Combining generative artificial intelligence and on-chip synthesis for de novo drug design
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Abstract
Generative deep learning and miniaturized bench-top synthesis were combined to automate the design of novel LXR agonists.
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A synthesis-on-a-chip procedure was devised in which an AI/ML tool suggested compounds that could be immediately synthesized by constraining compounds with a subset of simple reactions and building blocks.
“For example, by constraining compounds with a subset of simple reactions and building blocks, a “synthesis-on-a-chip” procedure was devised where the AI/ML tool suggested compounds that could be immediately synthesized.161”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [161]
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References 67 · Cited by 190
- Authors
- Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- Goethe University Frankfurt (DE)
- Corresponding author
- Grisoni, Francesca
- Huisman, Berend J. H.
- Merk, Daniel
- Schneider, Gisbert
- ORCID
- Venue
- Science Advances
- Published
- 2021-06-11
- Volume
- 7
- Issue
- 24
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC8195470
- OpenAlex
- W3127493072
- Cited by
- 190
- References
- 67
- Citations by year
- 2026: 36 · 2025: 44 · 2024: 50 · 2023: 26 · 2022: 20 · 2021: 14
- FWCI
- 14.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Science Advances (publishedVersion, cc-by-nc)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, cc-by-nc)
- IRIS (submittedVersion, cc-by-nc)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Funders
- Swiss National Science Foundation (205321_182176)
- Eidgenössische Technische Hochschule Zürich (205321)
- ETH RETHINK
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- de novo drug design, generative AI, microfluidics, liver X receptor agonist, liver X receptor alpha, chemical space exploration
- MeSH terms
- Artificial Intelligence, Drug Design, Drug Discovery
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