Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
model · method
DiffDock
DiffDock is the method recorded for Ligand potency prediction using generated poses. This page preserves the configuration reported by A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.
At a glance
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. This does not change the review status of its results.
| Property | Description and evidence |
|---|---|
| Recorded dataset | SARS-CoV-2 Mpro ligandsA 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 |
| Model type | Not extracted or verified for this record. |
| Known versions | Not extracted or verified for this record. |
| Training data | Not extracted or verified for this record. |
| Context limits | Not extracted or verified for this record. |
| Access | Not extracted or verified for this record. |
| Code licence | Not extracted or verified for this record. |
| Weights licence | Not extracted or verified for this record. |
How it works
Recorded evaluation
The imported evaluation describes this procedure: Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score.
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 columnBenchmarks and results
Release 2026-09-16-d74d282221a9 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| DiffDock: Ligand potency prediction using generated poses Model: DiffDock · Benchmark: Ligand potency prediction using generated poses · Dataset: SARS-CoV-2 Mpro ligands Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score. Independent external evaluation · Evaluation metadata: needs review | ||
| 0.695 Pearson R Unit: unitless · Direction: unknown | Uncertainty: ± 0.037 Scored: Not reported · Eligible: Not reported | source checkedA 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. |
Strengths and limitations
Limitations and conditions
- This is an evidence-limited profile. The recorded evaluation context does not establish performance on other datasets or configurations.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
Profile review details
Reviewed the existing release record, its source pointer and linked evaluation context. This is not a fresh full-text architecture review or independent reproduction; numerical review status is unchanged.
Stable record: reported-model-415ee22f46526cSources and history
Release 2026-09-16-d74d282221a9 · Record review: needs review
- 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-model-415ee22f46526c
- areas
- molecular-interactions
- entity level
- method
- version
- Not reported
- reported name
- DiffDock
- missing metadata
- version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract