Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the mo...

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Autores principales:	Dávid Péter Kovács, William McCorkindale, Alpha A. Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f5f5a1b384fd4609aa785325aa34d776
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spelling	oai:doaj.org-article:f5f5a1b384fd4609aa785325aa34d7762021-12-02T17:05:49ZQuantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias10.1038/s41467-021-21895-w2041-1723https://doaj.org/article/f5f5a1b384fd4609aa785325aa34d7762021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21895-whttps://doaj.org/toc/2041-1723Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the model’s performance.Dávid Péter KovácsWilliam McCorkindaleAlpha A. LeeNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dávid Péter Kovács William McCorkindale Alpha A. Lee Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
description	Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the model’s performance.
format	article
author	Dávid Péter Kovács William McCorkindale Alpha A. Lee
author_facet	Dávid Péter Kovács William McCorkindale Alpha A. Lee
author_sort	Dávid Péter Kovács
title	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
title_short	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
title_full	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
title_fullStr	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
title_full_unstemmed	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
title_sort	quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f5f5a1b384fd4609aa785325aa34d776
work_keys_str_mv	AT davidpeterkovacs quantitativeinterpretationexplainsmachinelearningmodelsforchemicalreactionpredictionanduncoversbias AT williammccorkindale quantitativeinterpretationexplainsmachinelearningmodelsforchemicalreactionpredictionanduncoversbias AT alphaalee quantitativeinterpretationexplainsmachinelearningmodelsforchemicalreactionpredictionanduncoversbias
_version_	1718381826589851648

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

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