Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks. However, certain important tasks are less amenable to these technologies, benefiting from innovations to traditional inference schemes. One such task is protein re-design. Recently a new re-design algorithm, AOBB-K*, was introduced and was competitive with state-of-the-art BBK* on small protein re-design problems. However, AOBB-K* did not scale well. In this work we focus on scaling up AOBB-K* and introduce three new versions: AOBB-K*-b (boosted), AOBB-K*-DH (with dynamic heuristics), and AOBB-K*-UFO (with underflow optimization) that significantly enhance scalability.

翻译：科学计算因神经网络等技术的进步而蓬勃发展。然而，某些重要任务较难适用这些技术，仍需从传统推理方案的创新中获益。蛋白质再设计便是此类任务之一。近期提出的新型再设计算法AOBB-K*在小型蛋白质再设计问题上展现出与当前最优算法BBK*相抗衡的性能，但其可扩展性不足。本研究聚焦于提升AOBB-K*的可扩展性，提出三种改进版本：AOBB-K*-b（强化版）、AOBB-K*-DH（动态启发式版）及AOBB-K*-UFO（下溢优化版），显著增强了算法的可扩展能力。