NBGA Optimization

This project implements a Neighborhood-Based Genetic Algorithm (NBGA) for ligand optimization, inspired by the approach described in An Evolutionary Approach to Drug-Design Using a Novel Neighbourhood Based Genetic Algorithm research paper. The algorithm evolves ligand structures to minimize their interaction energy with a protein's active site, using a tree-based representation for ligands and a dynamic neighborhood topology for genetic operations.

Features

• NBGA implementation for ligand-protein binding optimization.
• TSP Algorithm Comparison: Compare NBGA, SWAP_GATSP, OX_SIM, and MOC_SIM on classic TSPLIB datasets with interactive plots and tables.
• See algorithm documentation and comparisons for details on TSP implementations and improvements.
• Ligand structure represented as a variable-length tree of functional groups.
• Interaction energy calculated using a modified Lennard-Jones potential.
• Streamlit integration for interactive parameter tuning and visualization.
• Reproducible results with fixed random seed.

Algorithm Overview

• Ligand Representation: Each ligand is a chromosome (list) where each gene represents a functional group or an empty position.
• Fitness Evaluation: The interaction energy between the ligand and protein residues is minimized. Empty or mostly empty ligands are penalized.
• Genetic Operators: Single-point crossover, uniform random mutation, and trio tournament selection.
• Neighborhood Topology: Individuals are arranged in a ring, and neighborhoods are randomized each generation to maintain diversity.
• Visualization: The evolution of the best fitness (energy) is plotted and can be smoothed for trend analysis.

For more details, see the original paper.

Getting Started

Dependency management:
All dependencies are managed using pyproject.toml and locked in poetry.lock. It is recommended to use Poetry to manage dependencies and the virtual environment

1. Clone the Repository

git clone https://github.com/AnishSarkar22/nbga-optimization.git
cd NBGA-TSP

2. Set Up the Environment with Poetry

# Install Poetry if you don't have it
pip install poetry

# Install dependencies and create a virtual environment
poetry install

# Activate the virtual environment
poetry shell

3. Run the Streamlit App

streamlit run Home.py

This will launch a web interface where you can adjust algorithm parameters and visualize the optimization process.

Usage

• NBGA Ligand Optimization: Adjust parameters and run the genetic algorithm to optimize ligand binding.
• TSP Algorithm Comparison: Select the TSP Comparison from the sidebar, choose your dataset directory, and run the comparison to visualize and compare algorithm performance.
• View the best ligand found and its interaction energy.
• Analyze the energy evolution plot for convergence trends.

File Structure

• Home.py — Main NBGA implementation and Streamlit interface.
• pages/1_Ligand_Optimization.py — Script for Ligand Optimization.
• pages/2_TSP_Comparison.py — Script for TSP algorithm Comparison.
• algorithms/ligand.ipynb — NBGA algorithm and utilities for ligand optimization.
• algorithms/tsp.ipynb — NBGA and other algorithms for TSP (original version).
• algorithms/tsp_enhanced.ipynb — Enhanced NBGA and TSP algorithms.
• algorithms/README.md — Comparison and documentation of algorithms.
• evaluation_dataset/ — Reference optimal TSP datasets and extraction scripts.
• tsp_dataset/ — TSP datasets and extraction scripts.
• Other scripts and data files as needed.

Reference

If you use this code or approach in your research, please cite:

Arnab Ghosh, Avishek Ghosh, Arkabandhu Chowdhury, Amit Konar.
"An Evolutionary Approach to Drug-Design Using a Novel Neighbourhood Based Genetic Algorithm."
arXiv:1205.6412

This repository provides an implementation based on the above paper, with additional improvements and extensions.

License

This project is licensed under the MIT License. See the original paper for further details.