Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
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-based latent protein generation architectures and leverage the domain-domain interactions of monomeric computationally predicted protein structures to construct Teddymer, a new large-scale dataset of ...
| Authors | Kieran Didi, Zuobai Zhang, Guoqing Zhou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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