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Dagstuhl Seminar 24451

Machine Learning for Protein-Protein and Protein-Ligand Interactions

( Nov 03 – Nov 08, 2024 )

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Over the past few decades, machine learning (ML) has helped advance progress in a wide range of problems in computational biology and biochemistry, particularly towards understanding the structure and function of proteins. Similarly, in cheminformatics, ML is increasingly influencing pharmaceutical decision making and enabling novel drug design strategies. However, an area of great importance that requires further advances, likely involving significant innovations, is the understanding, prediction, and design of protein-protein and protein-ligand interactions. This Dagstuhl Seminar aims to connect the protein-ML and cheminformatics-ML communities and foster their communication with key experts in biology and chemistry. This seminar will allow us to discuss both theoretical and application-oriented ML topics in the context of protein-protein and protein-ligand interactions. The initial topics for discussion are listed below:

Stream I: Theory and foundations

  • Injecting biological and chemical knowledge as inductive bias into modeling
  • Representation learning for proteins and small molecules
  • Generative modeling for design and prediction

Stream II: Real-world applications

  • Tackling the data availability problem
  • Fundamental training datasets
  • Model performance monitoring
  • Reliable performance benchmarking
  • Efficient interaction between ML experts and biology experts
  • Interpretability and effective reduction of predictions to practice

We consider flexibility to be an important aspect of the seminar organization, and we will not impose a rigid structure. The core of the workshop will be based on discussion and brainstorming sessions, and not on formal conference-style presentations. The program for each day will be determined by collective discussion and by voting on the most interesting topics, which will typically result in splitting the participants into several smaller subgroups based on their interests. Each evening, we will meet with all the participants for a debriefing session, report on the outcomes of individual discussions, and plan the program for the next day. After dinner, we will have less formal concurrent sessions in smaller groups focusing on specific areas that comprise more immediately tractable problems. These evening sessions may also include short overview presentations on specific topics by selected participants.

Copyright Anne-Florence Bitbol, Jennifer Listgarten, Tomas Pluskal, and Christoph Steinbeck

  • Machine Learning
  • Other Computer Science

  • protein
  • ligand
  • molecular interactions
  • biological machine learning
  • generative models