Hugo Ceulemans

Johnson & Johnson (United States) — United States

Computer Science · Physical Sciences

36h-index6.4kcitations97works4.42yr avg
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Research Topics

Computational Drug Discovery Methods(39), Machine Learning in Materials Science(16), Cell Image Analysis Techniques(12), Bioinformatics and Genomic Networks(9), Protein Structure and Dynamics(8)

Publications97 total

MolAgent: Biomolecular Property Estimation in the Agentic Era
Journal of Chemical Information and Modeling·2025· 1 cited
VitroBert: modeling DILI by pretraining BERT on in vitro data
Journal of Cheminformatics·2025· 1 citedOpen Access
Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling
Scientific Reports·2025· 1 citedOpen Access
Improving route development using convergent retrosynthesis planning
Journal of Cheminformatics·2025· 5 citedOpen Access
Toward Dose Prediction at Point of Design
Journal of Medicinal Chemistry·2024· 5 cited
Low concentration cell painting images enable the identification of highly potent compounds
Scientific Reports·2024· 1 citedOpen Access
Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss
Lecture notes in computer science·2024Open Access
Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules
Scientific Data·2024· 29 citedOpen Access
Low Concentration Cell Painting Images Enable the Identification of Highly Potent Compounds
Research Square (Research Square)·2024Open Access
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
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
Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data
Chemical Research in Toxicology·2023· 8 citedOpen Access
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Proceedings of the AAAI Conference on Artificial Intelligence·2023· 37 citedOpen Access
Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure–Activity Relationship Models
Chemical Research in Toxicology·2023· 20 citedOpen Access
Conformal efficiency as a metric for comparative model assessment befitting federated learning
Artificial Intelligence in the Life Sciences·2023· 5 citedOpen Access
JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations
2023· 94 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
Atom‐to‐atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
Molecular Informatics·2021· 68 citedOpen Access
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Frequent Co-authors

Mathieu Bollen(17), Jörg K. Wegner(12), Monique Beullens(8), Willy Stalmans(8), Jaak Simm(6), Ádám Arany(6), Vladimir Chupakhin(6), Yves Moreau(6), Ola Engkvist(6), Aleyde Van Eynde(4), Rik Gijsbers(4), Lewis Mervin(4), Noé Sturm(4), Günter Klambauer(3), Marvin Steijaert(3), Sepp Hochreiter(3), Anne E. Carpenter(3), Emmanuel Gustin(3), Koen Van der Borght(3), Steffen Jaensch(3)