How Do Machine Learning Models Enhance Toxicological Predictions?
Machine learning models enhance toxicological predictions in several ways:
- Data Processing: ML models can handle large volumes of toxicology data efficiently, enabling the identification of patterns and trends that are not apparent through traditional methods. - Feature Selection: Advanced algorithms can identify the most relevant features that contribute to toxicity, reducing dimensionality and improving model performance. - Predictive Accuracy: By leveraging historical data, ML models can predict the toxicity of new compounds with high accuracy, aiding in the early detection of potentially harmful substances. - High-throughput Screening: ML models facilitate the rapid screening of vast chemical libraries, expediting the identification of safe and effective drugs.
