Ansgar Schuffenhauer

Novartis (Switzerland) — Switzerland

Computer Science · Physical Sciences

34h-index7.0kcitations94works0.02yr avg
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Research Topics

Computational Drug Discovery Methods(62), Microbial Natural Products and Biosynthesis(18), Analytical Chemistry and Chromatography(13), Chemical Synthesis and Analysis(13), Asymmetric Synthesis and Catalysis(11)

Publications94 total

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information
Journal of Chemical Information and Modeling·2023· 94 citedOpen Access
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Proceedings of the AAAI Conference on Artificial Intelligence·2023· 37 citedOpen Access
Discovery of New Binders for DCAF1, an Emerging Ligase Target in the Targeted Protein Degradation Field
ACS Medicinal Chemistry Letters·2023· 32 citedOpen Access
Conformal efficiency as a metric for comparative model assessment befitting federated learning
Artificial Intelligence in the Life Sciences·2023· 5 citedOpen Access
Industry-Scale Orchestrated Federated Learning for Drug Discovery
arXiv (Cornell University)·2022· 10 citedOpen Access
MELLODDY: cross pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information
2022· 24 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
Journal of Cheminformatics·2021· 57 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021· 2 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021Open Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021· 1 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021· 3 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021· 4 citedOpen Access
Splitting chemical structure data sets for federated privacy-preserving machine learning
2021Open Access
Discovery of Potent, Highly Selective, and <i>In Vivo</i> Efficacious, Allosteric MALT1 Inhibitors by Iterative Scaffold Morphing
Journal of Medicinal Chemistry·2020· 26 citedOpen Access
Evolution of Novartis’ Small Molecule Screening Deck Design
Journal of Medicinal Chemistry·2020· 77 cited
Systematic Chemogenetic Library Assembly
Cell chemical biology·2020· 55 citedOpen Access
Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream targets and downstream phenotypes
Scientific Reports·2020· 8 citedOpen Access
Systematic Chemogenetic Library Assembly
2020· 9 citedOpen Access
Outline for a Perspective Article on Novartis’ approach to compound screening for hit and lead finding and the design of compounds sets for diversity driven screening
The Novartis Repository (Novartis)·2020
Gene-signatures predict biologically relevant dose-response potencies in phenotypic assays
2019Open Access
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Frequent Co-authors

Peter Ertl(16), Edgar Jacoby(15), Pierre Acklin(8), Khalil Azzaoui(6), Peter Willett(5), Paul M. Selzer(5), Lutz F. Tietze(5), Stefan Wetzel(4), Herbert Waldmann(4), Jérôme Hert(4), David J. Wilton(4), Steffen Renner(4), Wouter Heyndrickx(4), Noé Sturm(4), Adam Zalewski(4), Jaak Simm(4), Yves Moreau(4), Thibault Varin(4), Ulrich Schopfer(4), Silvio Roggo(3)