3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…