Tim Hempel
Tim Hempel is a research software engineer who specializes in AI for computational molecular biology and coauthored BioEmu, a Microsoft Research AI for Science deep-learning model of protein structure ensembles. In 2021, at Freie Universität Berlin, he was first author of a PNAS paper on independent Markov decomposition and of a paper on the drug candidate otamixaban against SARS-CoV-2 cell entry. A 2022 Nature Communications paper with Andreas Mardt, Cecilia Clementi and Frank Noé joined that decomposition with VAMPnets, a deep learning approach to Markov modeling. BioEmu-1 became open source in February 2025, and the BioEmu paper appeared in Science in July 2025 with Hempel as second author. He is a Senior Research Software Development Engineer at Microsoft AI for Science in Berlin.
Timeline
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2021
Independent Markov decomposition
In 2021, he is first author of a PNAS paper that introduces independent Markov decomposition, a method that uses weak coupling between subsystems to compute a global kinetic model without sampling every combination of subsystem states. His affiliations are the Department of Mathematics and Computer Science and the Department of Physics at Freie Universität Berlin.
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August 2021
Otamixaban against SARS-CoV-2 entry
In August 2021, he is first author, with Katarina Elez, Nadine Krüger and others, of a paper showing that the late-stage drug candidate otamixaban inhibits SARS-CoV-2 cell entry and suppresses TMPRSS2 activity in a human lung cell line.
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October 2021
The Deeptime Python library
In October 2021, he is a coauthor, with Moritz Hoffmann, Martin Scherer, Frank Noé and others, of Deeptime, a Python library for machine learning dynamical models from time series data. The library offers Markov state models, Hidden Markov Models and Koopman models.
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November 2022
Learning independent Markovian domains
In November 2022, he and Andreas Mardt, Cecilia Clementi and Frank Noé publish the Nature Communications paper "Deep learning to decompose macromolecules into independent Markovian domains". It combines independent Markov decomposition with VAMPnets, a deep learning approach to Markov modeling, and Hempel designs the method and develops the test systems.
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February 2025
BioEmu-1 goes open source
In February 2025, he coauthors the Microsoft Research blog post that introduces BioEmu-1 as open source, signing as a Senior Research Software Development Engineer. The deep-learning model generates thousands of protein structures per hour on a single graphics processing unit.
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July 2025
BioEmu in Science
In July 2025, Science publishes "Scalable emulation of protein equilibrium ensembles with generative deep learning", on which Hempel is second author after Sarah Lewis. BioEmu emulates protein equilibrium ensembles by generating thousands of statistically independent structures per hour on a single GPU.
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July 2025
A broad coronavirus inhibitor
In July 2025, he is second author, after Katarina Elez, of a Nature Communications paper that combines molecular dynamics simulations with active learning to screen drug candidates. The framework cuts computational costs about 29-fold and finds BMS-262084, a TMPRSS2 inhibitor with an IC50 of 1.82 nM.
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As of October 2026
Engineer at AI for Science
He works as a Senior Research Software Development Engineer at Microsoft AI for Science in Berlin, specializing in AI for computational molecular biology with a strong emphasis on training data.
Career
- NowMicrosoftSenior Research Software Development Engineer
- Until 2021Freie Universität Berlin
Sources and credits
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- Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes
- Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties
- Deeptime: a Python library for machine learning dynamical models from time series data
- Deep learning to decompose macromolecules into independent Markovian domains
- Exploring the structural changes driving protein function with BioEmu-1
- Scalable emulation of protein equilibrium ensembles with generative deep learning
- Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor
- Tim Hempel at Microsoft Research

