Marian Novotný

Marian is the head of the biology branch of the group. Marian is interested in using 3D structural information to understand cellular processes and their evolution. Marian was involved in a 3D structural prediction, a development of methods for structure validation (accessible surface area-calculation precision) and a development of methods for structure-based function prediction (left-handed helices) .

David Hoksza

Faculty of Mathematics and Physics, Associate Professor

David is the head of the computer science branch of the group. He is interested in the development of efficient algorithms in the area of structural bioinformatics and data visualization. He has been involved in projects dealing mostly with protein and RNA structure with occasional excursions to the fields of cheminformatics (ligand-based virtual screening, exploration of chemical space), computational genomics (analysis of MinION data) and systems biology (visualization and analysis of molecular networks).

Petr Škoda

Petr, an assistant professor at Charles University, is a researcher and a developer. He focuses mainly on fields of similarity modelling, ligand-based virtual screening, data transformation, and linked data. Petr is an open-source contributor to projects such as LinkedPipes ETL, DCAT-AP Viewer, and p2rank web interface.

Kamila Riedlová

Kamila, a postdoctoral researcher, has an education in molecular dynamics simulations. She has performed both all-atom and coarse-grained simulations of potential new drugs with model lipid membranes and monolayers. Her expertise is interdisciplinary - she has studied molecular biology and biochemistry of organisms along with general chemistry. Her PhD is in the modeling of chemical nano- and biostructures, a field that falls under physical chemistry. Currently, she is focusing on docking and the integration of MD simulations in relation to machine learning for bioinformatics purposes.

Radoslav Krivák

Radoslav has a background in theoretical computer science and machine learning. His research interests include applying machine learning to analyze and map protein surfaces and exploring protein and chemical structural spaces. Radoslav is the main author of P2Rank, ligand-binding site prediction software.

Christos Feidakis

Using informatics in order to make sense out of biological data, specifically structure-related data and processes, such as the interactions of proteins with other proteins and smaller molecules.
Interests: Structural bioinformatics, comparative modeling, evolution, in-silico drug design, Python

Jan Jelínek

Jan is a PhD. student in the computer science branch of the group. He focuses on machine learning and structural bioinformatics. His main research area is a protein structure prediction, however he is also involved in a projects dealing with protein structure identification using mass spectroscopy, RNA structure prediction, and statistical analysis of Ribo-Seq data.

Hamza Gamouh

A PhD student in computer science with a background in artificial intelligence and machine learning. Main focus is on the applying machine learning for detecting protein interaction sites. Main research areas include protein-ligand binding sites prediction, cryptic binding sites, protein-protein interaction sites and protein function prediction.

Vít Škrhák

Vít is a researcheer with a background in software engineering. His current research focus revolves around the development of innovative tools for exploring conformation changes in protein structures. Moreover, he also finds interest in exploiting gene mutation data to estimate potential functionality changes in proteins.

Dávid Jakubec

David is a postdoctoral researcher in the computer science branch of the group. His main expertise lies in molecular modelling and computational chemistry, with additional background in molecular biology and biochemistry. David is primarily interested in the nature of forces driving molecular interactions and recognition. He is further interested in molecular evolution and has been developing tools for its computational study.

Andrea Šoltésová

Andrea is a postdoctoral researcher in the computer science branch of the group. Main expertise of Andrea Šoltésová lies in the field of computational biology, machine learning and data mining in general. Her main research area is in structural bioinformatics. Her research interest focuses on predictive modelling of protein-DNA interaction through machine learning.