How ligands with different efficacies regulate G protein-coupled receptor signaling remains incompletely understood. Here, we combine Markov state modeling of nearly one millisecond of aggregate all-a...

On May 29, 2026, the Office of Management and Budget issued a Notice of Proposed Rulemaking (NPRM), OMB-2026-0034, Regulation for Federal Financial Assistance. The scope of this proposed regulation is...

G-protein-coupled receptors (GPCRs) are a large family of membrane proteins that mediate cellular responses to diverse stimuli and serve as targets fo…

G Protein-Coupled Receptors (GPCRs) are important targets for drug discovery owing to their ability to respond to a broad range of stimuli and their involvement in numerous pathologies. Although traditional ligand-based and structure-based approaches have facilitated the development of effective therapeutics for many GPCRs, these approaches often fall short when applied to receptors with limited ligand or structural data. This limitation highlights the critical need for advanced strategies capable of accurately predicting ligand bioactivity across the entire GPCR family, especially for understudied receptor subtypes. In this study, we introduce BOLD-GPCRs (BERT-Optimized Ligand Discovery for GPCRs), a deep learning framework designed to enhance the prediction of ligand bioactivity across class A GPCRs. Accessible via a user-friendly web interface, BOLD-GPCRs employs transfer learning and leverages curated data sets of known class A GPCR ligands, receptor sequences, and signaling-relevant mutations. By integrating dense neural network classifiers with transformer-based protein language models, BOLD-GPCRs captures complex relationships between receptor sequence/function and ligand activity. Our results demonstrate that BOLD-GPCRs achieves robust predictive performance for both ligand bioactivity and mutational effects across a broad range of class A GPCRs, underscoring its potential as a valuable tool for ligand discovery, especially for poorly characterized receptors.

Positive allosteric modulators (PAMs) of the μ opioid receptor (MOR) offer a promising path toward safer opioid therapeutics, yet their mechanisms of action remain poorly understood. Here, we uncover ...

G protein-coupled receptors (GPCRs) are important targets for drug discovery owing to their ability to respond to a broad range of stimuli and their involvement in numerous pathologies. Although tradi...

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For decades, computational theoretical chemistry has provided critical insights into molecular behavior, often anticipating experimental discoveries. This review surveys twenty notable examples from t...

The U.S. government has sought to restrict immigration under the “America First” doctrine. These policies severely harm American science by stripping it of talent and eliminating a major driver of its...

NIH Funding Opportunities and Notices in the NIH Guide for Grants and Contracts: Updated NIH Processes for No-Cost Extensions NOT-OD-25-110. NIH

Nature Communications - Dinoflagellates and cyanobacteria produce saxitoxin (STX) congeners that block voltage-gated sodium channels. Here authors show how amphibians may sequester STX congeners...

Dr. Ardem Patapoutian says he watches “with deep sadness as the United States’ remarkable scientific enterprise, which took generations of hard work and national investment to build, faces a concerted...

The β2-adrenergic receptor (β2AR) is a critical target for the treatment of airway diseases such as asthma or chronic obstructive pulmonary disease (COPD). To better understand its mechanism of actio...

Multiple groups and NIH-funded scientists challenge agency’s decision as arbitrary and capricious

The reasons for selecting these researchers—who have led work on neuronal migration, dopamine receptors in neuronal signaling and the structure of ion channels, among other areas—remain unclear.

I shared a controversial take the other day at an event and I decided to write it down in a longer format: I’m afraid AI won't give us a compressed 21st century.