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

Debby D. Wang, Mohammad Sadegh Taghizadeh, Yuting Huang

Abstract: 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.

External IDs:doi:10.1371/journal.pcbi.1013074