https://github.com/scottmreed/chemistry-augmented-generation

https://github.com/scottmreed/chemistry-augmented-generation

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/scottmreed/chemistry-augmented-generation
• Owner: scottmreed
• License: mit
• Created: 2025-01-06T16:29:14.000Z (6 months ago)
• Default Branch: master
• Last Pushed: 2025-03-11T02:12:51.000Z (4 months ago)
• Last Synced: 2025-05-06T03:01:57.223Z (about 2 months ago)
• Language: Python
• Size: 7.83 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-dspy - Chemistry Augmented Generationn

README

        
# Augmented TPSA Prediction with RAG and MIPRO

This repository contains tools and code to replicate the research presented in the study **"Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations."** The project demonstrates the enhancement of molecular property prediction accuracy by combining Retrieval-Augmented Generation (RAG) and Multiprompt Instruction Proposal Optimizer (MIPRO) with large language models (LLMs). Specifically, this repository focuses on predicting **topological polar surface area (TPSA)** for molecules.

## Features

1. **Outlier Visualization**:  

   `plot_outliers.py` generates supporting information figures for analyzing outliers in TPSA prediction.

   

2. **Randomized Dataset Creation**:  

   `tpsa_random_pubchem.py` creates or overwrites `balanced_tpsa_data.csv` in the `tpsa_saved_data/` directory. This script pulls molecular data from PubChem and ensures a balanced dataset for TPSA predictions.

3. **Typed TPSA Prediction**:  

   `typed_tpsa_prediction.py`:

   - Builds datasets for training and validation.

   - Optimizes prompts for accurate TPSA prediction.

   - Plots results and saves models for reuse.

   - Requires an OpenAI API key specified in a `.env` file.

---

## Installation

### Requirements

- Python 3.9 or higher

- [RDKit](https://www.rdkit.org/) for molecular data handling.

- [DSPy](https://github.com/stanfordnlp/dspy) DSPy 2.5.18 # higher versions lack typed predictors

- OpenAI API access for LLM predictions.

- deepchem 2.8 # PyTorch not required

- additional requirements in requirements.txt

### Setup

1. Clone the repository:

   ```bash

   git clone https://github.com/your-repo/augmented-tpsa-prediction.git

   cd augmented-tpsa-prediction

   ```

2. Install dependencies:

```

pip install -r requirements.txt

```

3. Create a .env file in the root directory and add your OpenAI API key:

```

OPENAI_API_KEY=your_openai_key_here

```

### Re-creating manuscript data and figures

To re-create SI figure S3, run comparison\

To re-create all other SI figures, run plot_outliers\

To re-create manuscript figures, run typed_tpsa_prediction\

To re-train models, delete all .json files from tpsa_saved_data and run typed_tpsa_prediction, then recreate no_demos and no_sig json files manually\

To re-run analyis, delete all csv files from tpsa_saved_data and run typed_tpsa_prediction\

To create a new molecular dataset run tpsa_random_pubchem\

To recreate from scratch with new data, delete all csv and json files, run tpsa_random_pubchem, typed_tpsa_prediction, plot_outliers, and comparison