Background Molecular descriptors and fingerprints have been routinely used in QSAR/SAR analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and other drug discovery processes. Since the calculation of such quantitative representations of molecules may require substantial computational skills and efforts, several tools have been previously developed to make an attempt to ease the process. However, there are still several hurdles for users to overcome to fully harness the power of these tools. First, most of the tools are distributed as standalone software or packages that require necessary configuration or programming efforts of users. Second, many of the tools can only calculate a subset of molecular descriptors, and the results from multiple tools need to be manually merged to generate a comprehensive set of descriptors. Third, some packages only provide application programming interfaces and are implemented in different computer languages, which pose additional challenges to the integration of these tools.Results A freely available web-based platform, named ChemDes, is developed in this study. It integrates multiple state-of-the-art packages (i.e., Pybel, CDK, RDKit, BlueDesc, Chemopy, PaDEL and jCompoundMapper) for computing molecular descriptors and fingerprints. ChemDes not only provides friendly web interfaces to relieve users from burdensome programming work, but also offers three useful and convenient auxiliary tools for format converting, MOPAC optimization and fingerprint similarity calculation. Currently, ChemDes has the capability of computing 3679 molecular descriptors and 59 types of molecular fingerprints.ConclusionChemDes provides users an integrated and friendly tool to calculate various molecular descriptors and fingerprints. It is freely available at http://www.scbdd.com/chemdes. The source code of the project is also available as a supplementary file.Graphical abstract:An overview of ChemDes. A platform for computing various molecular descriptors and fingerprints

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Melatonin is a ubiquitous hormone found in various organisms and highly affects the function of immune cells. In this review, we summarize the current understanding of the significance of melatonin in macrophage biology and the beneficial effects of melatonin in macrophage‐associated diseases. Enzymes associated with synthesis of melatonin, as well as membrane receptors for melatonin, are found in macrophages. Indeed, melatonin influences the phenotype polarization of macrophages. Mechanistically, the roles of melatonin in macrophages are related to several cellular signaling pathways, such as NF‐κB, STATs, and NLRP3/caspase‐1. Notably, miRNAs (eg, miR‐155/‐34a/‐23a), cellular metabolic pathways (eg, α‐KG, HIF‐1α, and ROS), and mitochondrial dynamics and mitophagy are also involved. Thus, melatonin modulates the development and progression of various macrophage‐associated diseases, such as cancer and rheumatoid arthritis. This review provides a better understanding about the importance of melatonin in macrophage biology and macrophage‐associated diseases.

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Gallic acid (GA) is a naturally occurring polyphenol compound present in fruits, vegetables, and herbal medicines. According to previous studies, GA has many biological properties, including antioxidant, anticancer, anti-inflammatory, and antimicrobial properties. GA and its derivatives have multiple industrial uses, such as food supplements or additives. Additionally, recent studies have shown that GA and its derivatives not only enhance gut microbiome (GM) activities, but also modulate immune responses. Thus, GA has great potential to facilitate natural defense against microbial infections and modulate the immune response. However, the exact mechanisms of GA acts on the GM and immune system remain unclear. In this review, first the physicochemical properties, bioavailability, absorption, and metabolism of GA are introduced, and then we summarize recent findings concerning its roles in gastrointestinal health. Furthermore, the present review attempts to explain how GA influences the GM and modulates the immune response to maintain intestinal health.

The conversion of skeletal muscle fiber from fast twitch to slow‐twitch is important for sustained and tonic contractile events, maintenance of energy homeostasis, and the alleviation of fatigue. Skeletal muscle remodeling is effectively induced by endurance or aerobic exercise, which also generates several tricarboxylic acid ( TCA ) cycle intermediates, including succinate. However, whether succinate regulates muscle fiber‐type transitions remains unclear. Here, we found that dietary succinate supplementation increased endurance exercise ability, myosin heavy chain I expression, aerobic enzyme activity, oxygen consumption, and mitochondrial biogenesis in mouse skeletal muscle. By contrast, succinate decreased lactate dehydrogenase activity, lactate production, and myosin heavy chain II b expression. Further, by using pharmacological or genetic loss‐of‐function models generated by phospholipase Cβ antagonists, SUNCR 1 global knockout, or SUNCR 1 gastrocnemius‐specific knockdown, we found that the effects of succinate on skeletal muscle fiber‐type remodeling are mediated by SUNCR 1 and its downstream calcium/ NFAT signaling pathway. In summary, our results demonstrate succinate induces transition of skeletal muscle fiber via SUNCR 1 signaling pathway. These findings suggest the potential beneficial use of succinate‐based compounds in both athletic and sedentary populations.

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.