  In this study, we present ScoreFormer, a novel graph transformer model
designed to accurately predict molecular docking scores, thereby optimizing
high-throughput virtual screening (HTVS) in drug discovery. The architecture
integrates Principal Neighborhood Aggregation (PNA) and Learnable Random Walk
Positional Encodings (LRWPE), enhancing the model's ability to understand
complex molecular structures and their relationship with their respective
docking scores. This approach significantly surpasses traditional HTVS methods
and recent Graph Neural Network (GNN) models in both recovery and efficiency
due to a wider coverage of the chemical space and enhanced performance. Our
results demonstrate that ScoreFormer achieves competitive performance in
docking score prediction and offers a substantial 1.65-fold reduction in
inference time compared to existing models. We evaluated ScoreFormer across
multiple datasets under various conditions, confirming its robustness and
reliability in identifying potential drug candidates rapidly.
