3 citations · 3 across the 3 of their papers we have counts for
8 papers
FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
Julian Cremer, Tuan Le, Mohammad M. Ghahremanpour +3
We present FLOWR.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation with joint potency and binding affinity prediction and confidence estimation.…
FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
Julian Cremer, Ross Irwin, Alessandro Tibo +3
We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching wi…
Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration
Alan Kai Hassen, Andrius Bernatavicius, Antonius P. A. Janssen +3
Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce…
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
Diffusion Generative Modeling on Lie Group Representations
Marco Bertolini, Tuan Le, Djork-Arné Clevert
We introduce a novel class of score-based diffusion processes that operate directly in the representation space of Lie groups. Leveraging the framework of Generalized Score Matchin…
Exact Solutions to the Quantum Schrödinger Bridge Problem
Mykola Bordyuh, Djork-Arné Clevert, Marco Bertolini
The Quantum Schrödinger Bridge Problem (QSBP) describes the evolution of a stochastic process between two arbitrary probability distributions, where the dynamics are governed by t…