The problem of IC50 prediction for ligand-protein pairs using Transformer architecture under limited resources

• Datum: 2026
• Hauptverfasser: Krysenko, Pavlo, Bektimirov, Alim
• Format: Artikel
• Sprache: Ukrainisch
• Veröffentlicht: Kyiv National University of Construction and Architecture 2026
• Schlagworte: deep learning; ligand; protein; bioinformatics; convolutional neural networks; data compression
• Online Zugang: https://es-journal.in.ua/article/view/365080
• Назва журналу: Environmental safety and natural resources

The article discusses approaches to optimizing the training of models for predicting the half-maximal inhibitory concentration (IC50) of ligand-protein pairs under limited computational resources. A method of smart bucketing of data by protein length with a dynamic selection of the number of groups...