AI in Medicine

LigForge: Physics-Informed Diffusion for Structure-Based Drug Design

Authors: Tobias Schopper (Innophore GmbH) , Michael Hetmann , Lena Parigger , Chiara Gasbarri , Christian C. Gruber , Georg Steinkellner

Abstract

Structure-based drug design aims to generate novel small molecules that bind
favorably to a defined protein target. While recent generative models have demon-
strated impressive results, they typically face challenges such as limited specificity,
insufficient diversity, poor physical or chemical validity, weak binding affinity, and
difficulties generalizing beyond the binders and targets seen during training. Here
we present LigForge, a physics-informed pipeline that decouples key competences
to overcome these challenges.

Keywords:

How to Cite: Schopper, T. , Hetmann, M. , Parigger, L. , Gasbarri, C. , Gruber, C. & Steinkellner, G. (2026) “LigForge: Physics-Informed Diffusion for Structure-Based Drug Design”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).