Gisbert Schneider
Full Professor
Researches computer-assisted drug design, using generative deep learning and chemical language models for de novo molecular design.
Gisbert Schneider's lab at ETH Zurich works on proteins, drugs & materials and large language models. 26 papers since 2023, 4 at top venues, mostly Nature Communications and Nature Machine Intelligence.
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 15%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 1
- 11 papers2024 · T1 2
- 9 papers2023 · T1 1
T1 venuesOtherVenues: Nature Communications, Nature Machine Intelligence, Nucleic Acids Research
Research focuspaper titles that use each topic's words
- Proteins, drugs & materials9 papers
- design5 papers
- chemical3 papers
- Large language models1 paper
- molecular design1 paper
Paper list
From the lab homepage.
20261 paper
20255 papers
- Chem. Eur. J. , e202404517Discovery of 1,3,4-oxadiazin-5-one derivative CJ1-34 as a partial ATP synthase inhibitor for CNS applications (opens in a new tab)
- Nature Communications (T1)Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-objective molecular optimization (opens in a new tab)
- TECH JournalMedikamente der Zukunft mit KI
- Nucleic Acids ResearchSelective inhibition of Mycobacterium tuberculosis GpsI unveils a novel strategy to target the RNA metabolism (opens in a new tab)
- Mol. Inf. 44, e202400361Simple user-friendly reaction format (opens in a new tab)
202411 papers
- Alzheimer's Dement. 10, e12445Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer’s disease (opens in a new tab)
- RSC Advances 14, 37035-37044Combining de novo molecular design with semiempirical protein–ligand binding free energy calculation (opens in a new tab)
- Nat. Chem. 16, 239-248Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (opens in a new tab)
- Chimia 78, 499-512Enhancing drug discovery and development through the integration of medicinal chemistry, chemical biology, and academia-industry partnerships: Insights from Roche’s endocannabinoid system projects (opens in a new tab)
- RSC Advances 14, 4492-4502Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning (opens in a new tab)
- RSC Med. Chem. 15, 2310-2321Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry (opens in a new tab)
- Nat. Rev. Drug Discov. 23, 141-155Integrating QSAR modelling and deep learning in drug discovery: The emergence of deep QSAR (opens in a new tab)
- Nature Machine Intelligence (T1)Neural multi-task learning in drug design (opens in a new tab)
- Nature Communications (T1)Prospective deep interactome learning for de novo drug design (opens in a new tab)
- Mol. Inf. , 202400205Protein binding site representation in latent space (opens in a new tab)
- ChemBioChem 25, e202400095Task-similarity is a crucial factor for few-shot meta-learning of structure-activity relationships (opens in a new tab)
20239 papers
- Biochem. Pharmacol. 115504Allosteric targeting resolves limitations of earlier LFA-1 directed modalities (opens in a new tab)
- Mol. Inf. 42, 202300059Compression of molecular fingerprints with autoencoder networks (opens in a new tab)
- Nat. Comput. Sci. 3, 922-933Designing molecules with autoencoder networks (opens in a new tab)
- Commun. Chem. 6Identifying opportunities for late-stage C-H alkylation with in silico reaction screening and high-throughput experimentation (opens in a new tab)
- Nature Communications (T1)Leveraging molecular structure and bioactivity with chemical language models for drug design (opens in a new tab)
- ACS Omega 8Machine learning for fast, quantum mechanics-based approximation of drug lipophilicity (opens in a new tab)
- Chem. Data Collect. 46, 101040QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets (opens in a new tab)
- Cell Oncol. 46, 331-356.Santhana Kumar, K., Brunner, C., Schuster, M., Kopp, L. L., Gries, A., Yan, S., Jurt, S., Moehle, K., Bruns, D., Grotzer, M., Zerbe, O., Schneider, G. & Baumgartner, M. Discovery of a small molecule ligand of FRS2 that inhibits invasion and tumor growth (opens in a new tab)
- Curr. Opin. Struct. Biol. 79, 102548.Structure-based drug design with geometric deep learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ETH ZurichRolling
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Computer Science Doctorate (D-INFK)
- Round
- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
- See program page
Confirmed on the program page.
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