Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO

Abstract

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\textsuperscript{*}}, was introduced and was competitive with state-of-the-art {BBK\textsuperscript{*}} on small protein re-design problems. However, {AOBB-K\textsuperscript{*}} did not scale well. In this work, we focus on scaling up {AOBB-K\textsuperscript{*}} and introduce three new versions: {AOBB-K\textsuperscript{*}}-b (boosted), {AOBB-K\textsuperscript{*}}-DH (with dynamic heuristics), and {AOBB-K\textsuperscript{*}}-UFO (with underflow optimization) that significantly enhance scalability.