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• Proteina-Complexa sets a new state-of-the-art in computational binder design benchmarks, delivering higher in-silico success rates than existing generative approaches.result 90%
• The novel test-time optimization strategies of Proteina-Complexa greatly outperform previous hallucination methods under normalized compute budgets.method 85%
• Proteina-Complexa surpasses prior methods in small molecule target and enzyme design tasks without requiring sequence re-design.result 80%
• The Teddymer dataset provides a valuable large-scale resource of synthetic binder-target pairs for pretraining protein design models.technical 75%
• Proteina-Complexa achieves faster sampling speed than baseline methods while maintaining comparable novelty.result 70%
• The method enables diverse binder generation via fold class guidance, addressing limitations of previous generators that produced primarily alpha helical structures.method 65%
• Initializing existing hallucination methods from Proteina-Complexa samples accelerates search on easy targets but not on hard targets.limitation 60%

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