Another future direction is utilising deep learning techniques, such as geometric deep learning ( ), to analyse complex ligand-binding data and generate prediction models to create novel medications. Deep learning enhances virtual screening by identifying novel ligand binding patterns and improving chemical libraries based on known ligands. This approach may aid in discovering compounds that exhibit reduced off-target effects and enhanced efficacy. However, deep learning can mitigate the interpretability of the model, making understanding how the model works harder ( ). Nevertheless, deep learning has critical potential to improve the performance of currently available computational methods.