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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
Last synced: 18 days ago
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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 (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-19T04:30:19.000Z (5 months ago)
- Last Synced: 2024-10-13T05:38:15.327Z (about 1 month 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.mdpreprocess_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/