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Skittles: GNN-Assisted Pseudo‐Ligands Generation and Its Application for Binding Sites Classification and Affinity Predictionстатья
- Авторы: Evteev Sergei, Ereshchenko Alexey, Adjugim Denis, Vyacheslavov Alexey, Pastukhova Anna, Malyshev Alexander, Terentiev Victor, Ivanenkov Yan
- Журнал: Proteins: Structure, Function and Bioinformatics
- Год издания: 2025
- Издательство: Wiley-Liss Inc
- Местоположение издательства: United States
- DOI: 10.1002/prot.26816
- Аннотация: Nowadays, multiple solutions are known for identifying ligand–protein binding sites. Another important task is labeling each point of a binding site with the appropriate atom type, a process known as pseudo-ligand generation. The number of solutions for pseudo-ligand generation is limited, and, to our knowledge, the influence of machine learning techniques has not been studied previously. Here, we describe Skittles, a new graph neural network-assisted pseudo-ligand generation approach, and compare it with known force-field-based methods. We also demonstrate the application of Skittles-based data for solving several important problems in structural biology, including ligand–protein binding site classification and ligand–protein affinity prediction.
- Добавил в систему: Малышев Александр Сергеевич