Chemoinformatics aids toxicology in several ways: 1. Predictive Modeling: By using machine learning algorithms, chemoinformatics can predict the potential toxicity of chemical compounds. This helps in early-stage drug development by identifying potentially hazardous substances before they reach animal or human testing. 2. Data Integration: It integrates data from various sources such as chemical databases, toxicology reports, and biological assays, providing a comprehensive understanding of a compound's toxicological profile. 3. Virtual Screening: Chemoinformatics allows for the virtual screening of large chemical libraries to identify compounds with desired toxicological properties, reducing the need for extensive laboratory testing.
