Proteina-Complexa unifies generative flow-based modeling with structure-based 'hallucination' to set a new SOTA in atomistic protein binder design.
March 31, 2026
Original Paper
Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
arXiv · 2603.27950
The Takeaway
By combining a massive new synthetic dataset (Teddymer) with test-time optimization, it significantly outperforms previous methods in drug discovery tasks. It democratizes high-quality binder design for small molecules and enzymes, providing a unified framework for atomistic interaction modeling.
From the abstract
Protein interaction modeling is central to protein design, which has been transformed by machine learning with applications in drug discovery and beyond. In this landscape, structure-based de novo binder design is cast as either conditional generative modeling or sequence optimization via structure predictors ("hallucination"). We argue that this is a false dichotomy and propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms. We extend recent flow-bas