Machine-Learning Prediction of Chemical Toxicity Using Molecular Fingerprints
When arranged in certain ways or orders, stereochemical functional groups can cause health risks upon entering the human body. Interacting with proteins of specific organs, different forms of the same chemical may bind to various proteins causing malicious expression of teratogenic hormones. This project using MATHEMATICA develops a machine learning workflow to predict molecular toxicity from chemical structure. Molecules are first imported as SMILES strings paired with toxicity vectors, then checked with a custom `SMILESQ` function so invalid molecules can be removed. Valid molecules are converted into molecular fingerprints using `MoleculeFingerprint`, allowing each structure to be represented numerically for classification. The dataset is split into training and testing groups so the model can be evaluated on molecules it has not seen before. After designing two Machine Learning functions for general toxicity based on 12 assays from Tox21, a unique fingerprint designed specifically in the presence of toxicity is then designed and tested. With now three Machine Learning functions, an interface is then designed to choose what type of prediction to run as well as highlighting the possible malicious chemicals groups within the molecule while plotting the molecule out, specifically with accuracy rates as high as 97% for predicting toxicity simply just based on the SMILES string of the molecule.