Matches in SemOpenAlex for { <https://semopenalex.org/work/W3209900550> ?p ?o ?g. }
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- W3209900550 abstract "Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures. These models are usually used to prioritize a list of candidate molecules for future laboratory experiments and to help chemists gain better insights into how structural changes affect a molecule's biological activities. Developing accurate and interpretable QSAR models is therefore of the utmost importance in the drug discovery process. Deep neural networks, which are powerful supervised learning algorithms, have shown great promise for addressing regression and classification problems in various research fields, including the pharmaceutical industry. In this chapter, we briefly review the applications of deep neural networks in QSAR modeling and describe commonly used techniques to improve model performance." @default.
- W3209900550 created "2021-11-08" @default.
- W3209900550 creator A5079572594 @default.
- W3209900550 date "2021-11-04" @default.
- W3209900550 modified "2023-10-17" @default.
- W3209900550 title "Deep Neural Networks for QSAR" @default.
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