{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Encodes molecular structures as MinHash fingerprints (MHFP6) for fast similarity search and nearest-neighbor queries using locality-sensitive hashing.","skillfed_tags":["cheminformatics","molecular-fingerprints","similarity-search"],"use_cases":["Virtual screening: find structurally similar drug candidates from a chemical library given a query molecule.","Analog recovery: identify known compounds similar to a novel structure in benchmarking or hit-to-lead workflows.","Approximate nearest-neighbor search: retrieve the k most similar molecules from a large dataset without exhaustive comparison.","Molecular clustering: group compounds by structural similarity using fingerprint distances.","Ligand-based design: compare test molecules against a reference set to assess novelty or redundancy."],"what_it_does":"MHFP is a molecular fingerprinting algorithm that converts chemical structures (provided as SMILES strings or RDKit molecule objects) into compact hash-based representations suitable for fast similarity searching. It applies the MinHash method to circular substructures extracted from molecules, encoding structural detail up to six bonds (MHFP6). The fingerprints are designed to work with locality-sensitive hashing (LSH) for approximate nearest-neighbor search, enabling rapid retrieval of similar molecules from large chemical databases.\n\nThe package provides two main interfaces: MHFPEncoder for generating fingerprints and computing pairwise distances, and LSHForestHelper for building indexed structures that support fast approximate queries. It also includes SECFP (SMILES Extended Connectivity Fingerprint), a folded variant. The package is a thin Python wrapper around the fingerprinting logic and depends on RDKit for molecular parsing and NumPy for numerical operations.","worth_installing":"Yes, if you need fast molecular similarity search and can accept an unmaintained package. The algorithm is well-published and the code is stable, but verify RDKit compatibility with your environment and confirm the license terms before use. Not recommended for new projects requiring ongoing support or frequent updates."},"id":"mhfp","links":{"html":"https://skillfed.io/packages/mhfp","md":"https://skillfed.io/packages/mhfp.md","pypi":"https://pypi.org/project/mhfp/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2023-02-16","license_spdx":null,"license_treatment":"unclear","name":"mhfp","python_support":"unspecified","summary":"Molecular MHFP fingerprints for cheminformatics applications"},"popularity":{"monthly_downloads":86738,"position":13842,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.6"}