PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening
This work addresses the problem of limited generalization in drug discovery for researchers by providing a more accurate and efficient tool for predicting protein-ligand interactions, though it is incremental as it builds on existing deep learning approaches with specific enhancements.
The paper tackled the challenge of developing a versatile deep learning model for protein-ligand interaction prediction, addressing data scarcity with a novel data augmentation strategy and physics-informed graph neural network, resulting in significant improvements in binding affinity scoring and virtual screening that outperformed task-specific models and achieved results comparable to state-of-the-art methods.
Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target proteins. Despite remarkable advances in deep learning-based PLI prediction, the development of a versatile model capable of accurately scoring binding affinity and conducting efficient virtual screening remains a challenge. The main obstacle in achieving this lies in the scarcity of experimental structure-affinity data, which limits the generalization ability of existing models. Here, we propose a viable solution to address this challenge by introducing a novel data augmentation strategy combined with a physics-informed graph neural network. The model showed significant improvements in both scoring and screening, outperforming task-specific deep learning models in various tests including derivative benchmarks, and notably achieving results comparable to the state-of-the-art performance based on distance likelihood learning. This demonstrates the potential of this approach to drug discovery.