Jaak Simm

KU Leuven — Belgium

Computer Science · Physical Sciences

26h-index2.9kcitations110works2.02yr avg
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Research Topics

Computational Drug Discovery Methods(26), Bioinformatics and Genomic Networks(12), Advanced Graph Neural Networks(10), Gene expression and cancer classification(8), Machine Learning and Algorithms(8)

Publications109 total

Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations
npj Digital Medicine·2025· 3 citedOpen Access
Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations
2024· 1 citedOpen Access
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
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
Conformal efficiency as a metric for comparative model assessment befitting federated learning
2022· 4 citedOpen Access
SparseChem: Fast and accurate machine learning model for small molecules
arXiv (Cornell University)·2022· 6 citedOpen Access
Expressive Graph Informer Networks
Lecture notes in computer science·2022· 1 citedOpen Access
Self-labeling of Fully Mediating Representations by Graph Alignment
Communications in computer and information science·2022Open 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
MELLODDY TUNER release v1 public data
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
MELLODDY TUNER release v1 public data
Figshare·2021· 6 citedOpen Access
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Frequent Co-authors

Yves Moreau(25), Ádám Arany(16), Madis Metsis(9), Hugo Ceulemans(8), Andres Salumets(7), Martijn Oldenhof(6), Noé Sturm(5), Lukas Friedrich(5), Adam Zalewski(5), Ansgar Schuffenhauer(5), Masashi Sugiyama(5), Pooya Zakeri(5), Tiina Drell(4), Anu Aaspõllu(4), Jörg K. Wegner(4), Edward De Brouwer(4), Wouter Heyndrickx(4), Lewis Mervin(4), Ritsuya Niwayama(4), Peter Schmidtke(4)