How are ML and AI transforming toxicological data analysis?
Traditional toxicological methods often rely on labor-intensive laboratory experiments and animal testing. ML and AI offer an alternative by leveraging large datasets to predict toxicological outcomes. They use algorithms to identify patterns and relationships within extensive chemical and biological data, enabling researchers to predict toxicity with high accuracy. For instance, by using quantitative structure-activity relationship (QSAR) models, AI can predict the toxic effects of new compounds based on their chemical structure.
