De novo protein ligand design including protein flexibility and conformational adaptation.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41570088", "kind": "science-record", "revision": 2, "content_hash": "sha256:1df8b0ad5165814e52fb1001a773d6ac336d37ecd82d0aa7e6ed9e828cf33ffe", "created_at": "2026-11-01T20:31:32Z", "updated_at": "2026-11-01T21:36:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "De novo protein ligand design including protein flexibility and conformational adaptation.", "doi": "10.1093/bioinformatics/btag027", "pmid": "41570088", "authors": ["Jakob Agamia", "Martin Zacharias"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "abstract": "MOTIVATION: The rational design of chemical compounds that bind to a desired protein target molecule is a major goal of drug discovery. Most current molecular docking but also fragment-based build-up or machine-learning based generative drug design approaches employ a rigid protein target structure.\nRESULTS: Based on recent progress in predicting protein structures and complexes with chemical compounds we have designed an approach, AI-MCLig, to optimize a chemical compound bound to a fully flexible and conformationally adaptable protein binding region. During a Monte-Carlo (MC) type simulation to randomly change a chemical compound the target protein-compound complex is completely rebuilt at every MC step using the Chai-1 protein structure prediction program. Besides compound flexibility it allows the protein to adapt to the chemically changing compound. MC-protocols based on atom/bond type changes or based on combining larger chemical fragments have been tested. Simulations on three test targets resulted in potential ligands that show very good binding scores comparable to experimentally known binders using several different scoring schemes. The MC-based compound design approach is complementary to existing approaches and could help for the rapid design of putative binders including induced fit of the protein target.\nAVAILABILITY AND IMPLEMENTATION: Datasets, examples and source code are available on our public GitHub repository https:/github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140.", "claims": [{"id": "claim-method", "statement": "To this end, MC simulations were performed with the regular score being replaced with a dice similarity score, derived with rdkit (Landrum 2023).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41570088", "locator": "sections[8] \"MC-based recovery of target ligands from simple starting compounds\" paragraph 2", "quote": "To this end, MC simulations were performed with the regular score being replaced with a dice similarity score, derived with rdkit (Landrum 2023)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets, examples, and source code are available on our public GitHub repository https://github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41570088", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Datasets, examples, and source code are available on our public GitHub repository https://github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "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:41570088"], "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:41570088", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btag027", "sha256": "sha256:15906eaeb3d6df16f7309d92f81b77cbee3ddea05fa6fd3e6bb4c58a28c6ab34", "bytes": 37872, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btag027", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12944823", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Dice similarity score", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To this end, MC simulations were performed with the regular score being replaced with a dice similarity score, derived with rdkit (Landrum 2023)."}}], "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 (Dice similarity score), 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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