What Are the Key Applications of Machine Learning in Toxicology?
Predictive Toxicology: ML models can predict the toxicity of new compounds, helping in the early-stage screening of drug candidates or environmental chemicals. Risk Assessment: ML can evaluate and quantify the risks associated with exposure to various chemicals, aiding regulatory agencies in decision-making. Mechanistic Insights: By analyzing biological data, ML can provide insights into the mechanisms of toxicity, facilitating understanding at the molecular level. Data Integration: ML can integrate diverse data sources, including genomic, proteomic, and metabolomic data, to provide a holistic view of toxicological effects.
