Work

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

Year: 2023

Type: preprint

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*, 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2309.00408

Authors: Pezeshki, Bobak, Marinescu, Radu, Ihler, Alexander, Dechter, Rina

Institutions: University of California, Irvine, IBM (United States)

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Science Foundation

Award: RI: Small: Anytime Algorithms and Bounds for Probabilistic Graphical Models

OpenAlex ID: W4386436158

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