2026-07-24T00:02:59Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22822 2026-04-20T11:49:37Z ddc:004 open_access

Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451) Bitbol, Anne-Florence Listgarten, Jennifer Pluskal, Tomas Bushuiev, Anton Bushuiev, Roman biological machine learning ligand molecular interactions protein 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Anne-Florence Bitbol and Jennifer Listgarten and Tomas Pluskal and Anton Bushuiev and Roman Bushuiev 2025 Is Part Of Dagstuhl Reports, Volume 14, Issue 11 (2025) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.14.11.1 urn:nbn:de:0030-drops-228223 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.11.1 eng https://creativecommons.org/licenses/by/4.0/legalcode