Content available in repository
Content available in repository
Personal profile
Research profile
Sarah de Ruiter is a PhD Candidate in the Molecular Machine Learning group in the Department of Biomedical Engineering. Her research focuses on infusing chemical knowledge and intuition into deep learning models to advance drug design methodologies.
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Collaborations and top research areas from the last five years
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Research output
- 2 Article
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peptidy: a light-weight Python library for peptide representation in machine learning
Özçelik, R. (Corresponding author), van Weesep, L., de Ruiter, S. & Grisoni, F. (Corresponding author), 21 Mar 2025, In: Bioinformatics Advances. 5, 1, 4 p., vbaf058.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile3 Link opens in a new tab Citations (Scopus)86 Downloads (Pure) -
Chemical language modeling with structured state space sequence models
Özçelik, R., de Ruiter, S., Criscuolo, E. & Grisoni, F. (Corresponding author), 22 Jul 2024, In: Nature Communications. 15, 1, 12 p., 6176.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile48 Link opens in a new tab Citations (Scopus)168 Downloads (Pure)
Datasets
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Supporting material and code for "Chemical Language Modeling with Structured State Space Models"
Özçelik, R. (Contributor), de Ruiter, S. (Contributor), Criscuolo, E. (Contributor) & Grisoni, F. (Contributor), Zenodo, 5 Jul 2024
DOI: 10.5281/zenodo.12666371, https://zenodo.org/records/12666371
Dataset
Thesis
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Structured state space models for de novo drug design
de Ruiter, S. (Author), Özçelik, R. (Supervisor 1) & Grisoni, F. (Supervisor 2), Feb 2023Student thesis: Bachelor
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