Conferences  >  Chemistry  >  Physical (Theoretical) Chemistry, Computational Chemistry  >  Germany

Machine Learning methods have recently found widespread use in areas of atomistic modelling, mainly focusing on developing surrogate models for the potential energy surface with superior computational efficiency while retaining first principles accuracy. However, approaches to learning observable properties directly are just emerging and are challenged by several issues, which we intend to address in the workshop. The event is meant to support the development of a new collaborative, international network connecting different fields of research and integrating the young researchers community with the help of a scientifically diverse, interactive workshop.

ML of electron density and Hamiltonians, ML of electronic observables, ML of mechanical & magnetic observables, ML of spectroscopic observables, ML of reaction networks, Theoretical and experimental databases

Computational Physics and Numerical Simulation,    Condensed Matter Physics and Materials

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

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.

Molecular Biology,    Biochemistry and Medicinal Chemistry, Toxicology

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)