6 papers
OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
Diogo Soares, Leon Hetzel, Paulina Szymczak +6
Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial pr…
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Johanna Sommer, John Rachwan, Nils Fleischmann +2
Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of network evaluations. This inferen…
OneProt: Towards Multi-Modal Protein Foundation Models
Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer +8
Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OnePro…
Unified Guidance for Geometry-Conditioned Molecular Generation
Sirine Ayadi, Leon Hetzel, Johanna Sommer +2
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative m…
Expressivity and Generalization: Fragment-Biases for Molecular GNNs
Tom Wollschläger, Niklas Kemper, Leon Hetzel +2
Although recent advances in higher-order Graph Neural Networks (GNNs) improve the theoretical expressiveness and molecular property predictive performance, they often fall short of…
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer +2
We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclid…