Author

Christian Tyrchan

AstraZeneca (Sweden)ORCID 0000-0002-6470-984XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Assay:protein structure· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Hongtao ZhaoiD· 3 shared Werngard CzechtizkyiD· 3 shared Eva NittingeriD· 2 shared Garegin A. PapoianiD· 1 shared Helena KäckiD· 1 shared Johan Ulander· 1 shared Karolina KwapieńiD· 1 shared Lars TornbergiD· 1 shared

Activity by Year

2021-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Hongtao ZhaoORCID

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Author profile →

4 papers

Recent·Most cited

Enhanced Thompson Sampling by Roulette Wheel Selection for Screening Ultra-Large Combinatorial Libraries

Hongtao Zhao, Eva Nittinger, Melissa A. Yu, Symon M. Gathiaka, W. Patrick Walters, Christian Tyrchan

The paper improves Thompson sampling for screening large chemical libraries by introducing a warmup routine and roulette wheel selection, achieving 100% recovery with 0.1% sampling.

Journal of Cheminformatics · Oct 13, 2025Peer-reviewedBioinformatics

AI-Augmented R-Group Exploration in Medicinal Chemistry

Hongtao Zhao, Karolina Kwapień, Eva Nittinger, Christian Tyrchan, Magnus Nilsson, Susanne Berglund, Werngard Czechtizky

The paper develops an enhanced Free-Wilson QSAR model for R-group exploration using atom-centric pharmacophoric features, showing good predictivity across 12 datasets.

Journal of Chemical Information and Modeling · Feb 17, 2025Peer-reviewedBioinformatics

Efficient Sampling of PROTAC-Induced Ternary Complexes

Hongtao Zhao, Stefan Schießer, Christian Tyrchan, Werngard Czechtizky

The paper introduces a method for efficiently sampling PROTAC-induced ternary complexes, validated on 40 co-crystallized structures with high accuracy and speed.

bioRxiv · Oct 30, 2024 · PreprintMolecular Biology

Protein structure dynamic prediction: a Machine Learning/Molecular Dynamic approach to investigate the protein conformational sampling

Martina Audagnotto, Werngard Czechtizky, Leonardo De Maria, Helena Käck, Garegin A. Papoian, Lars Tornberg, Christian Tyrchan, Johan Ulander

The paper combines deep learning and mechanistic modeling to predict protein conformational ensembles and compares them to experimental MD structures, showing correlations and highlighting areas for improvement in folding and dynamics prediction.

bioRxiv · Dec 1, 2021 · PreprintBioinformatics