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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: A novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is aBRD4i based on its binding pattern with BRD4 is demonstrated.
Abstract: Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4–ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD...

38 citations

Journal ArticleDOI
TL;DR: In this paper, the authors introduce the applications of GNNs in de novo drug design from three aspects: molecule scoring, molecule generation and optimization, and synthesis planning, and discuss the current challenges and future directions of graph neural networks.

38 citations

Journal ArticleDOI
TL;DR: Cl cloning, characterization and enzymatic inhibition of a new MCAT from Helicobacter pylori strain SS1 are described, and corytuberine might be used as a potential lead compound in the discovery of the antibacterial agents using HpMCAT as target.

37 citations

Journal ArticleDOI
TL;DR: To understand the mechanism behind this extraordinary functionally relevant structural transition, Markov state models are constructed using an adaptive seeding method and highlight several parallel folding pathways with heterogeneous molecular mechanisms, which reveal the folding kinetics and atomic details of the conformational transition.
Abstract: The C-terminal domain of the bacterial transcription antiterminator RfaH undergoes a dramatic all-α-helix to all-β-barrel transition when released from its N-terminal domain. These two distinct folding patterns correspond to different functions: the all-α state acts as an essential regulator of transcription to ensure RNA polymerase binding, whereas the all-β state operates as an activator of translation by interacting with the ribosomal protein S10 and recruits ribosomal mRNA. Accordingly, this drastic conformational change enables RfaH to physically couple the transcription and translation processes in gene expression. To understand the mechanism behind this extraordinary functionally relevant structural transition, we constructed Markov state models using an adaptive seeding method. The constructed models highlight several parallel folding pathways with heterogeneous molecular mechanisms, which reveal the folding kinetics and atomic details of the conformational transition.

37 citations

Journal ArticleDOI
TL;DR: The consistency between the results of the BD simulations and the experimental data indicated that the 3D model of the P05-rsk2 channel complex is reasonable and can be employed in further biological studies, such as rational design of the novel therapeutic agents blocking the small-conductance, calcium-activated and apamin-sensitive potassium channels, and for mutagenesis studies in both toxins and SK channels.

36 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