Drug discovery today
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Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
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Drug discovery today · Aug 2018
ReviewN-acylethanolamine hydrolyzing acid amidase inhibition: tools and potential therapeutic opportunities.
N-acylethanolamines (NAEs) (e.g., N-palmitoylethanolamine, N-arachidonoylethanolamine, N-oleoylethanolamine) are bioactive lipids involved in many physiological processes including pain, inflammation, anxiety, cognition and food intake. Two enzymes are responsible for the hydrolysis of NAEs and therefore regulate their endogenous levels and effects: fatty acid amide hydrolase (FAAH) and N-acylethanolamine-hydrolyzing acid amidase (NAAA). ⋯ An increasing number of studies describe the synthesis and pharmacological characterization of NAAA inhibitors. Recent medicinal chemistry efforts have led to the development of potent and stable inhibitors that enable studying the effects of NAAA inhibition in preclinical disease models, notably in the context of pain and inflammation.