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approach. Fundamental research focuses on induced pluripotent stem cell (iPSC)-derived neuronal models to elucidate the molecular and cellular alterations contributing to neurodegeneration in familial and
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robustness Building Uncertainty-Aware General Purpose Models Merge developments into a unified, efficient MLFF architecture Train MLFFs on large, diverse datasets across broad chemical space Evaluate models
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Designing hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledge Advancing causal inference methods to predict transformation products
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dynamics and biomolecular condensates contribute to PD co-pathologies in human midbrain assembloid models. The work combines advanced imaging, molecular biology, and functional disease modeling. Key
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datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs