---
id: prolif
version: "2.2.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# prolif — Interaction Fingerprints for protein-ligand complexes and more

License: permissive · Maintenance: active · Downloads: 77.3K/mo

## What it is and what it does

ProLIF is a Python library for extracting and encoding protein-ligand (and nucleic acid) interactions from molecular dynamics simulations, docking results, and crystal structures into fingerprint representations. It sits at the intersection of structural biology and cheminformatics, converting 3D molecular complexes into discrete, analyzable interaction patterns that can be used for binding analysis, virtual screening validation, and machine learning workflows.

The package wraps mdanalysis for trajectory parsing and uses gemmi for structural geometry calculations, making it a thin but specialized layer atop standard computational chemistry tools. Its main use is converting raw molecular dynamics or docking output into a tabular format where each row represents a frame or pose and each column represents a detected interaction type, enabling downstream statistical analysis or model training.

Use it for:

- Validate docking poses by comparing their interaction fingerprints to known binders or experimental structures.
- Extract interaction patterns from MD trajectories to identify stable binding modes and transient contacts.
- Generate training data for machine learning models predicting binding affinity or selectivity from interaction patterns.
- Analyze ligand-DNA/RNA interactions in molecular dynamics simulations for nucleic acid drug discovery.
- Benchmark virtual screening campaigns by fingerprinting hits and comparing to reference ligands.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

ProLIF generates interaction fingerprints from molecular complexes in MD trajectories, docking simulations, and experimental structures, encoding ligand-protein/DNA/RNA contacts in a machine-readable format.

Yes—if you work with molecular dynamics, docking, or structural biology and need to convert 3D complexes into interaction data. Active maintenance, permissive license, low install friction, no known vulnerabilities, and a focused scope make it a reliable choice. Not worth installing if you only need basic distance-based contact analysis or lack mdanalysis in your workflow.

## Install

pip install prolif
uv add prolif
poetry add prolif

## Installing prolif

Before you install: Low friction: pure Python wheel with no compiled dependencies beyond its ten runtime packages (pandas, numpy, scipy, mdanalysis, networkx, tqdm, multiprocess, dill, psutil, gemmi). Active maintenance—last commit 2026-08-01, release 18 days old.

License in practice: Apache-2.0 permissive license allows commercial and private use, modification, and distribution with minimal restrictions; suitable for most research and commercial workflows.

Quickstart:

pip install prolif

import prolif
from prolif import Fingerprint

# Load trajectory and generate fingerprints
fp = Fingerprint()
results = fp.run(trajectory, ligand_selection, protein_selection)

Requires Python >=3.10 and mdanalysis for trajectory I/O; gemmi for structural analysis.

Verify before relying:

- Specific interaction types detected (hydrogen bonds, hydrophobic contacts, etc.) not enumerated in the excerpt.
- Performance characteristics on large trajectories or complex systems not quantified.
- Whether fingerprints are compatible with standard ML libraries or require preprocessing.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 77.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

protein ligand interaction fingerprints, molecular dynamics interaction analysis, ligand binding fingerprints, drug-protein interaction profiling, molecular complex interaction encoding, docking simulation interaction extraction, biomolecular interaction fingerprinting, computational-chemistry, molecular-dynamics, drug-discovery

[View on SkillFed](https://skillfed.io/packages/prolif) · [View on PyPI](https://pypi.org/project/prolif/)