ipsae

What problem does it solve?

ipSAE-based ranking provides an objective, scalable method to prioritize protein-protein interaction designs for experimental validation by scoring predicted interfaces.

Core Features & Use Cases

• Score designs across AlphaFold2, AlphaFold3, and Boltz predictions to rank binders by interface quality.
• Compare predictors (ipSAE vs ipTM / iPAE) to select top candidates.
• Batch ranking and reporting with chain-pair and residue-level scores for large design sets.
• Use Case: Prioritize designs for experimental testing to improve hit rates and reduce bench time.

Quick Start

Run ipsae.py with your predictor outputs to generate scores for ranking designs, e.g., python ipsae.py scores_rank.json design_0.pdb 10 10