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.

Thienyl [3, 2-d] pyrimidin-4-one compounds, preparation method, pharmaceutical compositions and use thereof

Abstract: Disclosed are new thienyl [3, 2-d] pyrimidin-4-one compounds shown as the general formula (I), preparation method, pharmaceutical compositions and pharmacological use thereof. The compounds are strong DPPⅣ (dipeptide peptidase Ⅳ) inhibitors and can treat type II diabetes through well inhibiting DPPⅣ, indirectly increasing the content of GLP-1 in vivo and inducing a series of physiological actions in vivo. Therefore, the compounds could be developed as new promising drugs for treating diabetes.

Advances in Cancer Chemotherapeutic Drug Research in China

Abstract: In this chapter, the history of cancer chemotherapy discipline in China is briefly reviewed

Molecular Modeling and 3D-QSAR Studies on the Interaction Mechanism of Tripeptidyl Thrombin Inhibitors with Human α-Thrombin†

Abstract: The mechanism of inhibition of peptidyl inhibitors with thrombin was studied using molecular modeling, molecular mechanics, and CoMFA statistical analysis. A new procedure for the elucidation of binding conformations, BCSPL, is described and was employed to obtain the binding conformers of a series of 18 tripeptidyl thrombin inhibitors. Energetic studies and QSAR analysis of the BCSPL-derived conformers indicated a modest correlation between the calculated binding energies of the title compounds and their inhibitory activities to human α-thrombin. CoMFA analysis of the BCSPL alignment resulted in a satisfactory model of the thrombin active site.

Pharmacophore-directed homology modeling and molecular dynamics simulation of G protein-coupled receptor: study of possible binding modes of 5-HT2C receptor agonists.

Abstract: A new pharmacophore-based modeling procedure, including homology modeling, pharmacophore study, flexible molecular docking, and long-time molecular dynamics (MD) simulations was employed to construct the structure of the human 5-HT 2C receptor and determine the characteristics of binding modes of 5-HT 2C receptor agonists. An agonist-receptor complex has been constructed based on homology modeling and a pharmacophore hypothesis model based on some high active compounds. Then MD simulations of the ligand-receptor complex in an explicit membrane environment were carried out. The conformation of the 5-HT 2C receptor during MD simulation was explored, and the stable binding modes of the studies agonist were determined. Flexible molecular docking of several structurally diverse agonists of the human 5-HT 2C receptor was carried out, and the general binding modes of these agonists were investigated. According to the models presented in this work and the results of Flexi-Dock, the involvement of the amino acid residues Asp134, Ser138, Asn210, Asn331, Tyr358, Ile131, Ser132, Val135, Thr139, Ile189, Val202, Val208, Leu209, Phe214, Val215, Gly218, Ser219, Phe223, Trp324, Phe327, and Phe328, in agonist recognition was studied. The obtained binding modes of the human 5-HT 2C receptor agonists have good agreement with the site-directed mutagenesis data and other studies.

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.