Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.
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http://dx.doi.org/10.1021/acs.chemrestox.4c00050 | DOI Listing |
Acad Radiol
January 2025
Department of Radiology, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431 (M.R.). Electronic address:
Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task Force to develop this guide to help radiology researchers responsibly adopt LLM technologies. LLMs can improve various phases of the research process by helping to automate literature reviews, generate research questions, analyze complex datasets, and draft manuscripts.
View Article and Find Full Text PDFSci Bull (Beijing)
December 2024
China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100029, China; National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China. Electronic address:
J Struct Biol
January 2025
Center of Structural Biology, Vanderbilt University, Nashville, TN, USA; Department of Chemistry, Vanderbilt University, Nashville, TN, USA; Institute for Drug Discovery, Institute for Computer Science, Wilhelm Ostwald Institute for Physical and Theoretical Chemistry, University Leipzig, Leipzig, Germany; Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI and School of Embedded Composite Artificial Intelligence SECAI, Dresden/Leipzig, Germany; Department of Pharmacology, Institute of Chemical Biology, Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Electronic address:
High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually.
View Article and Find Full Text PDFNeurospine
December 2024
Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML).
Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, "neck function," which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed.
Comput Biol Chem
January 2025
College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
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