rewire.it
Paper-reported evidence

A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases

2025 · Peer-reviewed · version of record

Paper-reported results. The values below come from this source, not an independent rewire.it run.

Open primary paper →DOI: 10.1021/acs.jcim.5c02481

Primary full text via Europe PMC XML; venue: Journal of Chemical Information and Modeling; PMC ID: PMC12801289.

2 verified numerical rows · source checked 2026-09-15

Paper-reportedIndependent paper evaluation

Boltz-2

Ligand potency prediction using generated poses

Reported score
0.800± 0.027
Metric
Pearson R
Dataset / split
SARS-CoV-2 Mpro ligands

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

Table 3, Boltz-2 row, Pearson’s R columnVerify at source →
Paper-reportedIndependent paper evaluation

DiffDock

Ligand potency prediction using generated poses

Reported score
0.695± 0.037
Metric
Pearson R
Dataset / split
SARS-CoV-2 Mpro ligands

Potency prediction using DiffDock ligand-pose generation plus paper scoring pipeline; not a native DiffDock affinity score.

Table 3, DiffDock row, Pearson’s R columnVerify at source →