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Ligand potency prediction using generated poses

This paper-specific evaluation tests Ligand potency prediction using generated poses using SARS-CoV-2 Mpro ligands.

2 evaluations · 2 metric rows

At a glance

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. This does not change the review status of its results.

Data, procedure and scoring
PropertyDescription and evidence
Record typePaper-specific task; protocol incompletely extractedA 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; Table 3, DiffDock row, Pearson’s R column
InputsSARS-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, Boltz-2 row, Pearson’s R column; Table 3, DiffDock row, Pearson’s R column
AssessmentPearson RA 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; Table 3, DiffDock row, Pearson’s R column
Recorded split or evaluation settingUnextractedA 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; Table 3, DiffDock row, Pearson’s R column
DatasetsNot extracted or verified for this record.
OrganismsNot extracted or verified for this record.
AssaysNot extracted or verified for this record.
AdaptationNot extracted or verified for this record.
BaselinesNot extracted or verified for this record.

How it works

Reported evaluation outline

Outline of the existing paper extraction. Split membership, fitting details and scorer implementation remain incompletely reviewed.

Reported evaluation outlineSARS-CoV-2 Mpro ligands. Then: Recorded fitting or scoring procedure. Then: Assess Pearson RSARS-CoV-2 Mpro ligandsRecorded fitting or scoringprocedureAssess Pearson R
Read the diagram as text
  1. SARS-CoV-2 Mpro ligands
  2. Recorded fitting or scoring procedure
  3. Assess Pearson R
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; Table 3, DiffDock row, Pearson’s R column

Evaluation context

The existing paper extraction describes: Potency prediction using Boltz-2 ligand-pose generation protocol; see paper scoring pipeline.; Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score. This description is retained with the exact evaluation records; it is not a new protocol reconstruction.

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; Table 3, DiffDock row, Pearson’s R column

Tested models and results

Release 2026-09-16-d74d282221a9 · 2 evaluations · 2 metric rows. Different protocols are not a single leaderboard.

Results grouped by the exact reported evaluation
Metric and findingCoverage and uncertaintyEvidence
Boltz-2: Ligand potency prediction using generated poses

Potency prediction using Boltz-2 ligand-pose generation protocol; see paper scoring pipeline.

Independent external evaluation · Evaluation metadata: needs review

0.800 Pearson R

Unit: unitless · Direction: unknown

Uncertainty: ± 0.027

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, Boltz-2 row, Pearson’s R column

Source checking is not independent reproduction.

DiffDock: Ligand potency prediction using generated poses

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

Profile review details

Catalogue extraction inspected; protocol claims remain limited to the cited evidence. Missing details are not presumed absent from the original paper.

Stable record: reported-task-d5f897ab0f6f67

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: needs review

Download this release
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.
missing metadata
protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
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