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. 2022 May 23;40(10):1520–1527. doi: 10.1038/s41587-022-01307-0

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — A wide range of high-throughput experiments use selection on libraries of DNA, RNA or displayed protein molecules coupled with sequencing to characterize sequence-specific molecular interactions. ProBound uses machine learning tailored to model the recognition, selection and sequencing processes underlying these data to infer biophysically meaningful recognition models.