A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening

Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the identification and enhancement of molecules that effectively bind to target proteins. Despite recent advances in deep learning-based PLI prediction, developing a versatile model capable of accurate binding affinity scoring and virtual screening in PLI prediction is an ongoing challenge. This is primarily due to the lack of structure--affinity data, resulting in low model generalization ability. We here propose a viable solution to this challenge by introducing a novel data augmentation strategy along with a physics-informed neural network. The resulting model exhibits significant improvement in both scoring and screening capabilities. Its performance was compared to task-specific deep learning-based PLI prediction models, confirming its versatility. Notably, it even outperformed computationally expensive molecular dynamics simulations as well as the other deep learning models in a derivative benchmark while maintaining sufficiently high performance in virtual screening. This underscores the potential of this approach in drug discovery, demonstrating its applicability to both binding affinity scoring and virtual screening.