KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40824044", "kind": "science-record", "revision": 2, "content_hash": "sha256:b8bd2e6265c4df62b41cc986533aaa5f8764e50cb35ccb566793ed42c340b1a4", "created_at": "2026-11-01T20:30:55Z", "updated_at": "2026-11-01T21:35:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.", "doi": "10.1093/bioinformatics/btaf457", "pmid": "40824044", "authors": ["Meiling Liu", "Shujuan Su", "Guohua Wang", "Shan Huang"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Predicting microbe-drug associations (MDgAs) is critical for understanding the role of microbes in drug metabolism, exploring their interactions with host physiology, and advancing personalized therapy. However, traditional methods face challenges in dealing with data sparsity, information imbalance, and the extraction of complex biological knowledge, which limit the accurate prediction of microbe-drug associations. Therefore, developing a computational model that can efficiently integrate multi-source data and address the challenges of data sparsity and information imbalance is essential.\nRESULTS: The paper proposes a model that integrates knowledge graphs and contrastive learning. By constructing both local and non-local association graphs, the model effectively captures the complex relationships between microbes and drugs. We preprocess and model the embedding representations of microbes and drugs, and design a multi-level interactive contrastive learning mechanism to optimize the information flow both within and outside the graph. Experimental results show that our model significantly outperforms existing methods in metrics such as AUC and AUPR, providing an efficient solution for predicting microbe-drug associations.\nAVAILABILITY AND IMPLEMENTATION: The source code is available at: https://github.com/SJshujuan/KGCLMDA. The code used in this study is also available on Zenodo: https://doi.org/10.5281/zenodo.16754402.", "claims": [{"id": "claim-method", "statement": "To evaluate the effectiveness of the microbe–drug potential association prediction model, we implemented 5-fold cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40824044", "locator": "sections[9] \"2.5 The final association prediction\" paragraph 4", "quote": "To evaluate the effectiveness of the microbe–drug potential association prediction model, we implemented 5-fold cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We collected known microbe–drug associations from the MDAD and aBiofilm databases, integrating 2470 clinically or experimentally validated interactions between 1373 drugs and 173 microbes, and 2884 verified associations involving 140 microbes and 1720 drugs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40824044", "locator": "sections[2] \"2 Materials and methods\" paragraph 2", "quote": "We collected known microbe–drug associations from the MDAD and aBiofilm databases, integrating 2470 clinically or experimentally validated interactions between 1373 drugs and 173 microbes, and 2884 verified associations involving 140 microbes and 1720 drugs."}], "state": "complete"}, {"id": "claim-target-result", "statement": "KGCLMDA significantly outperformed the baseline methods, improving AUC by 0.0305 on MDAD, 0.0269 on aBiofilm, and 0.0257 on DrugVirus, and improving AUPR by 0.073 on MDAD, 0.0370 on aBiofilm, and 0.0502 on DrugVirus.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40824044", "locator": "sections[11] \"3.1 Baseline comparison\" paragraph 4", "quote": "The results in Tables 1–3 show that KGCLMDA outperforms the other methods in AUC and AUPR, improving AUC by 0.0305 (MDAD), 0.0269 (aBiofilm), and 0.0257(DrugVirus), and AUPR by 0.073 (MDAD), 0.0370 (aBiofilm), and 0.0502 (DrugVirus)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "KGCLMDA significantly outperformed the baseline methods, improving AUC by 0.0305 on MDAD, 0.0269 on aBiofilm, and 0.0257 on DrugVirus, and improving AUPR by 0.073 on MDAD, 0.0370 on aBiofilm, and 0.0502 on DrugVirus.", "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:40824044"], "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:40824044", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf457", "sha256": "sha256:e45e9a95d3823e39a78fa13bbc1d33f38c901dfb75fba52305ef01120e1feab4", "bytes": 31846, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf457", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12448813", "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": "5-fold cross-validation evaluated using AUC and AUPR", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To evaluate the effectiveness of the microbe–drug potential association prediction model, we implemented 5-fold cross-validation."}}], "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 (5-fold cross-validation evaluated using AUC and AUPR), 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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["We collected known microbe–drug associations from the MDAD and aBiofilm databases, integrating 2470 clinically or experimentally validated interactions between 1373 drugs and 173 microbes, and 2884 verified associations involving 140 microbes and 1720 drugs."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40824044"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:59:49Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 8792, "completion_tokens": 2130, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40824044", "type": "paper", "label": "pmid:40824044"}, {"id": "source:pmid:40824044", "type": "manuscript", "label": "PMID 40824044 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40824044", "type": "paper", "label": "pmid:40824044"}, {"id": "source:pmid:40824044", "type": "manuscript", "label": "PMID 40824044 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40824044", "type": "extraction", "used": ["source:pmid:40824044"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40824044", "parent_hash": "sha256:27a0e2b4b44666e64d8a257bcd02650402d326f2d674f048095d2b582a9dc010", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"]}