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

• Date: 2026
• Author Affiliations: Pavlo Krysenko — Доктор філософії, молодший науковий співробітник, Інститут телекомунікацій і глобального інформаційного простору НАН України, Київ; Alim Bektimirov — Провідний інженер, Інститут телекомунікацій і глобального інформаційного простору НАН України, Київ
• Keywords: keywords
• Main Authors: Krysenko, Pavlo, Bektimirov, Alim
• Format: Article
• Language: Ukrainian
• Published: Kyiv National University of Construction and Architecture 2026
• Subjects: deep learning; ligand; protein; bioinformatics; convolutional neural networks; data compression
• Online Access: https://es-journal.in.ua/article/view/365080
• Journal Title: 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 to improve randomization is proposed. To solve the problem of the quadratic complexity of the Transformer architecture, a convolution layer was used to compress the input data. Based on 4 conducted experiments, the relationship between the degree of sequence compression and the obtained root mean square error (RMSE) for lgIC50 was analyzed.