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