Protein-ligand interactions are critical in numerous biological processes, and understanding how their binding influences biological processes is at the core of drug discovery and development. Binding affinity is a thermodynamic property that describes the strength of such interactions, providing valuable insights into the likelihood of successful binding and the potency of a drug candidate . Since experimental determination of the binding affinity is costly and time-consuming, computational methods are largely adopted because they can successfully prioritize drug candidates from extensive compound libraries more efficiently, accelerating drug discovery and development. Free energy calculations based on molecular dynamics are often employed; however, they come with a high computational cost, requiring substantial time to obtain reliable results . The greatest challenge lies in the intricate interplay between protein and ligand molecules, involving diverse molecular interactions and conformational changes, which limit the achievement of precise predictions . Therefore, overcoming these challenges and developing efficient computational methods for accurate binding affinity prediction remains an active area of research . Graph convolutional neural network (GCN) architectures have recently gained significant traction in the field . Researchers have achieved exciting results by capitalizing on the inherent graph structure and the powerful learning capabilities of GCNs . This progress underscores the versatility and potential of GCN architectures in pushing the boundaries of predictive modeling in molecular sciences . Nevertheless, integrating the three-dimensional (3D) structure of the protein-ligand system into GCNs may not always be straightforward.