Publication entry:

Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework

Full citation: Riedlová, K., Skrhák, V., Gatlin, W. G., Ludwick, M., Turano, L., Novotny, M., Hoksza, D., & Verkhivker, G. M. (2026). Predicting and decoding allosteric binding sites using protein language models and structure-based machine learning: An energy landscape-guided explainable AI framework. Journal of Chemical Theory and Computation.
BibTeX citation file download: riedlova2026predicting.bib

Internal authors

Kamila Reimitz Riedlová

Researcher

Vít Škrhák

PhD student

David Hoksza

Head of department, Associate professor

Research topics

Bioinformatics

Bioinformatics sits on the interface of computer science and molecular biology where it helps to manage, understand and analyze biological and biomedical data. Our research in bioinformatics focuses on the development of software tools applicable mainly in the domain of structural bioinformatics and visualization. These include tools for protein binding site detection, with the application in computational drug discovery, or tools for visualization of the structure of macromolecules. All our methods are implemented as software solutions used by thousands of users all over the world. Most of our tools were first implemented as bachelor or master thesis in our department.