arXiv:2606.14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery. Recent geometric deep learning methods have achieved promising performance by representing protein-ligand complexes as three-dimensional graphs. However, most existing approaches mainly rely on static interaction geometry from a single bound conformation, while neglecting molecular flexibility and binding-induced conformational changes. To address this limitation, we propose a curvature-informed potential energy surface (CPES) graph neural netw
The convergence of advanced geometric deep learning, computational power, and increasing demand for efficient drug discovery pushes innovation in protein-ligand binding prediction.
Accurate prediction of protein-ligand binding affinity is a fundamental bottleneck in drug discovery, and improvements can significantly accelerate the development of new therapeutics.
This new method, by incorporating molecular flexibility and conformational changes via a curvature-informed potential energy surface, improves upon previous static geometric deep learning approaches.
- · Pharmaceutical industry
- · Biotech startups
- · AI in drug discovery
- · Patients
- · Traditional drug screening methods
- · Drug discovery companies relying solely on static models
Faster and cheaper drug discovery pipelines.
An increase in successfully developed and approved drugs across various disease areas.
Enhanced ability to design highly specific drugs with fewer side effects, enabling personalized medicine.
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Read at arXiv cs.LG