Jeremy Wohlwend, Gabriele Corso, Saro Passaro, Mateo Reveiz, Ken Leidal, Wojtek Swiderski, Tally Portnoi, Itamar Chinn, Jacob Silterra, Tommi Jaakkola, Regina Barzilay
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引用次数: 0
摘要
了解生物分子相互作用是推动药物发现和蛋白质设计等领域发展的基础。本文介绍了开源深度学习模型 Boltz-1,该模型在模型架构、速度优化和数据处理方面进行了创新,在预测生物分子复合物的三维结构方面达到了 A lpha F old 3 级精度。Boltz-1 在一系列不同的基准测试中表现出了与最先进的商业模型不相上下的性能,为结构生物学领域的商业化工具树立了新的标杆。通过在麻省理工学院开放许可下发布训练和推理代码、模型权重、数据集和基准,我们旨在促进全球合作、加速发现,并为推进生物分子建模提供一个强大的平台。
Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this paper, we introduce Boltz-1, an open-source deep learning model incorporating innovations in model architecture, speed optimization, and data processing achieving AlphaFold3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates a performance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training and inference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.