The pollution of water resources by pharmaceuticals and agents of personal care products (PPCPs) poses an increasingly pressing issue that has received considerable attention from scientists and government agencies alike. Hydrophobic zeolites can serve as selective adsorbents to remove these contaminants from aqueous solution. So far, the adsorption of PPCPs in zeolites has often been investigated in case studies focusing on a small number of contaminants and one or a few zeolites. We present a computational screening approach to investigate the interaction of 53 PPCPs with 14 all-silica zeolites, aiming at a more comprehensive understanding of factors that are beneficial for a strong host-guest interaction and thus an efficient adsorption. The systems are modelled on the classical force field level of theory, allowing for the efficient computational treatment of a large number of PPCP-zeolite combinations and evaluated in terms of the interaction energy between PPCP and zeolite framework. For selected PPCP-zeolite combinations additional Free Energy Perturbation simulations are employed to compute Free Energies of Transfer between the aqueous phase and the adsorbed state. These results can serve as a starting point for experimental studies of relevant PPCP-zeolite combination or more in-depth theoretical investigations.
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http://dx.doi.org/10.1002/cphc.202400347 | DOI Listing |
Expert Opin Drug Discov
January 2025
Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY, USA.
Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.
View Article and Find Full Text PDFComput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFBr J Radiol
January 2025
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Yanta Western Road, Xi'an, Shannxi, 710061.
Purpose: To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.
Materials And Methods: This prospective study with patient consent included 62 COPD patients. Patients were examined by pulmonary function test (PFT), standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT).
Mol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFClin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
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