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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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

Kairui Liang^1,2, Wenling Qin³, Yonghong Zhang¹

¹Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.

|October 6, 2025

Summary

This summary is machine-generated.

We developed a novel machine learning model that accurately predicts Signal transducer and activator of transcription 3 (STAT3) inhibitors. This advanced model enhances prediction accuracy and interpretability for drug discovery.

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Area of Science:

Computational Chemistry
Machine Learning
Drug Discovery

Background:

Signal transducer and activator of transcription 3 (STAT3) is crucial in physiological and oncogenic pathways.
Existing machine learning models for STAT3 inhibitor prediction need improved performance and interpretability.

Purpose of the Study:

To develop an advanced machine learning model for predicting STAT3 inhibitors.
To enhance the predictive performance and interpretability of STAT3 inhibitor screening.

Main Methods:

Introduced a fingerprint-enhanced graph (FPG) attention network model.
Integrated sequence-based fingerprints and graph attention networks for feature learning.
Utilized multilayer perceptron for molecular activity classification.

Main Results:

The FPG model achieved the best predictive performance (AUC=0.897) among 49 tested models.
Outperformed existing prediction models in identifying STAT3 inhibitors.
SHAP algorithms and attention heatmaps enhanced model interpretability by revealing structure-activity relationships.

Conclusions:

The FPG model offers superior predictive accuracy and interpretability for STAT3 inhibitor discovery.
The developed web service (STAT3 Pro) facilitates further research and application of STAT3 inhibitor prediction.