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.

Hexahydro dibenzo [a,g] quinolizidine compound and preparation method, medicinal composition and application thereof

Abstract: The invention relates to a novel hexahydro dibenzo [a,g] quinolizidine compound shown as a general formula (I) and a derivative, an enantiomer, a diastereoisomer, a racemate, an enantiomer, diastereoisomer and racemate mixture, pharmaceutically acceptable salt and a preparation method thereof. In addition, the compound has a good effect of preventing and treating nervous system diseases, particularly diseases related to a dopamine receptor and a serotonin receptor. Biological activity experiments show that the compound is expected to be developed into a powerful new chemical entity for treating diseases related to the dopamine receptor and the serotonin receptor, particularly schizophrenia, Parkinson's disease, drug addiction, migraine and the like.

Phenols and γ‐Lactams from the Aerial Part of Pseudolysimachion linariifolium subsp. dilatatum

Abstract: Linariifolioside II (1) and (2S)-2-hydroxy-5-oxoproline methyl ester (2), two new compounds along with 13 known compounds were obtained from the aerial part of Pseudolysimachion linariifolium Holub subsp. dilatatum (Nakai & Kitag.) D.Y. Hong. Their chemical structures were revealed mainly through NMR and MS data. The absolute configuration of 2 was deduced by comparing its experimental CD with the calculated ECD spectra. At a concentration of 1 mm, total antioxidant capacities of compounds 1-15 were measured using a rapid ABTS method in vitro. Compounds 1, 3-5, and 11-14 exhibited approximately equal antioxidant capacity to that of vitamin C (Vc).

Interaction models of 3-methylfentanyl derivatives with mu opioid receptors.

Abstract: AIM: To study the interaction model of 3-methylfentanyl derivatives with mu opioid receptor. METHODS: After a systematic conformational search, a three-dimensional quantitative structure-activity relationship study was carried out with comparative molecular field analysis (CoMFA). RESULTS: 1) The 6 CoMFA models had good predictive values and each model corresponded to the minimum-energy conformations of 13 compounds studied; 2) The important geometric parameters of mu pharmacophore d1 (A), d2 (A), d3 (A), d4 (A), d5 (A), and d6 (A) were 5.2, 5.4, 4.9, 10.6, 10.2, and 5.8 in Model A; 5.2, 6.5, 3.6, 10.6, 11.6, and 5.8 in Model B; 5.2, 4.6, 4.9, 11.6, 9.2, and 6.5 in Model C; 5.2, 5.4, 4.9, 10.5, 10.3, and 5.8 in Model D; 3.6, 5.4, 4.9, 5.7, 7.5, and 5.7 in Model E; 5.2, 4.7, 4.9, 11.2, 9.5, and 6.4 in Model F, respectively. CONCLUSIONS: The several bioactive conformations of fentanyl analogs possibly existed and did not need to be the absolute minimum-energy conformation, each of which was involved in the interaction with mu opioid receptor.

Pattern Recognition and Machine Learning

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.

“Bioinformatics” 특집을 내면서

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.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

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.

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays

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.