knf 0.1.0

Automated Descriptor Engine for SNCI, SCDI, and 9D KNF

KNF-CORE: Automated Descriptor Engine

KNF-CORE is a specialized computational chemistry pipeline designed to automate the extraction of key molecular descriptors for KNF (Kinetic and Non-covalent Feature) analysis. It integrates powerful tools like xTB (Extended Tight-Binding) and Multiwfn (Multifunctional Wavefunction Analyzer) to generate a unique 9-dimensional vector representation of molecular interactions.

Features

• Automated Workflow: From a single input structure (XYZ, SDF, MOL, etc.) to a final descriptor vector with minimal user intervention.
• Robust Geometry Handling: Automatically detects and converts input formats, perceives molecular fragments, and handles both single molecules and two-fragment complexes.
• xTB Integration: Performs semi-empirical quantum mechanical optimization (Geometry Optimization) and single-point energy calculations to derive electronic properties.
• Multiwfn Analysis: Automates Non-Covalent Interaction (NCI) analysis to capture weak interactions critical for binding affinity and stability.
• COSMO Solvation Model: Computes Sigma-Profile descriptors (SCDI) to account for solvation effects.
• Docker Support: Fully containerized environment for consistent execution across different platforms.

Installation

Prerequisites

• Python 3.8+
• xTB: Must be installed and accessible in your system PATH (xtb command).
• Multiwfn: Must be installed and accessible (Multiwfn command), or configured via settings.ini.
• OpenBabel: Required for file format conversions (obabel command).

Install from Source

Clone the repository and install using pip:

git clone https://github.com/yourusername/KNF-CORE.git
cd KNF-CORE
pip install .

Docker Usage

A Dockerfile is provided for a complete, pre-configured environment.

1. Build the image:

   docker build -t knf-core .
   

2. Run the container:

   docker run --rm -v $(pwd):/data knf-core /data/input.sdf
   

Usage

Command Line Interface (CLI)

The package provides a knf command for easy execution.

Basic Usage:

knf input_molecule.sdf

Options:

• --charge <int>: Net charge of the system (default: 0).
• --spin <int>: Spin multiplicity (default: 1).
• --force: Force recalculation of existing steps.
• --clean: Clean up previous run directories before starting.
• --debug: Enable verbose debug logging.

Example with Test File: The repository includes a test file example.mol (diethyl sulfate) for verification.

knf example.mol --charge 0 --force

General Example:

knf drug_molecule.sdf --charge 1 --force

Python API

You can also use KNF-CORE within your own Python scripts:

from knf_core.pipeline import KNFPipeline

# Initialize the pipeline
pipeline = KNFPipeline(
    input_file='test.sdf',
    charge=0,
    spin=1
)

# Run the analysis
pipeline.run()

Output

The pipeline generates a results folder containing:

• knf.json: The final 9D KNF vector and metadata.
• output.txt: Human-readable summary of the results.
• xtbopt.xyz: Optimized geometry.
• Intermediate files from xTB and Multiwfn.

License

This project is licensed under the MIT License - see the LICENSE file for details.