README.md

---
datasets:
  - sarahantgan/5HT_Ki_Prediction
language:
  - en
license: cc-by-4.0
tags:
  - bioactivity
  - cheminformatics
  - regression
  - serotonin
  - binding-affinity
size_categories:
  - 100-1K
task_categories:
  - regression
pretty_name: 5-HT Ki Prediction Dataset
---

# Serotonin Receptor (5-HT) Binding Affinity Prediction Dataset

This dataset was curated from the PDSP Ki Database to support training machine learning models that predict binding affinity (Ki in nM) of ligands to serotonin (5-HT) receptors.

## Files Included

- `curated_ki_database.csv`: Cleaned Ki dataset filtered for 5-HT targets
- `fingerprints_with_ki.csv`: Molecules represented as 2048-bit Morgan (ECFP4) fingerprints
- `rf_model.pkl`: Trained scikit-learn RandomForestRegressor model
- `test_predictions.csv`: Predictions vs. actual Ki values on the test set
- `train_model.ipynb`: Full Jupyter notebook with training code and evaluation

## Modeling Approach

- Molecules were standardized using [MolVS](https://molvs.readthedocs.io).
- SMILES strings were converted to 2048-bit Morgan fingerprints (ECFP4) using RDKit.
- A Random Forest Regressor was trained using scikit-learn.
- The model was evaluated on a held-out 20% test set.

### 📈 Model Performance

- **R² Score**: `your_R2_here`
- **RMSE**: `your_RMSE_here` nM

## Source

- PDSP Ki Database: https://pdsp.unc.edu/databases/kidb.php

## Project by 

Sara Hantgan  
University of Michigan | BIOINF 595 Final Project  
Winter 2025