Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency

With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem that heavily relates to computational drug discovery. Aiming at this problem, we have proposed an efficient scoring framework using deep learning techniques. This framework describes a binding structure by a high-resolution atomic graph, places a focus on the inter-molecular interactions and learns the graph in a rational way. For a protein-ligand binding complex, the generated atomic graph reserves key information of the atoms (as graph nodes), and focuses on inter-molecular interactions (as graph edges) that can be identified by introducing multiple distance ranges to the atom pairs within the binding area. To provide more confidence in the predicted binding strengths, we have interpreted the deep learning model from the model level and in a post-hoc analysis. The proposed learning framework has been demonstrated to have competitive performances in scoring and screening tasks, which will prospectively promote the development of related fields further.Author summary: The binding between a small compound (ligand) and a protein plays a crucial role in many biological processes, such as signal transduction and immunoreaction. Particularly, a small-molecule drug can bind to a target protein to modulate its signaling pathways and suppress the progression of the associated disease. Apparently, the binding strength is a key indicator for evaluating how well such small-molecule drugs work, therefore becoming a core topic in computational drug discovery. Nowadays, the binding structure of a ligand and its target protein can be resolved experimentally or modeled computationally, while the accurate scoring of such a binding structure (predicting the binding strength) still remains a challenge. An effort has been put into the development of benchmark databases that provide a variety of protein-ligand binding structures and their experimentally resolved binding strengths, leading to increasing deep learning applications in this field. In this study, we represent a protein-ligand binding structure as a graph, with the atoms as nodes and the inter-molecular interactions as edges. A light but efficient deep learning architecture has been adopted for learning such graphs and outputting the binding strengths. Validated by our experiments, the model performs well in both scoring and screening tasks.