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.

本研究提出了一种新颖的图形变换器模型 ScoreFormer，用于准确预测分子对接得分，从而优化药物发现中的高通量虚拟筛选。该模型通过集成 Principal Neighborhood Aggregation（PNA）和 Learnable Random Walk Positional Encodings（LRWPE）来增强对复杂分子结构及其与对应对接得分的关系的理解能力。这种方法在化学空间的覆盖范围和性能提升方面显著超过传统的高通量虚拟筛选方法和最新的图神经网络模型，具有竞争力的对接得分预测性能，并相较于现有模型，推理时间减少了 1.65 倍，我们通过在多个数据集下进行评估，在各种条件下验证了 ScoreFormer 的鲁棒性和可靠性，能够快速识别潜在的药物候选物。