AIMC Topic: Drug Discovery
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SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.
Journal of bioinformatics and computational biology
Mar 25, 2025
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...
NNSFMDA: Lightweight Transformer Model with Bounded Nuclear Norm Minimization for Microbe-Drug Association Prediction.
Journal of molecular biology
Mar 24, 2025
Identifying potential connections between microbe-drug pairs play an important role in drug discovery and clinical treatment. Techniques like graph neural networks effectively derive accurate node representations from sparse topologies,however, they ...
A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery.
Kidney international
Mar 24, 2025
For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and the slit diaphragm. Despite its status as the gold standard, electron microscopy has significant li...
Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.
Computers in biology and medicine
Mar 22, 2025
Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysi...
Digital evolution: Novo Nordisk's shift to ontology-based data management.
Journal of biomedical semantics
Mar 22, 2025
The amount of biomedical data is growing, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterpris...
A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control.
Journal of fish diseases
Mar 20, 2025
Viral diseases pose a significant threat to the sustainability of global aquaculture, causing economic losses and compromising food security. Traditional control methods often demonstrate limited effectiveness, highlighting the need for alternative a...
Embracing the changes and challenges with modern early drug discovery.
Expert opinion on drug discovery
Mar 19, 2025
INTRODUCTION: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery ...
Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics.
Drug discovery today
Mar 17, 2025
Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyond lifestyle changes and current medications. Existing anti-obesity drugs face limitations regarding efficacy, side effects, weight regain and high co...
Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.
Biomolecules
Mar 17, 2025
Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy...
Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.
International journal of molecular sciences
Mar 17, 2025
The classification of Hepatitis C virus (HCV) NS3 inhibitors is essential for identifying potential antiviral agents through computational methods. This study aims to develop an optimized machine learning (ML) model using random forest (RF) and molec...
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