GRIPHIN: grids of pharmacophore interaction fields for affinity prediction

Pharmacophores are widely used to describe protein-ligand interactions. In this work, we propose a hybrid framework for binding affinity prediction that combines pharmacophoric maps of the protein binding site with a graph-based representation of the ligand. Our method achieves performance comparable to state-of-the-art models while offering interpretability through attribution methods, thereby demonstrating the potential of pharmacophoric representations in deep learning.Scientific contributionWe investigate whether a purely pharmacophoric representation of the protein pocket is sufficient to train a deep learning model for affinity prediction. For this purpose, we devise a hybrid model architecture from simple building blocks for affinity prediction. To enhance interpretability, we apply integrated gradients to attribute predictions to individual pharmacophoric features. Source code and model weights are available at https://github.com/molinfo-vienna/GRIPHIN .