Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

• URL: http://arxiv.org/abs/2603.27950v1
• Date: Mon, 30 Mar 2026 01:54:03 GMT
• Title: Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
• Abstract summary: We propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms.<n>Proteina-Complexa sets a new state of the art in computational binder design benchmarks.<n>We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks.
• Score: 64.59043351161004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-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 synthetic binder-target pairs for pretraining. Combined with high-quality experimental multimers, this enables training a strong base model. We then perform inference-time optimization with this generative prior, unifying the strengths of previously distinct generative and hallucination methods. Proteina-Complexa sets a new state of the art in computational binder design benchmarks: it delivers markedly higher in-silico success rates than existing generative approaches, and our novel test-time optimization strategies greatly outperform previous hallucination methods under normalized compute budgets. We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks, again surpassing prior methods. Code, models and new data will be publicly released.

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