Dresden 2026 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 15: Computational Biophysics III
BP 15.6: Vortrag
Mittwoch, 11. März 2026, 11:15–11:30, BAR/SCHÖ
From Data to Discovery: Machine Learning Force Fields for Fast and Accurate Ligand-Protein Screening — •Sergio Suárez-Dou1, Miguel Gallegos1, Hamza Ibrahim2, Joshua T. Berryman1, Andrea Volkamer2, and Alexandre Tkatchenko1 — 1Department of Physics and Materials Science, University of Luxembourg, Luxembourg — 2Data Driven Drug Design, Center for Bioinformatics, Saarland University, Germany
Pretrained Machine Learning Force Fields (MLFF) are transforming computational chemistry by combining speed and accuracy. In drug discovery, predicting binding energies and affinities is key to identifying viable candidates. Among MLFFs, SO3LR excels in both performance and efficiency, enabling accurate ligand binding predictions at low computational cost, surpassing semiempirical methods.
However, data coverage remains a challenge. While datasets like OMol25 and QCell offer broad molecular diversity, drug-like chemical space requires more targeted representation. To address this, we are developing a dataset optimised for docking evaluations, covering key regions of drug-like space. Our goal is a pretrained model capable of predicting drug-ligand affinities with PBE0+MBD DFT-level accuracy, bridging quantum precision with high-throughput screening and accelerating drug discovery.
Keywords: Machine Learning Force Fields (MLFF); Drug discovery; Binding Affinity; Drug-like Chemical Space