A Multi-input Deep Learning Architecture for STAT3 Inhibitor Prediction

Signal transducer and activator of transcription 3 (STAT3) is a critical factor involved in various physiological and oncogenic signaling pathways. Machine learning models are valuable tools for predicting or screening STAT3 inhibitors. However, the predictive performance and interpretability of existing models still require improvement. In this study, we introduce a fingerprint-enhanced graph (FPG) attention network model, which integrates sequence-based fingerprints and structure-based graph representations to predict STAT3 inhibitors. During the feature learning process, the FPG model converts sequence information into a fingerprint vector, while structural information is encoded into a separate vector using a graph attention network module. These two vectors are then concatenated and passed through a multilayer perceptron for molecular activity classification. Among 49 models with various representations and algorithm combinations, the FPG-based model achieved the best predictive performance, with an average area under the curve of 0.897 on the test set. Furthermore, the model outperformed existing prediction models for identifying STAT3 inhibitors. Additionally, fingerprint analysis and attention heatmaps, combined with SHAP algorithms, provided valuable insights into the structure-activity relationship of STAT3 inhibitors, enhancing model interpretability. To facilitate related research and applications, we developed a web service (STAT3 Pro: https://gzliang.cqu.edu.cn/software/Stat3Pro.html) for STAT3 inhibitor prediction.