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• W2346751919 endingPage "639" @default.
• W2346751919 startingPage "627" @default.
• W2346751919 abstract "Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future." @default.
• W2346751919 created "2016-06-24" @default.
• W2346751919 creator A5077868077 @default.
• W2346751919 creator A5090707029 @default.
• W2346751919 date "2016-05-30" @default.
• W2346751919 modified "2023-10-17" @default.
• W2346751919 title "Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?" @default.
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• W2346751919 doi "https://doi.org/10.1080/17460441.2016.1186876" @default.
• W2346751919 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27149299" @default.
• W2346751919 hasPublicationYear "2016" @default.
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