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bioRxiv · 10.1101/2024.04.16.589805

Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Wu, M.-H.·Xie, Z.·Zhi, D.
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Abstract

Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction.

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Wu, M.-H., Xie, Z., Zhi, D.. 2024-04-19. Protein-ligand binding affinity prediction: Is 3D binding pose needed?. https://doi.org/10.1101/2024.04.16.589805

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