李翛然
(2025-06-28 14:03):
#paper Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction http://jeremywohlwend.com/assets/boltz2.pdf mit最新发布的一个全原子预测模型。 我个人觉得效果并没有显著超越af3 和protenix 但是新闻吹的很多。大家可以用一用,测试一下
bioRxiv,
2025-6-18.
DOI: 10.1101/2025.06.14.659707
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction
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Abstract:
AbstractAccurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule–protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000×more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code1under a permissive open license, providing a robust and extensible foundation for both academic and industrial research.
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