Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO

Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1662-1672, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v216-pezeshki23a, title = {Boosting {AND/OR}-based computational protein design: dynamic heuristics and generalizable {UFO}}, author = {Pezeshki, Bobak and Marinescu, Radu and Ihler, Alexander and Dechter, Rina}, booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}, pages = {1662--1672}, year = {2023}, editor = {Evans, Robin J. and Shpitser, Ilya}, volume = {216}, series = {Proceedings of Machine Learning Research}, month = {31 Jul--04 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v216/pezeshki23a/pezeshki23a.pdf}, url = {https://proceedings.mlr.press/v216/pezeshki23a.html}, 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.} }
Endnote
%0 Conference Paper %T Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO %A Bobak Pezeshki %A Radu Marinescu %A Alexander Ihler %A Rina Dechter %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-pezeshki23a %I PMLR %P 1662--1672 %U https://proceedings.mlr.press/v216/pezeshki23a.html %V 216 %X 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.
APA
Pezeshki, B., Marinescu, R., Ihler, A. & Dechter, R.. (2023). Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:1662-1672 Available from https://proceedings.mlr.press/v216/pezeshki23a.html.

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