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Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451)

Authors Anne-Florence Bitbol, Jennifer Listgarten, Tomas Pluskal, Anton Bushuiev, Roman Bushuiev and all authors of the abstracts in this report

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ACM Subject Classification
  • Applied computing → Life and medical sciences
Keywords
  • biological machine learning
  • ligand
  • molecular interactions
  • protein

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Abstract

Dagstuhl Seminar 24451 focused on how machine learning (ML) is revolutionizing computational biology and chemistry by enhancing the prediction and design of protein-protein and protein-ligand interactions. Key topics included integrating biological and chemical knowledge into ML models, addressing data quality and availability issues, and fostering interdisciplinary collaborations. Theoretical discussions explored representation learning, generative models, and protein language models as efficient alternatives to traditional methods. Practical sessions emphasized the importance of experimental constraints in ML workflows and proposed standards for balanced datasets. The seminar concluded by encouraging collaboration between computational and wet-lab researchers, setting the groundwork for future innovations in protein science and drug discovery.

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Anne-Florence Bitbol, Jennifer Listgarten, Tomas Pluskal, Anton Bushuiev, and Roman Bushuiev. Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451). In Dagstuhl Reports, Volume 14, Issue 11, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/DagRep.14.11.1

Author Details

Anne-Florence Bitbol
  • EPFL - Lausanne, CH
Jennifer Listgarten
  • University of California - Berkeley, US
Tomas Pluskal
  • IOCB - Prague, CZ
Anton Bushuiev
  • Czech Technical University - Prague, CZ
Roman Bushuiev
  • The Czech Academy of Sciences - Prague, CZ
and all authors of the abstracts in this report
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