2 citations · 2 across the 7 of their papers we have counts for
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Bayesian Optimization for General Reaction Conditions
Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser +6
General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and hi…
Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
Malte Franke, Stefan P. Schmid, Zarko Ivkovic +2
The success of generative molecular design hinges on a model's steerability toward high-reward samples. Because many molecular properties are intrinsically linked to molecular size…
Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger +2
Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g., mole…
Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data
Bernardo Fichera, Zarko Ivkovic, Kjell Jorner +2
Uncertainty quantification for image data is dominated by complex deep learning methods, yet the field lacks an interpretable, mathematically grounded baseline. We propose Bayesian…
Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates
Luca Schaufelberger, Aline Hartgers, Kjell Jorner
Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their…