A comprehensive computational pipeline that bridges single-cell genomics and cheminformatics to accelerate Alzheimer's Disease research. This project integrates advanced bioinformatics and machine learning to create a seamless workflow from raw single-cell RNA sequencing data to predictive drug discovery models.

Accelerate Alzheimer's Disease research with our AD-scRNA2QSAR pipeline. Transform scRNA-seq data into QSAR models seamlessly. 🌟🛠️

AI-Powered Drug Discovery

Explore data collection, analysis, and machine learning for drug discovery. Create predictive models, perform EDA, and deploy as web apps.

Jupyter notebook and tabular data files to support the publication "Combined In vitro and In silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity" (Charest et al. 2025)

QSAR modelling

Post Drug repurposing analysis utilizing ADME screening, toxicity predictions with Tox21, and QSAR modeling to evaluate absorption, distribution, metabolism, and excretion profiles, predict toxicities, and assess molecular activity. This streamlined workflow accelerates drug discovery while ensuring safety and efficacy.

Some thoughts on building QSARModeling in Rust.