxicology.
• To highlight the role of different molecular descriptors, both handcrafted and data-driven.
• To emphasize the necessity of employing a wide range of ML techniques in the field.

Main Methods:

• Supervised learning: multiple linear regression, naïve Bayes, k-nearest neighbors, support vector machines, decision trees, ensemble learning, random forest, neural networks, deep learning.
• Unsupervised learning: Kohonen's self-organizing maps for data analysis and visualization.
• Molecular descriptors: handcrafted and data-driven approaches, including fragment descriptors, graph mining, and graph kernels.

Main Results:

• Various ML algorithms demonstrate utility in classification and regression tasks for toxicology.
• Data-driven descriptors and graph-based methods offer novel ways to represent molecular information.
• Unsupervised methods aid in data exploration, visualization, and integrating predictions with analysis.

Conclusions:

• A comprehensive application of diverse machine learning methods is essential for robust computational toxicology.
• Integrating various ML techniques and descriptor types enhances the predictive accuracy and scope of toxicological assessments.
• The field necessitates continuous exploration and application of advanced ML for chemical safety evaluation.