This project was built using RDKIT, Flask, Microsoft Azure, Docker, and Scikit-learn. The model was trained using the TOX21 dataset with about ~8000 molecules. An in-depth write up can be found here with details including correcting class-imbalance, different classifeirs used, performance metrics, and the theory behind the project.

The web app is now hosted on Render as the cost of Microsoft Azure is much higher.

The Toxicology in the 21st Century program, or Tox21, is a unique collaboration between several federal agencies to develop new ways to rapidly test whether substances adversely affect human health. Substances assayed in Tox21 include a diverse range of products such as: commercial chemicals, pesticides, food additives/contaminants, and medical compounds.

The p53 gene encodes a protein of the same name and is known as a tumor-suppressor protein. The p53 protein is expressed in cells when they undergo DNA damage — which can transform a normal cell into a cancerous one. To counteract the effects, p53 can cause growth arrest, repair DNA, or begin the process of cell death. Therefore, when DNA damage occurs, there is a significant increase in p53 expression. This increase in protein expression is a good indicator of irregular cell health. The Tox21 data was generated by testing cell lines which produce a florescent reporter gene product under the control of p53 cellular machinery. By measuring levels of the reporter gene product against various compounds, researchers were able to determine whether a compound was an agonist (activator) of the p53 pathway or not.

Fingerprinting is a way to represent molecular structure and properties as binary bit strings (0’s and 1's). This representation was initially developed and applied to searching databases for molecules with a specific substructure — but it can also be applied to machine learning. A hash function is a random number generator and is applied to each feature of a molecule, such as the types of bonds and molecules present, which means that they act as seeds to the function.