The problem of IC50 prediction for ligand-protein pairs using Transformer architecture under limited resources
DOI:
https://doi.org/10.32347/2411-4049.2026.2.287-292Keywords:
deep learning, ligand, protein, bioinformatics, convolutional neural networks, data compressionAbstract
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.
References
Zoziuk, M., Krysenko, P., Dovgiy, S., Makarov, V., Yakimenko, Y., & Koroliouk, D. (2026, April 10). Transformer with BPE tokenization for analysis of interactions of chemical substances and proteins. Computational Methods and Mathematical Modeling in Cyberphysics and Engineering Applications 2, pp. 289–299. doi:10.1002/9781394454518.ch9
Öztürk, H., Özgür, A., & Ozkirimli, E. (2018). DeepDTA: deep drug-target binding affinity prediction. Bioinformatics (Oxford, England), 34(17), i821–i829. doi:10.1093/bioinformatics/bty593
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2022). Efficient Transformers: A Survey. ACM Comput. Surv., 55(6). doi:10.1145/3530811
Huang, K., Fu, T., Glass, L. M., Zitnik, M., Xiao, C., & Sun, J. (2021). DeepPurpose: a deep learning library for drug-target interaction prediction. Bioinformatics (Oxford, England), 36(22–23), 5545–5547. doi:10.1093/bioinformatics/btaa1005
Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization. doi:10.48550/arXiv.1711.05101
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 П. Крисенко, А. Бектімиров
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal «Environmental safety and natural resources» works under Creative Commons Attribution 4.0 International (CC BY 4.0).
The licensing policy is compatible with the overwhelming majority of open access and archiving policies.
