What are the Future Directions for Machine Learning in Toxicology?
The future of ML in toxicology is promising, with ongoing research focused on:
- Explainable AI: Developing models that not only predict outcomes but also provide explanations for their predictions, enhancing transparency and trust. - Integration with Omics Data: Combining ML models with omics data (genomics, proteomics, etc.) to create comprehensive toxicological profiles. - Automated Model Building: Utilizing automated machine learning (AutoML) to streamline the model development process, making it accessible to non-experts. - Regulatory Acceptance: Working towards the acceptance of ML models by regulatory bodies, ensuring that they meet stringent safety and efficacy standards.
