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Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging "phenotype" over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.
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http://dx.doi.org/10.1053/j.semnuclmed.2025.01.004 | DOI Listing |
J Med Chem
September 2025
State Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Resistance-conferring mutations in the androgen receptor (AR) ligand-binding pocket (LBP) compromise the effectiveness of clinically approved orthosteric AR antagonists. Targeting the dimerization interface pocket (DIP) of AR presents a promising therapeutic approach. In this study, we report the design and optimization of -(thiazol-2-yl) furanamide derivatives as novel AR DIP antagonists, among which was the most promising candidate.
View Article and Find Full Text PDFNano Lett
September 2025
Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
Precise delivery of nanoliter-scale reagents is essential for high-throughput biochemical assays, yet existing platforms often lack real-time control and selective content fusion. Conventional methods rely on passive encapsulation or stochastic pairing, limiting both throughput and biochemical specificity. Here, we introduce an on-demand nanoliter delivery platform that seamlessly integrates electrical sensing, triggered droplet merging, and passive sorting in a single continuous flow.
View Article and Find Full Text PDFSci Transl Med
September 2025
Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Oligodendrocytes, the myelinating cells of the central nervous system (CNS), are essential for the formation of myelin sheaths and pivotal for maintaining axonal integrity and conduction. Disruption of these cells and the myelin sheaths they produce is a hallmark of demyelinating conditions like multiple sclerosis or those resulting from certain drug side effects, leading to profound neurological impairments. In this study, we created a human brain organoid comprising neurons, astrocytes, and myelinating oligodendrocytes.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Pharmacy, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China.
Background: Ankylosing spondylitis (AS), a chronic inflammatory disorder affecting axial joints, is frequently complicated by uveitis. However, the molecular mechanisms linking AS to secondary uveitis remain poorly understood.
Methods: We integrated transcriptomic datasets from AS (GSE73754) and uveitis (GSE194060) cohorts to identify shared molecular pathways.
J Med Chem
September 2025
Encoded Technologies, Molecular Modalities Discovery, GSK, Cambridge, Massachusetts 02140, United States.
DNA-encoded libraries (DELs) are used throughout small-molecule drug discovery to identify new lead compounds for protein targets. DEL hits are traditionally evaluated via off-DNA resynthesis and subsequent biological testing. This approach can be time- and resource-intensive, limiting the number of putative hits selected for follow-up.
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