MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37432499", "kind": "science-record", "revision": 2, "content_hash": "sha256:b45d9803f6063d60f689bc17d53a1f3d5faa07daf4d2f2a47fead6886bf5b66e", "created_at": "2026-11-01T19:02:11Z", "updated_at": "2026-11-01T21:31:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.", "doi": "10.1093/bioinformatics/btad432", "pmid": "37432499", "authors": ["Luigi Ferraro", "Giovanni Scala", "Luigi Cerulo", "Emanuele Carosati", "Michele Ceccarelli"], "journal": "Bioinformatics (Oxford, England)", "year": 2023, "abstract": "MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher.\nRESULTS: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms.\nAVAILABILITY AND IMPLEMENTATION: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.", "claims": [{"id": "claim-method", "statement": "The accuracy of the prediction was evaluated by measuring the Spearman and Pearson correlation between the predicted AUC values and the actual ones, averaged over 5-fold cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37432499", "locator": "sections[11] \"3.2 Performance and comparison with DrugCell\" paragraph 2", "quote": "The accuracy of the prediction was evaluated by measuring the Spearman and Pearson correlation between the predicted AUC values and the actual ones, averaged over 5-fold cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37432499", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MOViDA outperformed DrugCell in drug-sensitivity prediction, with a lower macroaverage MSE of 0.025 versus 0.035 and substantially lower error in the sparsely populated high-sensitivity classes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37432499", "locator": "sections[11] \"3.2 Performance and comparison with DrugCell\" paragraph 2", "quote": "When we used the macroaverage MSE (MMSE), which accounts for the imbalance of the classes, MOViDA had a lower error (0.025 vs. 0.035) after the cross-validation."}, {"asset": "source:pmid:37432499", "locator": "sections[11] \"3.2 Performance and comparison with DrugCell\" paragraph 2", "quote": "Notably, MOViDA exhibited higher accuracy in these classes compared to DrugCell, with a significantly lower error rate (0.032 vs. 0.060)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "MOViDA outperformed DrugCell in drug-sensitivity prediction, with a lower macroaverage MSE of 0.025 versus 0.035 and substantially lower error in the sparsely populated high-sensitivity classes.", "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:37432499"], "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:37432499", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btad432", "sha256": "sha256:e67a8118a6822e32809287c40496dbdb4a767a10ef9e2ef8eb94faddeddb92a3", "bytes": 46689, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btad432", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10375315", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The accuracy of the prediction was evaluated by measuring the Spearman and Pearson correlation between the predicted AUC values and the actual ones, averaged over 5-fold cross-validation."}}], "targets": [], "gaps": []}, "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": "R1", "expressible": true, "required_operations": ["pearson_r"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pearson."}, "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": ["Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["pearson_r"], "assets": ["source:pmid:37432499"], "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": "2026-11-01T18:46:58Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12508, "completion_tokens": 2424, "cached_tokens": 256}}, "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:37432499", "type": "paper", "label": "pmid:37432499"}, {"id": "source:pmid:37432499", "type": "manuscript", "label": "PMID 37432499 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37432499", "type": "paper", "label": "pmid:37432499"}, {"id": "source:pmid:37432499", "type": "manuscript", "label": "PMID 37432499 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37432499", "type": "extraction", "used": ["source:pmid:37432499"], "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:37432499", "parent_hash": "sha256:2920e7f543dcec2f0d4162930a65ecc9770cd4ac29723033ff69f21ee50f5140", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "correlation_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pearson_r"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The accuracy of the prediction was evaluated by measuring the Spearman and Pearson correlation between the predicted AUC values and the actual ones, averaged over 5-fold cross-validation."], "order_basis": "analysis_only", "id": "assay:1"}]}