Molecular function recognition by supervised projection pursuit machine learning

Abstract Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learni...

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Autores principales:	Tyler Grear, Chris Avery, John Patterson, Donald J. Jacobs
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4188accaacb94107ab6e9dd075021902
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spelling	oai:doaj.org-article:4188accaacb94107ab6e9dd0750219022021-12-02T10:54:23ZMolecular function recognition by supervised projection pursuit machine learning10.1038/s41598-021-83269-y2045-2322https://doaj.org/article/4188accaacb94107ab6e9dd0750219022021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83269-yhttps://doaj.org/toc/2045-2322Abstract Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learning for discovery-based design optimization. Molecular function recognition is achieved by pairing experiments that categorize systems with digital twin molecular dynamics simulations to generate working hypotheses. Feature extraction decomposes emergent properties of a system into a complete set of basis vectors. Feature selection requires signal-to-noise, statistical significance, and clustering quality to concurrently surpass acceptance levels. Formulated as a multivariate description of differences and similarities between systems, the data-driven working hypothesis is refined by analyzing new systems prioritized by a discovery-likelihood. Utility and generality are demonstrated on several benchmarks, including the elucidation of antibiotic resistance in TEM-52 beta-lactamase. The software is freely available, enabling turnkey analysis of massive data streams found in computational biology and material science.Tyler GrearChris AveryJohn PattersonDonald J. JacobsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tyler Grear Chris Avery John Patterson Donald J. Jacobs Molecular function recognition by supervised projection pursuit machine learning
description	Abstract Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learning for discovery-based design optimization. Molecular function recognition is achieved by pairing experiments that categorize systems with digital twin molecular dynamics simulations to generate working hypotheses. Feature extraction decomposes emergent properties of a system into a complete set of basis vectors. Feature selection requires signal-to-noise, statistical significance, and clustering quality to concurrently surpass acceptance levels. Formulated as a multivariate description of differences and similarities between systems, the data-driven working hypothesis is refined by analyzing new systems prioritized by a discovery-likelihood. Utility and generality are demonstrated on several benchmarks, including the elucidation of antibiotic resistance in TEM-52 beta-lactamase. The software is freely available, enabling turnkey analysis of massive data streams found in computational biology and material science.
format	article
author	Tyler Grear Chris Avery John Patterson Donald J. Jacobs
author_facet	Tyler Grear Chris Avery John Patterson Donald J. Jacobs
author_sort	Tyler Grear
title	Molecular function recognition by supervised projection pursuit machine learning
title_short	Molecular function recognition by supervised projection pursuit machine learning
title_full	Molecular function recognition by supervised projection pursuit machine learning
title_fullStr	Molecular function recognition by supervised projection pursuit machine learning
title_full_unstemmed	Molecular function recognition by supervised projection pursuit machine learning
title_sort	molecular function recognition by supervised projection pursuit machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4188accaacb94107ab6e9dd075021902
work_keys_str_mv	AT tylergrear molecularfunctionrecognitionbysupervisedprojectionpursuitmachinelearning AT chrisavery molecularfunctionrecognitionbysupervisedprojectionpursuitmachinelearning AT johnpatterson molecularfunctionrecognitionbysupervisedprojectionpursuitmachinelearning AT donaldjjacobs molecularfunctionrecognitionbysupervisedprojectionpursuitmachinelearning
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Molecular function recognition by supervised projection pursuit machine learning

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