xHaMMaDy/AD-scRNA2QSAR
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.
This project helps Alzheimer's Disease researchers analyze single-cell RNA sequencing data and predict potential drug candidates. It takes raw single-cell RNA sequencing data and chemical compound information (SMILES strings) as input. It then outputs detailed cell type annotations, cell communication networks, and predictions of a compound's bioactivity against AD-related targets. Neurodegenerative disease researchers, especially those in drug discovery, would find this useful for accelerating their work.
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Use this if you are an Alzheimer's Disease researcher needing to go from raw single-cell genomics data to predictive drug discovery models efficiently, without extensive coding.
Not ideal if your research is outside of Alzheimer's Disease or you require highly customized, domain-specific machine learning models beyond bioactivity prediction.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 13, 2025
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