LigForge: Physics-Informed Diffusion for Structure-Based Drug Design
Abstract
Structure-based drug design aims to generate novel small molecules that bind favorably to a defined protein target. While recent generative models have demonstrated 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.
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), 48-49. doi: https://doi.org/10.34749/3061-1466.2026.9
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