A Multi-input Deep Learning Architecture for STAT3 Inhibitor Prediction.

ABSTRACT: 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 p

SUBMITTER: Liang K 

PROVIDER: S-EPMC12489654 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature