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Kaixian Chen

Bio: Kaixian Chen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Virtual screening & Docking (molecular). The author has an hindex of 47, co-authored 380 publications receiving 9209 citations. Previous affiliations of Kaixian Chen include Shanghai University & East China University of Science and Technology.


Papers
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Journal ArticleDOI
TL;DR: Isatindigosesquilignans B and C were determined as the first examples of its glycosides from a natural source and a plausible biosynthetic pathway was proposed.

25 citations

Journal ArticleDOI
06 Jul 2015-PLOS ONE
TL;DR: It was found that both nifedipine and diazoxide protected MIN6 pancreatic β-cells and primary cultured murine islets from palmitic acid-induced apoptosis and suggested that regulation of calcium influx by small molecule compounds might provide benefits for the prevention and therapy of type 2 diabetes.
Abstract: Lipotoxicity plays an important role in pancreatic β-cell failure during the development of type 2 diabetes. Prolonged exposure of β-cells to elevated free fatty acids level could cause deterioration of β-cell function and induce cell apoptosis. Therefore, inhibition of fatty acids-induced β-cell dysfunction and apoptosis might provide benefit for the therapy of type 2 diabetes. The present study examined whether regulation of fatty acids-triggered calcium influx could protect pancreatic β-cells from lipotoxicity. Two small molecule compounds, L-type calcium channel blocker nifedipine and potassium channel activator diazoxide were used to inhibit palmitic acid-induced calcium influx. And whether the compounds could reduce palmitic acid-induced β-cell failure and the underlying mechanism were also investigated. It was found that both nifedipine and diazoxide protected MIN6 pancreatic β-cells and primary cultured murine islets from palmitic acid-induced apoptosis. Meanwhile, the impaired insulin secretion was also recovered to varying degrees by these two compounds. Our results verified that nifedipine and diazoxide could reduce palmitic acid-induced endoplasmic reticulum stress to generate protective effects on pancreatic β-cells. More importantly, it suggested that regulation of calcium influx by small molecule compounds might provide benefits for the prevention and therapy of type 2 diabetes.

25 citations

Journal ArticleDOI
TL;DR: Analysis of the log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration.
Abstract: Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

24 citations

Journal ArticleDOI
TL;DR: Both the docking simulations and QSAR analyses suggest that new potent dual inhibitors should share a structural feature with a moderately bulky group at R2 position and a rather negatively charged group around the position of the carbonyl group of DHDMBFs.

24 citations

Journal ArticleDOI
TL;DR: Four undescribed sulfur-containing indole alkaloids, isatisindigoticanines H, I and isatindigosides F, G along with three known analogues were obtained from Isatis tinctoria L. roots.

24 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

Journal ArticleDOI
TL;DR: This review covers the literature published in 2014 for marine natural products, with 1116 citations referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms.

4,649 citations

Journal ArticleDOI
11 Jun 2020-Nature
TL;DR: A programme of structure-assisted drug design and high-throughput screening identifies six compounds that inhibit the main protease of SARS-CoV-2, demonstrating the ability of this strategy to isolate drug leads with clinical potential.
Abstract: A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019–2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)1–4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (Mpro) of SARS-CoV-2: Mpro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-25,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of Mpro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds—including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds—as inhibitors of Mpro. Six of these compounds inhibited Mpro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 μM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available. A programme of structure-assisted drug design and high-throughput screening identifies six compounds that inhibit the main protease of SARS-CoV-2, demonstrating the ability of this strategy to isolate drug leads with clinical potential.

2,845 citations

Journal ArticleDOI
TL;DR: A number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens are described.
Abstract: This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.

2,791 citations