In this section
@article{Li:2026,
author = {Li, K and Xiao, X and Zhong, Z and Yang, G},
journal = {IEEE Open Journal of Engineering in Medicine and Biology},
title = {Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning},
year = {2026}
}
TY - JOUR
AB - Goal: Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. Methods: We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex’s bound and unbound status. Results: Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provids atom-level insights into prediction. Conclusions: This work highlight the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity. Index Terms—Deep learning, drug discovery, physics-informed neural networks, protein-ligand binding affinity prediction. Impact Statement–This study extends state-of-the-art deep learning algorithms to applications in protein-ligand binding affinity prediction. This study has implications for enhancing the generalization capability of protein-ligand interactions prediction methods by interatomic potential modeling.
AU - Li,K
AU - Xiao,X
AU - Zhong,Z
AU - Yang,G
PY - 2026///
SN - 2644-1276
TI - Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
T2 - IEEE Open Journal of Engineering in Medicine and Biology
ER -
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