@mutny_ml

Machine Learning and Experimental Design @ ETH Zurich @mmutny.bsky.social

 Zurich
mojmirmutny.github.io
Joined February 2020
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Experiment design, Bayesian optimization or Active learning -- all under one umbrella. The advent of self-driving labs is here. We need strategies to implement automatic information gathering! ML models are only as informed as the data they are trained on.
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🚨 Registration & Poster Submission Deadline Approaching! 🚨 Are you interested in how Machine Learning is shaping the future of Biocatalysis? 🧬🤖 Then don’t miss out on the ML for Biocatalysis Workshop in Zurich! Registration & submission deadline is March 3rd.
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We’re excited to welcome registered participants from over 10 countries and 15 leading universities 🏫🎓 💡 Discover how ML & computational tools are advancing enzyme design and engage in meaningful discussions about the challenges at the interface of wet lab and dry lab.
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✨ Featured Speakers: Ditte Welner, Sílvia Osuna, Gustav Oberdorfer, Christian C. Gruber, Markus Jeschek, Juergen Pleiss, Adrian Bunzel, Mattia Gollub, Julian Englert 🔗 Register and submit your poster abstract now! 👉 mlbiocat.github.io/mlbiocat2…
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Are you interested in how machine learning will shape the future of biocatalysis? Then join us for the ML for Biocatalysis Workshop in Zurich! 🧬🤖 Discover how ML & computational tools are advancing enzyme design and discuss challenges at the interface of wet lab and dry lab.
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Featuring exciting talks by Ditte Hededam Welner, Silvia Osuna, Gustav Oberdorfer, Christian Gruber, Markus Jeschek, Jürgen Pleiss, Adrian Bunzel & Mattia Gollub. Register and submit your poster abstract now: mlbiocat.github.io/mlbiocat2…
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Sponsors: NCCR Molecular Systems Engineering, NCCR Catalysis, SNSF, Competence Center for Biocatalysis, Adaptyv Bio, Twist Bioscience, Innophore, and inSEIT. @arkrause @WardGroupBS @rebeccambuller @t_vornholt @JeschekLab
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Meet Heisenberg, the software agent that cloud controls chemputers to make molecules using our ChemputerAGI, safely, on demand, & reproducibly.
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#NeurIPS2024It is my pleasure to share that our work on Transition Constrained Bayesian Optimization has been accepted to NeurIPS2024. Amazing work by coauthors @ Imperial: @folch_pablo @RuthMisener @CalvinTsay ETH: @arkrause, Weronica Ormaniec, and amazing scientists from BASF.
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We apply modern convex reinforcement learning and experiment design for the task of maximum identification with the caveat of transition constrains on the search space. This involves equilibration constrains and/or movement constraints.
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Our solution is provably converging to the algorithm optimally collecting information, and is practically tractable in discrete or continuous domains. We demonstrate the algorithms on sequence on problem from physical sciences such as reactor optimization!
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arxiv.org/abs/2402.08406
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Have you ever wondered how to address diversity and increase hit rate in your ML-driven sequential optimization (directed evolution) of enzymes? Small data? No problem, lets select the most informative one! Check out our work featured on front cover with Tobias (@t_vornholt)!
Tobias Vornholt@t_vornholt25 Jul 2024
Navigating sequence-activity landscapes with AI and automation - great to have our article featured on the cover of @ACSCentSci
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Navigating sequence-activity landscapes with AI and automation - great to have our article featured on the cover of @ACSCentSci
ACS Central Science@ACSCentSci25 Jul 2024
Our NEW issue is now live! Read it here: go.acs.org/ago Front cover story by @JeschekLab, @arkrause, @WardGroupBS & team go.acs.org/agp
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Mojmir Mutny retweeted
ChemCatChem@ChemCatChem22 Nov 2023
An Artificial Metalloenzyme for Atroposelective Metathesis by Thomas R. Ward, Christof Sparr, Markus Jeschek, and co-workers (@WardGroupBS, @christof_sparr, @JeschekLab) onlinelibrary.wiley.com/doi/…
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ML and automation can take protein engineering to a whole new level. In a really exciting collaboration, we have explored new strategies to navigate large sequence-activity landscapes in an efficient yet thorough manner. pubs.acs.org/doi/10.1021/acs…

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Abstract. Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to it

pubs.acs.org
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🌟#ICLR2024 Spotlight🌟 How to learn a policy in an MDP with non-additive rewards, e.g., experiment design, exploration, etc?🤔 Excited to share our paper: Submodular RL for such challenging tasks; where standard RL fails P: openreview.net/pdf?id=loYSzj… W: @mutny_ml, MZ, @arkrause
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The commentary I wrote is now live at Nature Biotech: nature.com/articles/s41587-0… (free version rdcu.be/dwSSy).
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I am at ICLR 2024. Happy to meet familiar and new faces! If you want to talk about AI, active learning, experiment design and self-driving labs, send me a msg. #AI4Science #ICLR2024
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I have graduated ETH Zurich in January, and my thesis on modern adaptive experiment design is finally online research-collection.ethz.ch/… Many thanks to my advisor @arkrause
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