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Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property

Haolin Zhan¹, Xin Zhu², Zhiwei Qiao³

¹Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China; College of Economics and Statistics, Guangzhou University, Guangzhou, China.

Analytica Chimica Acta

|February 3, 2023

Summary

This study introduces a new framework using Graph Neural Networks and a Learning Binary Neural Tree for drug discovery. It improves prediction accuracy and model interpretability, making AI insights more accessible to chemists.

Keywords:

Curriculum-based learning Graph Neural Network Hierarchical model Molecular property prediction Neural tree Uncertainty quantification

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Area of Science:

Computational chemistry
Drug discovery
Artificial intelligence in chemistry

Background:

Drug discovery relies on accurate molecular property prediction.
Deep learning offers potential for virtual screening but lacks interpretability.
Current models' black-box nature hinders chemists' ability to refine chemical spaces.

Purpose of the Study:

To develop an interpretable deep learning framework for molecular property prediction.
To enhance the accuracy and reduce uncertainty in neural network models for drug discovery.
To provide chemists with understandable insights from AI models.

Main Methods:

Utilized Graph Neural Networks (GNNs) for molecular graph feature extraction.
Implemented Curriculum-Based Learning Strategies to optimize GNN encoder training.
Integrated a Learning Binary Neural Tree (LBNT) as a predictor, jointly retrained with the GNN encoder.

Main Results:

The proposed framework improved prediction accuracy and reduced uncertainty compared to standard GNNs.
Combining GNNs with the LBNT predictor further enhanced predictive performance.
The LBNT predictor demonstrated superior performance and better explainability than common machine learning tools.

Conclusions:

The novel framework effectively balances predictive power and interpretability in drug discovery.
Curriculum-based learning and LBNT integration offer significant advantages over traditional methods.
This approach enhances the credibility and utility of AI in accelerating the drug discovery pipeline.