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

TL;DR

Proteina-Complexa introduces a unified atomistic protein binder generation method that combines generative modeling and test-time optimization, achieving state-of-the-art results in computational binder design.

Contribution

A novel unified framework that integrates generative pretraining and test-time optimization for atomistic protein binder design, surpassing existing methods.

Findings

Higher in-silico success rates than previous approaches

Test-time optimization strategies outperform hallucination methods

Effective interface hydrogen bond and fold class-guided binder generation

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.