Predicting accurate binding affinity is a significant challenge in the early drug discovery and development stages. Researchers possess a comprehensive understanding of both the capabilities and limitations of the existing tools. While they remain a powerful resource, there is a pressing need to develop more robust methods. As a result, in recent times, scientific literature has significantly expanded on AI-based approaches for binding affinity prediction. Such development has paved the way for new techniques that have the potential to address the limitations of traditional methods, whether on their own or combined. Inspired by the growing interest in including the protein counterpart in AI approaches, we developed the InterGraph model, a deep-learning approach to predict binding affinity from protein and ligand complexes. In our implementation, protein-ligand complexes are modeled as 3D topological multigraphs. The design of the protein-ligand multigraph involved a multi-threshold-based approach, which generated interaction spheres around each ligand atom, specifically to enclose proximity-based contacts. The resulting multigraphs were used to train a four-layer graph convolutional neuronal network. Notably, node features, as atom types, were retrieved directly from the PDB files to ensure a consistent and accurate representation of the molecular structure without relying on external atom typing references. Furthermore, InterGraph provides researchers with the flexibility to explore, allowing them to change distance thresholds and node attributes. This adaptability empowers researchers to tailor the model according to specific datasets or research objectives, enhancing its versatility. Our findings highlight the potential of InterGraph in capturing the complex relationships between proteins and ligands, leading to good predictions of binding affinities across multiple protein targets (MAE = 1.34; 𝑆𝜌 = 0.64). Despite being trained on diverse protein families, it exhibited superior accuracy in categories such as Transferase, Hydrolase, Lyase, Chaperone, Transcription, Protein-protein interactions, and Isomerase. This makes it suitable as a pre-trained model that can facilitate reinforcement learning algo-December 15, 2023 rithms in building upon its existing knowledge and fine-tuning it for the specific target categories mentioned above. Finally, we have shown that the model exhibits a remarkable sensitivity to the binding pose, highlighting its ability to discern slight changes in molecular interactions. This feature is of particular significance as it addresses a known limitation in conventional docking scoring functions, offering a more accurate binding affinity prediction. In conclusion, while binding affinity prediction remains an open challenge, InterGraph showed to be a valid approach that embraces the complexity of binding interactions and offers promising results. Moving forward, further advancements, including incorporating more diverse and comprehensive datasets and features, will likely contribute to the refinement and robustness of InterGraph.