The majority of machine learning scoring functions used in drug discovery for predicting protein-ligand binding poses and affinities have been trained on the PDBBind data set. However, it is unclear whether these new scoring functions are actually an...
Journal of chemical information and modeling
Dec 24, 2025
Accurate prediction of protein-ligand binding affinities (PLAs) is essential for drug discovery and development. Recent advancements suggest that transforming protein-ligand complexes into heterogeneous graph representations may offer a viable soluti...
Proceedings of the National Academy of Sciences of the United States of America
Dec 24, 2025
Understanding protein structure and dynamics is crucial for basic biology and drug design. Conventional methods often provide static conformations that inadequately capture protein flexibility. We present PackDock, a framework that integrates deep le...
Journal of chemical information and modeling
Dec 23, 2025
Protein-ligand binding affinity plays a central role in molecular recognition and drug discovery, yet accurate prediction remains challenging due to the complexity of three-dimensional interactions. Conventional computational approaches, including do...
Journal of chemical information and modeling
Dec 23, 2025
Target-specific scoring functions offer a promising route to improve structure-based virtual screening beyond generic, bias-prone scoring schemes. Here, we introduce AttentionScore, a deep learning-based scoring function for METTL3 that integrates li...
Journal of chemical information and modeling
Dec 22, 2025
The accurate prediction of protein-ligand binding poses and affinities is central to structure-based drug design. In this study, we first benchmarked three distinct pose generation strategies for data sets from the ASAP Antiviral Challenge 2025: mole...
Enzymes are biological catalysts that speed up chemical reactions in an eco-friendly way. Precise enzyme design is hindered by vast sequence space and intricate sequence-structure-function interdependencies. To address these challenges, we developed ...
Journal of chemical information and modeling
Dec 8, 2025
The drug discovery process is inherently lengthy, complex, and costly, with high attrition rates driven by safety concerns, limited efficacy, and regulatory barriers. AI-driven computational methods have become crucial in accelerating this process by...
Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific li...
Elucidation of the potential molecular targets of a bioactive compound, a process known as target-fishing, is a critical task in drug discovery. Computational methods can efficiently narrow down the candidate targets for subsequent experimental valid...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.