Cell-to-cell communication through ligand-receptor (LR) interactions can fundamentally shape the tumor microenvironment and immune responses, but the full spectrum of these interactions in anti-PD-1 therapy remains unexplored. We developed a predicti...
Journal of computer-aided molecular design
Oct 24, 2025
Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks ...
BACKGROUND: Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence f...
Journal of the American Chemical Society
Oct 22, 2025
Fragment-Based Drug Discovery (FBDD) is a powerful strategy used in the development of new therapeutics. Molecular fragments are screened against a target protein, where interactions are typically characterized by a low affinity. Nuclear Magnetic Res...
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...
Journal of chemical information and modeling
Oct 14, 2025
Pose prediction of ligands to proteins remains a central challenge of structure-based drug design. Although data leakage and generalizability concerns remain, data-driven methods for pose prediction (i.e., based on deep learning and diffusion) now ro...
G-protein-coupled receptors (GPCRs) are pivotal in cellular signal transduction and serve as key drug targets. Among them, the β-adrenergic receptors (βAR and βAR) regulate cardiovascular function and are activated by endogenous catecholamines, norep...
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...
Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable si...
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