Wohlwend Jeremy, Corso Gabriele, Passaro Saro, Reveiz Mateo, Leidal Ken, Swiderski Wojtek, Portnoi Tally, Chinn Itamar, Silterra Jacob, Jaakkola Tommi, Barzilay Regina
MIT CSAIL.
MIT Jameel Clinic.
bioRxiv. 2024 Dec 27:2024.11.19.624167. doi: 10.1101/2024.11.19.624167.
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.
理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。在本文中，我们介绍了Boltz-1，这是一个开源深度学习模型，它在模型架构、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时达到了AlphaFold3级别的准确性。在一系列不同的基准测试中，Boltz-1展示了与最先进的商业模型相当的性能，为结构生物学中商业可用工具设定了新的基准。通过在麻省理工学院开源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。
