Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to pre...
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Autor*in:	Elena M. Tosca [verfasserIn] Roberta Bartolucci [verfasserIn] Paolo Magni [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	artificial neural networks machine learning QSPR intrinsic aqueous solubility

Übergeordnetes Werk:	In: Pharmaceutics - MDPI AG, 2010, 13(2021), 7, p 1101
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:7, p 1101

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pharmaceutics13071101

Katalog-ID:	DOAJ084697318

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source	In Pharmaceutics 13(2021), 7, p 1101 volume:13 year:2021 number:7, p 1101
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authorswithroles_txt_mv	Elena M. Tosca @@aut@@ Roberta Bartolucci @@aut@@ Paolo Magni @@aut@@
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callnumber-first	R - Medicine
author	Elena M. Tosca
spellingShingle	Elena M. Tosca misc RS1-441 misc artificial neural networks misc machine learning misc QSPR misc intrinsic aqueous solubility misc Pharmacy and materia medica Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
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topic_title	RS1-441 Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules artificial neural networks machine learning QSPR intrinsic aqueous solubility
topic	misc RS1-441 misc artificial neural networks misc machine learning misc QSPR misc intrinsic aqueous solubility misc Pharmacy and materia medica
topic_unstemmed	misc RS1-441 misc artificial neural networks misc machine learning misc QSPR misc intrinsic aqueous solubility misc Pharmacy and materia medica
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title	Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
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title_sort	application of artificial neural networks to predict the intrinsic solubility of drug-like molecules
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title_auth	Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
abstract	Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (<i<logS</i<<sub<0</sub<) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with <i<logS</i<<sub<0</sub< value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict <i<logS</i<<sub<0</sub<; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.
abstractGer	Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (<i<logS</i<<sub<0</sub<) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with <i<logS</i<<sub<0</sub< value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict <i<logS</i<<sub<0</sub<; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.
abstract_unstemmed	Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (<i<logS</i<<sub<0</sub<) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with <i<logS</i<<sub<0</sub< value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict <i<logS</i<<sub<0</sub<; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.
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title_short	Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
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Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

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