ArticleJournal of chemical information and modeling2026
SwinSite: 3D Structure-Based Prediction of Protein-Ligand Binding Sites Using a Combined Vision Transformer and Convolution Model.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of protein-ligand binding sites is an essential step in structure-based drug discovery. Herein, we present SwinSite, a deep learning framework that leverages a hybrid architecture combining 3D convolutional neural networks and hierarchical vision transformer modules to predict ligand binding sites based on a 3D structure of a target protein. SwinSite encodes spatial information by voxelizing a protein structure into 3D grids centered around surface residues, allowing for a detailed spatial representation of the protein's surface environment. By combining local feature extraction with hierarchical self-attention via shifted windows, SwinSite effectively captures both fine-grained geometric features and long-range dependencies. Evaluations on multiple benchmark data sets demonstrate that SwinSite outperforms existing CNN- and GNN-based ligand binding site detection methods consistently, highlighting its robustness and generalization ability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.