GraphKAN: Graph Kolmogorov Arnold Network for Small Molecule-Protein Interaction Predictions

Tashin Ahmed, Md Habibur Rahman Sifat

Keywords: GraphKAN, KAN, GNN, Activation functions

TL;DR: Graph Kolmogorov Arnold Networks (GraphKAN) for predicting small molecule-protein binding affinities, demonstrating its potential and highlighting the need for further refinement to enhance computational drug discovery.

Abstract: This study presents a proof of concept for utilizing Graph Kolmogorov Arnold Networks (GraphKAN/GKAN) in predicting the binding affinity of small molecules to protein targets. Working with three protein targets, we explored the potential of GraphKAN to infer binding affinities. We compared the performance of GraphKAN with MLP-based graph neural networks, 1D convolutional neural networks (1D CNN), and machine learning algorithms like random forests. Although the model did not achieve state-of-the-art performance, our results demonstrate its feasibility and highlight its promise as a novel approach in computational drug discovery. This work opens new research directions, suggesting that further refinement and exploration of GraphKAN could significantly impact the efficiency and accuracy of binding affinity predictions, ultimately aiding in the discovery of new therapeutic agents. Source code is available at - https://github.com/TashinAhmed/ferroin.

Submission Number: 98