prolif
Interaction Fingerprints for protein-ligand complexes and more
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and mdanalysis for trajectory I/O; gemmi for structural analysis.
- 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 · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use, modification, and distribution with minimal restrictions; suitable for most research and commercial workflows.
last release 2026-07-27 (18 days) · last repo commit 2026-08-01 · 533 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 77,299 downloads/mo, #14,538 on PyPI
Alternatives
Verify before relying
pip install prolif
import prolif
from prolif import Fingerprint
# Load trajectory and generate fingerprints
fp = Fingerprint()
results = fp.run(trajectory, ligand_selection, protein_selection)- 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.
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 on this page. Verify before relying on it.
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
prolif on PyPI
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.
Requires Python >=3.10 and mdanalysis for trajectory I/O; gemmi for structural analysis.
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)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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagespandasnumpyscipymdanalysisnetworkxtqdmmultiprocessdillpsutilgemmi |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 77,299 / month, #14,538 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: Chemistry |
Evidence: prolif-2.2.1-py3-none-any.whl
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See also pinder · propka · pdb2pqr · aimsim-core · mdtraj · padelpy · py3Dmol · logomaker · ViennaRNA · fingerprints