Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Taiki Fujimoto, Hiroaki Gotoh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning antioxidant singlet oxygen feature importance interpretability carotenoid Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/f85ee57e14b544cbac95630c0dc2d789
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f85ee57e14b544cbac95630c0dc2d789
record_format	dspace
spelling	oai:doaj.org-article:f85ee57e14b544cbac95630c0dc2d7892021-11-25T16:27:49ZPrediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning10.3390/antiox101117512076-3921https://doaj.org/article/f85ee57e14b544cbac95630c0dc2d7892021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3921/10/11/1751https://doaj.org/toc/2076-3921A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.Taiki FujimotoHiroaki GotohMDPI AGarticlemachine learningantioxidantsinglet oxygenfeature importanceinterpretabilitycarotenoidTherapeutics. PharmacologyRM1-950ENAntioxidants, Vol 10, Iss 1751, p 1751 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning antioxidant singlet oxygen feature importance interpretability carotenoid Therapeutics. Pharmacology RM1-950
spellingShingle	machine learning antioxidant singlet oxygen feature importance interpretability carotenoid Therapeutics. Pharmacology RM1-950 Taiki Fujimoto Hiroaki Gotoh Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
description	A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.
format	article
author	Taiki Fujimoto Hiroaki Gotoh
author_facet	Taiki Fujimoto Hiroaki Gotoh
author_sort	Taiki Fujimoto
title	Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
title_short	Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
title_full	Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
title_fullStr	Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
title_full_unstemmed	Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
title_sort	prediction and chemical interpretation of singlet-oxygen-scavenging activity of small molecule compounds by using machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f85ee57e14b544cbac95630c0dc2d789
work_keys_str_mv	AT taikifujimoto predictionandchemicalinterpretationofsingletoxygenscavengingactivityofsmallmoleculecompoundsbyusingmachinelearning AT hiroakigotoh predictionandchemicalinterpretationofsingletoxygenscavengingactivityofsmallmoleculecompoundsbyusingmachinelearning
_version_	1718413176178999296

Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

Ejemplares similares