Project
AA1-10
New Methods for Inhibiting Sars-Cov2 Cell Entry
Project Heads
Frank Noé, Oliver Daumke
Project Members
Peter Bernat Szabo (since 01.09.2021)
Project Duration
01.04.2021 − 31.03.2024
Located at
FU Berlin
Description
We will develop new mathematical and machine learning methods to computationally design drugs inhibiting cell entry of the novel corona virus SARS-CoV-2.
External Website
Related Publications
- Johannes Schimunek et al. “A community effort to discover small molecule SARS-CoV-2 inhibitors”. In: (2023).
- Patrick Bryant and Frank Noe. “Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile”. In: bioRxiv (2023), pp. 2023–07.
- Tuan Le, Frank Noe, and Djork-Arné Clevert. “Representation Learning on Biomolecular Structures using Equivariant Graph Attention”. In: Learning on Graphs Conference. PMLR. 2022, pp. 30–1.
- Andreas Mardt et al. “Deep learning to decompose macromolecules into independent Markovian domains”. In: Nature Communications 13.1 (2022), p. 7101.
- Moritz Hoffmann et al. “Deeptime: a Python library for machine learning dynamical models from time series data”. In: Machine Learning: Science and Technology 3.1 (2021), p. 015009.
- Tim Hempel et al. “Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties”. In: Chemical science 12.38 (2021), pp. 12600–12609.
- Tim Hempel et al. “Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat”. In: Chemical Science 12.3 (2021), pp. 983–992.
- Victoria Callahan et al. “Alpha 1 Antitrypsin is an Inhibitor of the SARS-CoV2-Priming Protease TMPRSS2”. In: (2020).
- Markus Hoffmann et al. “Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity”. In: eBioMedicine 65 (Mar. 2021).
Related Pictures