https://github.com/drreetusharma/ai-driven-de-novo-drug-design

de novo drug design (joint project)
https://github.com/drreetusharma/ai-driven-de-novo-drug-design

docker drug machine-learning numpy pandas python pytorch tensorflow

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de novo drug design (joint project)

• Host: GitHub
• URL: https://github.com/drreetusharma/ai-driven-de-novo-drug-design
• Owner: DrReetuSharma
• License: other
• Created: 2024-03-11T10:29:32.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-06-19T04:30:19.000Z (6 months ago)
• Last Synced: 2024-10-13T05:38:15.327Z (3 months ago)
• Topics: docker, drug, machine-learning, numpy, pandas, python, pytorch, tensorflow
• Language: Python
• Homepage: https://aspire10x.com
• Size: 91.8 KB
• Stars: 5
• Watchers: 4
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AI-Driven Drug Design

#### Generating Novel Molecules with Desired Properties

## Overview:

This repository contains code and resources for utilizing AI-driven methods to design novel drug molecules with desired properties. Leveraging Generative Adversarial Networks (GANs) combined with reinforcement learning (RL) techniques, the goal is to generate diverse and high-quality drug candidates optimized for efficacy, selectivity, and pharmacokinetic profiles.

## Features:

Implementation of GAN-based models for generating molecular structures.

Integration of reinforcement learning algorithms to optimize molecular properties.

Preprocessing scripts for data collection and preparation.

Evaluation tools for assessing generated molecules' drug-likeness and bioactivity predictions.

Examples and tutorials demonstrating the usage of AI-driven methods for drug design.

## Dependencies:

Python 3.x

TensorFlow or PyTorch (for GAN implementation)

OpenAI Gym (for RL algorithms)

RDKit (for molecular manipulation and analysis)

Pandas, NumPy, Matplotlib (for data processing and visualization)

License:

This project is licensed under the MIT License. See the LICENSE file for more details.

## Acknowledgments:

This work is inspired by the advancements in AI-driven drug discovery and the contributions of researchers in the field.

Special thanks to the developers of open-source libraries and datasets used in this project.

References:

https://aspire10x.com/data-solutions/

## Structure

- Root

  |- README.md

  |- LICENSE

  |- requirements.txt

  |- src/

  |   |- train_gan.py

  |   |- train_rl.py

  |   |- evaluate_molecules.py

  |- data/

  |   |- dataset.csv

         preprocessed_dataset.csv

        features_array.npy ( result of train_molgan.py)

        adj_array.npy   ( result of train_molgan.py)

  

  |- docs/

  |   |- user_manual.md

  |   |- api_documentation.md

  |- examples/

  |   |- example_notebook.ipynb

  |- tests/

  |   |- test_gan.py

  |   |- test_rl.py

  |- scripts/

  |   |- setup.sh

  |   |- preprocess_data.py

  |- contrib/

      |- contribution_guidelines.md

preprocess_data.py input data/preprocessed_dataset.csv  output:adj_array.npy, feature_array.npy ( smiles to graph)

train_molgan.py   input:.npy  output:  generated_molecules_df.to_csv('data/generated_molecules.csv', index=False)

src/train_rl.py input: models/generator_final.pth  models/discriminator_final.pth

 

## Contact/correspondance:

For any inquiries or feedback, please contact [email protected].

https://aspire10x.com/data-solutions/