Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Area of Science:

• Computational chemistry and toxicology
• Pharmacology and drug development

Background:

• Experimental toxicity evaluation is resource-intensive and faces challenges with in vivo translation, limiting data availability.
• Machine learning (ML) offers potential to augment or replace traditional methods in drug discovery for property and toxicity prediction.
• Existing ML applications face risks from biased data, inappropriate algorithms, and poor validation, leading to inaccurate predictions and suboptimal decisions.

Purpose of the Study:

• To highlight the critical importance of understanding ML model predictive validity in drug discovery.
• To emphasize the need for enhanced understanding and application of ML models for toxicity prediction.
• To focus on well-defined datasets for small molecule toxicity prediction.

Main Methods:

• The study emphasizes a framework based on five crucial pillars for successful ML-driven molecular property and toxicity prediction.
• Pillar 1: Data set selection for toxicity prediction.
• Pillar 2: Appropriate structural representations.
• Pillar 3: Suitable model algorithms.
• Pillar 4: Robust model validation approaches.
• Pillar 5: Effective translation of predictions into decision-making.

Main Results:

• Accurate ML predictions depend on addressing data biases, algorithm selection, and validation methodologies.
• A structured approach focusing on the five pillars can mitigate risks associated with ML in drug discovery.
• Improved ML model understanding and application are vital for reliable toxicity predictions.

Conclusions:

• Enhancing the understanding and application of ML models, particularly for toxicity prediction using well-defined datasets, is crucial for advancing drug discovery.
• Addressing the five key pillars—data selection, structural representation, algorithm choice, validation, and decision translation—will improve ML model reliability.
• Fostering collaboration between ML researchers and toxicologists is essential for successful ML implementation in drug development, leading to faster timelines and improved decision quality.