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Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism, and thus, they are potential therapeutics to prevent and treat nonalcoholic fatty liver disease. The low success rate of FXR agonists' R&D and the side effects of clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery has rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR (hFXR)-specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of the FRED docking plus Chemgauss4 scoring function. It turned out that the hFXR-specific pose filter ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE the ideal approach for FXR agonist discovery. By screening of the Specs chemical library and in vitro FXR transactivation bioassay, we identified a new class of FXR agonists with compound XJ034 as the representative, which would have been missed if the PFE was not coupled. Following that, we performed in-depth biological studies which demonstrated that XJ034 resulted in a downtrend of intracellular triglyceride in vitro, significantly decreased the serum/liver TG in high fat diet-induced C57BL/6J obese mice, and more importantly, showed metabolic stabilities in both plasma and liver microsomes. To provide insight into further structure-based lead optimization, we solved the crystal structure of hFXR complexed with compound XJ034, uncovering a unique hydrogen bond between compound XJ034 and residue Y375. The current work highlights the power of our pose filter-based ensemble learning approach in terms of scaffold hopping and provides a promising lead compound for further development.
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http://dx.doi.org/10.1021/acs.jcim.9b01030 | DOI Listing |
Cureus
November 2024
Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, CHN.
Background Cardiovascular diseases (CVD), including coronary artery disease, ischemic heart disease, stroke, cardiomyopathy, and atrial fibrillation and flutter, are the leading cause of mortality worldwide, resulting in significant economic and health costs. Recognizing trends and geographical differences in the global burden of CVD facilitates health authorities in particular nations to assess the disease burden and forecast future epidemiological trends. Public health authorities in each country can better understand the differences in disease data and, by learning from the experiences and practices of successful countries and considering the characteristics of their diseases, allocate health resources more rationally and formulate more targeted healthcare strategies to reduce the disease burden.
View Article and Find Full Text PDFSci Rep
December 2024
SDU Health Informatics and Technology, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230, Odense, Denmark.
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases.
View Article and Find Full Text PDFIntell Based Med
July 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USA.
Objective: The paper aims to address the problem of massive unlabeled patients in electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy (DR). It is desired to estimate the actual DR prevalence in EHR with 96 % missing labels.
Materials And Methods: The Cerner Health Facts data are used in the study, with 3749 labeled DR patients and 97,876 unlabeled diabetic patients.
BMC Pregnancy Childbirth
December 2024
Department of Computer Science, Columbia University, 1214 Amsterdam Ave, 721 Schapiro CEPSR, New York, NY, 10027, USA.
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort.
View Article and Find Full Text PDFExp Dermatol
December 2024
Computer Science & Engineering Department, MNNIT Allahabad, Prayagraj, Uttar Pradesh, India.
Skin cancer remains one of the most common and deadly forms of cancer, necessitating accurate and early diagnosis to improve patient outcomes. In order to improve classification performance on unbalanced datasets, this study proposes a distinctive approach for classifying skin cancer that utilises both machine learning (ML) and deep learning (DL) methods. We extract features from three different DL models (DenseNet201, Xception, Mobilenet) and concatenate them to create an extensive feature set.
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