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.

Gene toxicity probability forecasting method based on molecule electrophilic vector and extend supporting vector machine

Abstract: The invention relates to the gene toxicity probability preparation method based on the MEV and SVM which is proper for the dummy toxicity appraise and selection according to the organic compound molecule structure information Firstly, it classifies the molecule structure based on the SMARTS and PATTY according to predefine rule; then to compute the atom descriptor (front track electron density, electron superdelocalizability and atom pi-charge) of every atom type according to the Huckel method and set the MEV to descript the electrophilicity; Last to statistic the gene toxicity data and MEV according to the SVM and get the posterior probability estimation of the molecule gene toxicity

Quantum chemical HF/4-31G calculations on buckminsterfullerene intermediates

Abstract: Quantum chemical ab initio (U)HF/4-31G investigation on buckminsterfullerene and some proposed intermediates in its formation is carried out in this study with a view to better understanding how small carbon species carry out self-assembly to form fullerenes. The calculations on 19 carefully designed fullerene intermediates reveal that the core of an intermediate, rather than the number of its dangling bonds or abutting pentagon rings, has an intrinsic effect on its energy. The computational results show that hexagonal-core structures have lower energies than pentagonal-core structures. In addition, the pentagonal core enclosed completely by hexagonal rings has the highest energy. The UHF/4-31G results also suggest that some intermediates such as C18, C21 and C30 with hexagonal cores have unusually low energies in comparison with their isomers or neighbours. Based on these calculated results, we outline the possible pathways from precursor to intermediates to fullerenes, subject to synthesis conditions and raw materials. These pathways support some existing proposals, such as medium monocyclic ring stacking and small ring polymerization mechanisms. However, our results do not suggest that the numbers of dangling bonds or abutting pentagonal rings have the highest impact on fullerene formation. The calculated thermodynamic parameters of the dimerization and addition reactions between two bowl-shaped intermediates suggest that these reactions are favorable to fullerene formation, and that the concentration of bowl-shaped fullerene intermediates should be very low in all detectable carbon species.

Two new sulfated sesquiterpenoids from Petasites tricholobus.

Abstract: Two new sulfated sesquiterpenoids, megastigman-7-ene-3, 5, 6, 9-tetrol-3-O-β-D-6'-sulfonated-glucopyranoside (1) and 3-O-β-D-6'-sulfonated-glucopyranosyl-6-(3-oxo-2-butenylidenyl)-1, 1, 5-trimethylcyclohexan-5-ol (2), along with one known sesquitepenoid compound icariside B1 (3) were isolated from the whole herb of Petasites tricholobus Franch. Their structures were identified by their chemical and spectroscopic characters. All obtained compounds were tested for their cytotoxicity against four cancer cell lines.

Ab initio HF and density-functional theory studies on the structure and vibrational frequency of huperzine A

Abstract: Huperzine A, an alkaloid used as acetylcholinesterase inhibitor isolated from traditional Chinese herb, was studied using semiempirical method AM1, ab initio Hartree–Fock (HF), and density-functional theory (DFT) B3LYP method at different basis set levels. The calculated results showed that the three-ring structure of HupA is rigid and the pyridone ring is planar. However, the hydrogen atom positions of its amide and amino groups will shift when the molecular environment changes, especially for the amino group. HF/4-31G calculated results revealed that the amino group can rotate at room temperature. The investigation also indicated that the B3LYP/6-31G* method is better than AM1 and HF/6-31G* for studying infra-red (IR) spectrum of HupA and its analogues. The predicted vibrational bands at B3LYP/6-31G* level are in good agreement with the observed spectrum except the vibrational modes which relate to the atoms of amide and amino groups. The reason for the differences of structure and vibrational bands is probably that these groups can form intermolecular hydrogen bonds in the crystal structure, which will affect the force properties and the vibrational frequency.

QSAR of 3-methylfentanyl derivatives studied with neural networks method.

Abstract: AIM: To use neural networks, which simulate the functions of living nervous systems, in QSAR studies; METHODS: Using the back-propagation neural networks program devised by us, combining with partial least squares (PLS) method, we studied the relationships of quantum chemical indices and analgesic activities of 25 3-methylfentanyl derivatives; RESULTS: Through learning process, a good QSAR model was established, and the activities of these compounds were predicted; the correlation between the activities and quantum chemical indices: the net charge of the atom N1, the net charge of the atom O16, the torsional angle of atoms C10-C9-N8-C4, the interatomic distance between atom C7 and the center of phenyl plane C9-14 (PhA), is quite well-matched. Based on these results, an interactive pattern between 3-methylfentanyl derivatives and opioid receptors was suggested; CONCLUSION: Not only are the results of neural networks superior to those of PLS method but they also provide accurate predictions of the activity of the compounds and also combine the PLS method with neural networks.

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.