MOTIVATION: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles...
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to o...
Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to the formation of colloidal aggregators. Such false positives often lead to significant research costs...
Recent advances in pharmacology are revolutionizing drug discovery and treatment strategies through personalized medicine, pharmacogenomics, and artificial intelligence (AI). The objective of the present study is to review the role of personalized me...
Journal of biomolecular structure & dynamics
May 1, 2025
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatab...
Journal of biomolecular structure & dynamics
May 1, 2025
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a c...
Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the...
SUMMARY: The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionize drug discovery by extracting evidence from scientific literature for drug target identification and valid...
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for auto...
The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understan...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.