AIMNet2(Score) ranks protein–ligand binding from one structure and no affinity labels
olexandr · x · 2026-07-24
AIMNet2(Score) scores protein–ligand binding without affinity labels
A new ChemRxiv preprint introduces AIMNet2(Score), a label-free method for ranking protein–ligand complexes from a single bound structure.
- The score is composed of three physics-based terms: protein–ligand interaction energy, ligand desolvation, and conformational strain.
- It relies on QM-level physics from AIMNet2 machine-learned interatomic potentials rather than experimental affinity data.
- The authors frame it as a direct alternative to label-dependent scoring methods for protein–ligand binding prediction.
Related event: AIMNet2(Score) Enables Label-Free Protein-Ligand Binding Scoring(3 posts)→
More from Research
- PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
- New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11
- LoMa Paper Ships REALLY HardPairs Dataset, Accepted at ECCV 2026 — ducha_aiki · 2026-09-11
- Johns Hopkins Launches Full-Stack Hands-on Robot Learning Class with SO-101 Arm Kits — _krishna_murthy · 2026-09-11
- SyncWorld: In-Context Robot World Model Simulates Unseen Views and Embodiments Zero-Shot — ChongZzZhang · 2026-09-11
- A 3D Pose Dataset for Dogs Released — ducha_aiki · 2026-09-11