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cs.LG2025

FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble

Riccardo Tedoldi, Ola Engkvist, Patrick Bryant +3

Sampling useful three-dimensional molecular structures along with their most favorable conformations is a key challenge in drug discovery. Current state-of-the-art 3D de-novo desig…

cs.LG2025

A Non-Adversarial Approach to Idempotent Generative Modelling

Mohammed Al-Jaff, Giovanni Luca Marchetti, Michael C Welle +5

Idempotent Generative Networks (IGNs) are deep generative models that also function as local data manifold projectors, mapping arbitrary inputs back onto the manifold. They are tra…

cs.LG2025

Energy-Based Flow Matching for Generating 3D Molecular Structure

Wenyin Zhou, Christopher Iliffe Sprague, Vsevolod Viliuga +3

Molecular structure generation is a fundamental problem that involves determining the 3D positions of molecules' constituents. It has crucial biological applications, such as molec…

cs.LG2024

Hessian-Informed Flow Matching

Christopher Iliffe Sprague, Arne Elofsson, Hossein Azizpour

Modeling complex systems that evolve toward equilibrium distributions is important in various physical applications, including molecular dynamics and robotic control. These systems…

cs.LG2024

Stable Autonomous Flow Matching

Christopher Iliffe Sprague, Arne Elofsson, Hossein Azizpour

In contexts where data samples represent a physically stable state, it is often assumed that the data points represent the local minima of an energy landscape. In control theory, i…