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

The provided text explores the increasing value of machine learning (ML) for predicting molecular properties and toxicity in drug discovery, emphasizing its potential to augment or replace traditional experimental processes. It highlights five crucial pillars for success in ML-driven toxicity prediction: data set selection, structural representations, model algorithms, model validation, and translation of predictions to decision-making. The sources discuss various types of chemical representations, including 2D, 3D, and graph-based methods, along with the advantages and limitations of different ML model types, such as generative models. Finally, the importance of robust model validation strategies, including retrospective and prospective approaches, is underscored, along with the critical role of understanding pharmacokinetics for human-relevant decision-making in drug safety.