{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"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.","skillfed_tags":["computational-chemistry","molecular-dynamics","drug-discovery"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"Yes\u2014if 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."},"id":"prolif","links":{"html":"https://skillfed.io/packages/prolif","md":"https://skillfed.io/packages/prolif.md","pypi":"https://pypi.org/project/prolif/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-27","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"prolif","python_support":"supports_current","summary":"Interaction Fingerprints for protein-ligand complexes and more"},"popularity":{"monthly_downloads":77299,"position":14538,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.1"}