Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, artificial intelligence (AI) is revolutionizing the study of RNA-ligand interactions, of...
Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular ...
Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein...
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...
Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, fo...
Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhan...
Journal of chemical information and modeling
Apr 16, 2025
Artificial intelligence (AI) is revolutionizing drug discovery with unprecedented speed and efficiency. In computer-aided drug design, structure-based and ligand-based methodologies are the main driving forces for innovation. In cases where no experi...
Journal of chemical information and modeling
Apr 15, 2025
The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class i...
Antibody discovery has been successful in designing and progressing molecules to the clinic and market based on largely empirical methods and human experience. The field is now transitioning from classical monospecific antibodies to innovative smart ...
Accurately predicting drug-target interactions (DTI) is crucial for drug discovery and can reduce drug development costs. Recent deep learning-based DTI predictions have demonstrated promising performance, but they still face two challenges: (i) The ...
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