Datasets
ASAP Discovery Antiviral Potency Prediction Challenge 2025 data hosted on Polaris.
Task
Ligand potency prediction using generated poses
Antiviral ligand-potency prediction uses a challenge dataset distinct from the paper’s pose-prediction challenge.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results tableOverview
Metrics
MAE and RMSE for predicted compound potency; these regression errors are separate from pose agreement.
Allowed inputs
Ligand representations and generated protein–ligand poses.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results tableEvaluation procedure diagram
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results tablelimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
2 evaluations · 2 results. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: Boltz-2 | Task: Ligand potency prediction using generated poses Dataset: SARS-CoV-2 Mpro ligands | 0.8 Pearson R unitless · unknown Uncertainty: ± 0.027 Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBoltz-2: Ligand potency prediction using generated poses Potency prediction using Boltz-2 ligand-pose generation protocol; see paper scoring pipeline. Aggregation: Not reported A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Table 3, Boltz-2 row, Pearson’s R column |
| Configuration: DiffDock | Task: Ligand potency prediction using generated poses Dataset: SARS-CoV-2 Mpro ligands | 0.695 Pearson R unitless · unknown Uncertainty: ± 0.037 Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDiffDock: Ligand potency prediction using generated poses Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score. Aggregation: Not reported A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Table 3, DiffDock row, Pearson’s R column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Methods and evaluation design
Procedure, tasks and evaluated configurations
How it works
Evaluation methodology
ASAP Discovery Antiviral Potency Prediction Challenge 2025 data hosted on Polaris. The challenge supplies training and test compounds for two protein targets. MAE and RMSE for predicted compound potency; these regression errors are separate from pose agreement. Training/validation bootstrap replicates assess model stability. They do not by themselves establish uncertainty on the untouched challenge test set.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results tableRecorded evaluations
Each evaluation records what was tested and under which conditions.
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Limitations and conditions
- Pose generation and downstream potency fitting jointly affect the comparison. Random training/validation resampling does not demonstrate scaffold-disjoint generalization.
Sources
A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table
Profile review details
Relevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged.
Stable record: reported-task-d5f897ab0f6f67Specifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Datasets | ASAP Discovery Antiviral Potency Prediction Challenge 2025 data hosted on Polaris.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Splits | The challenge supplies training and test compounds for two protein targets.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Metrics | MAE and RMSE for predicted compound potency; these regression errors are separate from pose agreement.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Baselines | The potency study compares a common LRIP-SF predictor supplied with poses from Glide, AutoDock Vina, FlexS, AlphaFold3, Boltz-2, DiffDock and Gnina variants. Thus the comparison varies pose provenance as well as evaluating the downstream potency regressor; some models were added after the challenge.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Introduction: challenge approaches; Methods: pose generation and LRIP-SF training; potency comparison results |
| Leakage controls | The regression models use repeated random training/validation splits of the challenge training compounds. The inspected training and comparison methods do not specify scaffold-disjoint folds or an audit of challenge compounds/targets against each pose model’s pretraining data. · Not reported in inspected sourcesSourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: LRIP-SF model training; challenge train/test description; Discussion of postchallenge comparisons |
| Uncertainty | Training/validation bootstrap replicates assess model stability. They do not by themselves establish uncertainty on the untouched challenge test set.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Entity type | Paper-specific computational evaluation protocol.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Organisms | Viral protease targets in the antiviral potency challenge.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Assays | Measured compound potency.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Allowed inputs | Ligand representations and generated protein–ligand poses.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
| Adaptation | Supervised potency prediction using challenge training compounds and a separate test collection.SourcesA Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Papers and result coverage
Last literature check: 2026-09-17. Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
| Paper or primary resource | Version | Reference |
|---|---|---|
| A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases | version of record | Read source |
Historical gaps recorded on 2026-09-17
The catalogue now holds 2 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.
- complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.
- exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.
Search and extraction details
primary comparison tables located
Searches
- A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases 10.1021/acs.jcim.5c02481
Evidence locations
- 1; XML table tbl1
- 2; XML table tbl2
- 3; XML table tbl3
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
17 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets ASAP Discovery Antiviral Potency Prediction Challenge 2025 data hosted on Polaris. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits The challenge supplies training and test compounds for two protein targets. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised potency prediction using challenge training compounds and a separate test collection. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics MAE and RMSE for predicted compound potency; these regression errors are separate from pose agreement. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines The potency study compares a common LRIP-SF predictor supplied with poses from Glide, AutoDock Vina, FlexS, AlphaFold3, Boltz-2, DiffDock and Gnina variants. Thus the comparison varies pose provenance as well as evaluating the downstream potency regressor; some models were added after the challenge. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Introduction: challenge approaches; Methods: pose generation and LRIP-SF training; potency comparison results Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The regression models use repeated random training/validation splits of the challenge training compounds. The inspected training and comparison methods do not specify scaffold-disjoint folds or an audit of challenge compounds/targets against each pose model’s pretraining data. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: LRIP-SF model training; challenge train/test description; Discussion of postchallenge comparisons Version: version of record | unreported automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty Training/validation bootstrap replicates assess model stability. They do not by themselves establish uncertainty on the untouched challenge test set. Individual claims | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
2 source records and release history
- A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Original source · version of record
- A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-d5f897ab0f6f67
- areas
- molecular-interactions
- tasks
- Ligand potency prediction using generated poses
- entity level
- task
- version
- Not reported
- task
- Ligand potency prediction using generated poses
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_tables_located; primary sources: evidence-expansion-mpro-pose-affinity-2025-c356a1c6; inspected locators: 1; XML table tbl1; 2; XML table tbl2; 3; XML table tbl3; searched queries: A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases 10.1021/acs.jcim.5c02481; gaps: complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.; exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.; claim scope: Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
- historical missing metadata
- protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- benchmark
- entity classification
- review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: mpro-pose-affinity-2025; source locator: Methods: Antiviral Potency Prediction Challenge data preparation; cached text lines 41–43; task metric definitions and corresponding results table; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.