Journal of chemical information and modeling
Oct 16, 2025
The advent of agentic AI systems is leading to significant transformations across scientific and technological domains. Advances in large language models (LLMs), reasoning capabilities, and integration with external tools have ushered in a new era wh...
Proceedings of the National Academy of Sciences of the United States of America
Oct 16, 2025
Rapid and accurate estimation of protein-ligand binding affinities is crucial for early-stage drug discovery, yet hindered by a trade-off between the accuracy of gold-standard physics-based methods and the speed of simpler empirical scoring functions...
BACKGROUND: Predicting drug-target interactions (DTIs) is essential for accelerating drug discovery, yet traditional experimental methods are time-consuming and costly. Computational approaches, especially those using machine learning and deep learni...
In drug discovery, metabolite identification data are used to identify metabolic soft spots in research molecules to facilitate reduced metabolism in subsequently designed compounds. In addition, knowledge about exact metabolite structures enables th...
BMC medical informatics and decision making
Oct 14, 2025
BACKGROUND: Leishmaniasis, a neglected tropical disease caused by Leishmania protozoan parasites and transmitted by sandflies, poses a significant global health challenge, especially in resource-limited environments. The life cycle of the parasite in...
MALT1 is a key component of the CARD11-BCL10-MALT1 (CBM) complex downstream from BTK on the B-cell receptor signaling pathway. It is a key mediator of NF-κB signaling and considered a potential therapeutic target for several subtypes of non-Hodgkin's...
Drug discovery is a challenging and resource-intensive process characterized by high costs, prolonged development timelines, and regulatory hurdles in the pharmaceutical sector. AI-driven recommendation systems have emerged as an effective approach t...
In silico drug-target interaction (DTI) prediction plays a key role in accelerating drug discovery and understanding molecular mechanisms. Traditional methods often struggle with the complexity and scale of biochemical data, thus limiting prediction ...
Journal of chemical information and modeling
Oct 12, 2025
Advances in machine learning (ML) offer significant potential to accelerate drug discovery. Although mathematical modeling and ML have become crucial in predicting drug-target interactions and properties, the complexity of chemical space and the "bla...
ConspectusThis Account discusses recent progress and challenges in binding free energy computations, focusing on two classes of enhanced sampling techniques: alchemical transformations and path-based methods. Binding free energy is a crucial metric i...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.