Author

Michaël Richard

University of CopenhagenORCID 0000-0002-6264-7323Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:machine learning (classical)· 2 Assay:protein sequence· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Approach:statistical model· 1

Frequent Co-Authors

Wouter BoomsmaiD· 3 shared Jesper SalomoniD· 2 shared Nikos S. HatzakisiD· 2 shared Pengfei TianiD· 2 shared Peter Mørch GrothiD· 2 shared Jacob Kæstel‐HanseniD· 1 shared Marcus Winther DreisleriD· 1 shared Simon BartelsiD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 23 mutual co-author links

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3 shared papersBioinformatics

Wouter BoomsmaORCID

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3 papers

Recent·Most cited

APOSM: Pairwise preference learning improves generative small-molecule design

Marcus Winther Dreisler, Michaël Richard, Nikos S. Hatzakis, Wouter Boomsma

APOSM improves generative small-molecule design by using pairwise preference learning to enhance surrogate model reliability and active candidate selection.

bioRxiv · Jun 10, 2026 · PreprintBioinformatics

FLOP: Tasks for Fitness Landscapes Of Protein wildtypes

Peter Mørch Groth, Michaël Richard, Jesper Salomon, Pengfei Tian, Wouter Boomsma

The paper curates a benchmark dataset for exploring protein fitness landscapes using wildtype sequences and evaluates different representation paradigms for their predictive power.

bioRxiv · Jun 21, 2023 · PreprintBioinformatics

Assessing the performance of protein regression models

Michaël Richard, Jacob Kæstel‐Hansen, Peter Mørch Groth, Simon Bartels, Jesper Salomon, Pengfei Tian, Nikos S. Hatzakis, Wouter Boomsma

This paper evaluates how different performance metrics and generalization definitions affect the assessment of protein regression models.

bioRxiv · Jun 18, 2023 · PreprintBioinformatics