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Article

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

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, I-27100 Pavia, Italy
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Author to whom correspondence should be addressed.
Academic Editors: Isabel Gonzalez-Alvarez and Peter Langguth
Received: 13 April 2021 / Revised: 15 July 2021 / Accepted: 16 July 2021 / Published: 20 July 2021
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 (logS0) 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 logS0 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 logS0; 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. View Full-Text
Keywords: artificial neural networks; machine learning; QSPR; intrinsic aqueous solubility artificial neural networks; machine learning; QSPR; intrinsic aqueous solubility
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MDPI and ACS Style

Tosca, E.M.; Bartolucci, R.; Magni, P. Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules. Pharmaceutics 2021, 13, 1101. https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics13071101

AMA Style

Tosca EM, Bartolucci R, Magni P. Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules. Pharmaceutics. 2021; 13(7):1101. https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics13071101

Chicago/Turabian Style

Tosca, Elena M., Roberta Bartolucci, and Paolo Magni. 2021. "Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules" Pharmaceutics 13, no. 7: 1101. https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics13071101

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