Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence est...
Journal of computer-aided molecular design
Dec 3, 2025
The identification of protein target classes is a key step in drug discovery, as it enables prioritization of screening campaigns and supports target-based drug repurpose. In this study, we developed a deep-learning pipeline based on a multilayer per...
Rapid identification of T cell receptors (TCRs) that specifically bind patient-unique neoepitopes is a critical challenge for personalized TCR-based therapies in oncology. Due to enormous diversity of both TCR and neoepitope repertoires, a machine le...
ConspectusCovalent chemical labeling of proteins is central to chemical biology, offering functional modifications beyond imaging probes. While genetically engineered systems using self-labeling tags (e.g., HaloTag, SNAP-Tag) or genetic code expansio...
Journal of chemical theory and computation
Nov 25, 2025
Identifying collective variables (CVs) that are both discriminative and interpretable remains a central challenge for enhanced sampling and mechanistic analysis of biomolecular systems. We present (), a supervised machine learning-based CV discovery...
Physical chemistry chemical physics : PCCP
Nov 19, 2025
Protein-ligand binding affinity prediction plays a crucial role in drug discovery. While recent works use two-dimensional graph neural networks to improve affinity prediction, we find that the three-dimensional geometric information of proteins and l...
Journal of chemical information and modeling
Nov 13, 2025
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for accurate prediction of binding poc...
Proceedings of the National Academy of Sciences of the United States of America
Nov 4, 2025
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of...
Journal of chemical information and modeling
Oct 31, 2025
Accurate in silico prediction of protein-ligand binding affinity is essential for efficient hit identification in large molecular libraries. Commonly used structure-based methods such as docking often fail to rank compounds effectively, and free ener...
LigPCDS (Ligand Point Cloud Data Set) is the first dataset of chemically labeled 3D point clouds of protein ligands. 3D images and structures of ligands were derived from X-ray protein crystallography experimental datasets deposited at the Protein Da...
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