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.

Study on mechanism of interaction of nociceptin and opioids binding with opioid receptor-like 1 receptor.

Abstract: "AIM: To study the mechanism of interaction of nociceptin and opioids with ORL1 receptor. METHODS: Molecular dynamics study was carried out before nociceptin was manually docked into the binding site of ORL1 receptor; DOCK4.0 program was applied to dock four stereoisomers of lofentanyl and etorphine into the binding pocket of ORL1 receptor; Binding energies were calculated, the relationship between binding energy and binding affinity was studied. RESULTS: Nociceptin fits well into the binding pocket, the N-terminal FGGF tetrapeptide is located in the inner region of the binding cavity, the nociceptin (5-7) interacts with the conservatively variable residues near the other end of binding pocket, and maybe determines selectivity of ORL1 receptor over dynorphin A, the positively charged core of nociceptin (8-13) binds predominantly with negatively charged EL-2 loop, which is thought to be able to mediate receptor activation. The shortest fully active analogue of nociceptin (1-13) is also discussed. The main difference between these two opioids and nociceptin exists in the kinds and the number of conserved and variable residues in the binding pocket and thereafter in the strength of their interaction. Prediction for binding affinities of four stereoisomers of lofentanyl has been performed based on their binding energies, the similar pharmacophore of lofentanyl and other fentanyl analogs, and the good correlation between binding energies and their experimental binding affinities (-log Ki values). CONCLUSION: Ligand docking results from this study are helpful in clarifying experimental observations of ligands interaction with opioid receptors, thus furthering biological investigations."

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.