Leveraging large language models for literature-driven prioritization of protein binding pockets.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40795239", "kind": "science-record", "revision": 2, "content_hash": "sha256:c6997f90ff5d32ecbe4bd8a86d46c0ec4fffe953e1dfedb158798f5086c5a0db", "created_at": "2026-11-01T20:30:53Z", "updated_at": "2026-11-01T21:35:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Leveraging large language models for literature-driven prioritization of protein binding pockets.", "doi": "10.1093/bioinformatics/btaf449", "pmid": "40795239", "authors": ["Roman Stratiichuk", "Mykola Melnychenko", "Ihor Koleiev", "Taras Voitsitskyi", "Vladyslav Husak", "Nazar Shevchuk", "Zakhar Ostrovsky", "Volodymyr Bdzhola", "Semen Yesylevskyy", "Serhii Starosyla", "Alan Nafiiev"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Accurately identifying and prioritizing protein binding pockets is a foundational element of small-molecule drug discovery. Defining these known pockets currently relies on a laborious manual process of extracting key residue data from selected publications, reconciling inconsistent terminology, and independently computing volumetric representations. This manual curation to ensure biological relevance is time-consuming, error-prone, and represents a major bottleneck for efficient, high-throughput drug discovery.\nRESULTS: We present a novel approach for the identification and prioritization of protein binding pockets for small molecules by combining geometric pocket detection with large language models (LLMs). Our method leverages Fpocket to generate candidate pockets, which are then validated against published experimental data extracted from research articles using LLM with a series of prompts fine-tuned to identify and extract residue-level information associated with experimentally confirmed binding sites. We developed a curated benchmark dataset of diverse proteins and associated literature to train and evaluate the LLM's performance in paper relevance assessment and pocket extraction.\nAVAILABILITY AND IMPLEMENTATION: The developed benchmark dataset and methodology are freely available at the GitHub repository (https://github.com/receptor-ai/LLM-benchmark-dataset) and Zenodo (DOI: 10.5281/zenodo.15798647).", "claims": [{"id": "claim-method", "statement": "To evaluate performance at the amino acid level within the pockets, we track the F1 score, recall, and precision metrics for the lists of amino acids in annotated and extracted pockets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40795239", "locator": "sections[5] \"2.3 Pocket extraction accuracy metrics\" paragraph 9", "quote": "To evaluate performance at the amino acid level within the pockets, we track the F1 score, recall, and precision metrics for the lists of amino acids in annotated and extracted pockets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available at the GitHub repository (https://github.com/receptor-ai/LLM-benchmark-dataset) and Zenodo (DOI: https://doi.org/10.5281/zenodo.15798647).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40795239", "locator": "sections[18] \"Data availability\" paragraph 2", "quote": "The data underlying this article are available at the GitHub repository (https://github.com/receptor-ai/LLM-benchmark-dataset) and Zenodo (DOI: https://doi.org/10.5281/zenodo.15798647)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40795239"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40795239", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf449", "sha256": "sha256:7aa3f3fbaf6f08bfb1e23a20d447d0dd2008abcfa3c5372cea0138fc7d1ebe4c", "bytes": 45301, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf449", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12371332", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Pocket Number Accuracy, Pocket Recall, Pocket Specificity, and amino acid-level F1 score, recall, and precision", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To evaluate performance at the amino acid level within the pockets, we track the F1 score, recall, and precision metrics for the lists of amino acids in annotated and extracted pockets."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (Pocket Number Accuracy, Pocket Recall, Pocket Specificity, and amino acid-level F1 score, recall, and precision), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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