MMPCS: multi-view molecular pretraining based on consistency information and specific information.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41533695", "kind": "science-record", "revision": 1, "content_hash": "sha256:383f5ca1e9a56069e2611cdf9631dcf8d4d5ed47bb5cd5bc93b24a946c12ab61", "created_at": "2026-11-01T20:31:30Z", "updated_at": "2026-11-01T20:31:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MMPCS: multi-view molecular pretraining based on consistency information and specific information.", "doi": "10.1093/bioinformatics/btag028", "pmid": "41533695", "authors": ["Chenyang Xie", "Yingying Song", "Song He", "Xiaochen Bo", "Zhongnan Zhang"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "abstract": "MOTIVATION: The goal of molecular representation learning is to automate the extraction of molecular features, a critical task in cheminformatics and drug discovery. While pretraining models using multiple views like SMILES, two-dimensional graphs, and three-dimensional conformations have advanced the field, integrating them effectively to produce superior representations remains a challenge.\nRESULTS: bridge this gap, we propose a novel multi-view molecular pretraining method termed MMPCS, which explicitly factorizes representations into consistency and specific information. Our approach utilizes the Graph Isomorphism Network and the RoBERTa model to encode two-dimensional molecular topological graphs and SMILES sequences, respectively. Each resulting molecular embedding is decomposed into a shared consistency component and a view-specific remainder. An autoencoder then aligns the consistency information across views. The combined consistency and view-specific representations serve as input for downstream tasks, enabling precise and task-aware predictions. When benchmarked against 16 state-of-the-art molecular pretraining methods, MMPCS achieved the highest average performance across both classification and regression tasks for molecular property prediction. It also delivered outstanding results in predicting drug-target binding affinity and cancer drug response, demonstrating its robustness and broad applicability. Additionally, a case study on the SARS-CoV-2 Omicron variant highlights the potential of MMPCS in facilitating drug repurposing efforts.\nAVAILABILITY AND IMPLEMENTATION: The source code and datasets supporting this study are publicly available at GitHub (https://github.com/xmubiocode/MMPCS) and Zenodo (https://doi.org/10.5281/zenodo.18182748).", "claims": [{"id": "claim-data-access", "statement": "The code and data for reproducing the MMPCS framework are publicly available on GitHub (https://github.com/xmubiocode/MMPCS) and archived on Zenodo (https://doi.org/10.5281/zenodo.18182748).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41533695", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The code and data for reproducing the MMPCS framework are publicly available on GitHub (https://github.com/xmubiocode/MMPCS) and archived on Zenodo (https://doi.org/10.5281/zenodo.18182748)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Compared with 16 classical and state-of-the-art methods, MMPCS achieved top or second-best performance on 11 molecular property prediction (MPP) datasets, and attained the highest average results across both the classification and regression tasks.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41533695", "locator": "sections[1] \"1 Introduction\" paragraph 10", "quote": "Compared with 16 classical and state-of-the-art methods, MMPCS achieved top or second-best performance on 11 molecular property prediction (MPP) datasets, and attained the highest average results across both the classification and regression tasks."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Compared with 16 classical and state-of-the-art methods, MMPCS achieved top or second-best performance on 11 molecular property prediction (MPP) datasets, and attained the highest average results across both the classification and regression tasks.", "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:41533695"], "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:41533695", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btag028", "sha256": "sha256:e3a4283dc4c51af204decac30fcf2b8488694767b641a479738f1fcedab307c8", "bytes": 44955, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btag028", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12881828", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "operations"]}, "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 (not named), 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": ["The code and data for reproducing the MMPCS framework are publicly available on GitHub (https://github.com/xmubiocode/MMPCS) and archived on Zenodo (https://doi.org/10.5281/zenodo.18182748)."], "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:41533695"], "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": "2026-11-01T20:10:03Z", "independence": "single_reader"}, "evidence_check": {"method": null, "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11798, "completion_tokens": 1218, "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:41533695", "type": "paper", "label": "pmid:41533695"}, {"id": "source:pmid:41533695", "type": "manuscript", "label": "PMID 41533695 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41533695", "type": "paper", "label": "pmid:41533695"}, {"id": "source:pmid:41533695", "type": "manuscript", "label": "PMID 41533695 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41533695", "type": "extraction", "used": ["source:pmid:41533695"], "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": null, "parent_hash": null, "fork_of": null, "generation": 0}, "gaps": ["execution.not_run", "method", "operations", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"]}