TransScore: A Graph Model for Pose Scoring and Affinity Prediction Based on Transformer Convolution Network

Predicting the interaction of protein and compound is an important task in drug discovery. Molecular docking has been a fundamental and vital computer-aid tool for digging potential interaction of the protein-compound pair. With the recent great success of artificial intelligence (AI), the scoring function, as a fundamental part of molecular docking, has been achieving much better performance by incorporating AI-based models. However, the AI-based models usually focus on a single prediction task (e.g., affinity prediction), which is limited by their lack of extensibility. Moreover, the performance of AI-based models usually declines in cold start scenarios, thus compromising the robustness. To this end, we propose a novel deep learning-based graph model based on the transformer convolution network for pose scoring and affinity prediction. TransScore captures the intrinsic characteristics of protein-compound poses by employing the self-attention mechanism, which achieves superior performances in both cold and warm scenarios for the pose-scoring task. The outstanding performance is also shown in imbalanced datasets, which demonstrates the robustness of TransScore. In addition, the gated residual algorithm in TransScore enhances the model to adapt to diverse related tasks. In particular, in the affinity prediction task, we have observed consistent improvements in warm/cold start scenarios. Moreover, it is noticeable that TransScore excels in both accuracy and precision, accurately predicting affinities and their relative ordering. We also conducted an analysis on carbonic anhydrase II, which bears out that TransScore can elaborate the interaction mechanism of the protein-ligand pair, suggesting the potential application of TransScore in drug discovery.