Toxicology-Prediction
A model that uses the Tox21 dataset [1] to attempt to find a relationship between molecular structure and relative toxicity, along with a Streamlit web app for interaction.
💡 Inspiration
A study from the American Society for Biochemistry and Molecular Biology (ASBMB) reports that:
- 90% of drugs fail clinical testing
- 30% of failures are due to molecular toxicity [2]
These failures result in billions of dollars in losses for pharmaceutical companies.
Our project addresses this challenge by:
- Developing a machine learning model that predicts toxicity directly from molecular structure
- Using Structure-Activity Relationship (SAR) principles to justify predictions based on similarity to known toxic compounds
- Building an interactive Streamlit app to make the model accessible for exploration and teaching
⚛️ By combining machine learning with chemistry fundamentals, this project explores how AI can help reduce drug failure rates, save costs, and accelerate pharmaceutical research.
📚 References
[1] National Center for Advancing Translational Sciences, “Toxicology in the 21st Century (Tox21)”, [Online]. Available: https://tox21.gov. Accessed: Feb. 20, 2025.
[2] D. Sun, “90% of drugs fail clinical trials”, ASBMB Today, Mar. 12, 2022. [Online]. Available: https://www.asbmb.org/asbmb-today/opinions/031222/90-of-drugs-fail-clinical-trials. Accessed: Feb. 20, 2025.