Full stack machine learning web app that predicts whether a molecular compound is an environmental carcinogen. Built using RDKit, Flask, Docker, Microsoft Azure, Scikit-learn, and Bootstrap. Read in-depth about it here.
An end-to-end machine learning web app that generates fantasy themed text. Built using the GPT2 model, Tensorflow, Chart.js, Google Cloud Platform, Docker, and Bulma CSS.
My very first web app which I used for converting lists of drug IDs to SMILES strings. Built using Flask, Pandas, Numpy, and Google Cloud Platform
A Brief Introductory article on Federated Learning.
An article going through the math behind calculating Maximum Likelihood Estimation by hand.
Masters Research Project — Exploring the Feasibility of Novel Small Molecule Patentability Through Building a PostgreSQL Database and Identifying Tanimoto Nearest Neighbours.
Honours Thesis — Detecting Prostate Cancer Using Tumor-Activatable Minicircles Encoding the Secretable Biomarker SEAP
Automating daily tasks, coverting SAS projects into python scripts and performing data integrity checks on enterprise data.
Built a cheminformatics data pipeline, put together a PostgreSQL database of 17 million small drug molecules, and integrated KNN/Tanimoto search functionality to the database.
Built a full-stack machine learning application to predict environmental carcinogens, converted molecules to binary fingerprints and trained logistic regression, knn and gradient boosting models - achieving a final accuracy of 96.4%.