# Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction

arXiv: https://arxiv.org/abs/2304.06253 · HTML: https://mlsanity.com/paper/2304.06253/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Interpretable and Explainable Machine Learning for Materials Science and Chemistry](https://mlsanity.com/paper/2111.01037/) (2021)
- [Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation](https://mlsanity.com/paper/2011.12117/) (2020)
- [Predicting CO2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks](https://mlsanity.com/paper/2210.01120/) (2022)

## Built on by (most cited first, 3 of 3)

- [ADMET property prediction through combinations of molecular fingerprints](https://mlsanity.com/paper/2310.00174/) (2023)
- [drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network](https://mlsanity.com/paper/2405.08979/) (2024)
- [XIMP: Cross Graph Inter-Message Passing for Molecular Property Prediction](https://mlsanity.com/paper/2601.19037/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) demonstrate great performance in compound property and activity prediction due to their capability to efficiently learn complex molecular graph structures. However, two main limitations persist including compound representation and model interpretability. While atom-level molecular graph representations are commonly used because of their ability to capture natural topology, they may not fully express important substructures or functional groups which significantly influence molecular properties. Consequently, recent research proposes alternative representations employing reduction techniques to integrate higher-level information and leverages both representations for model learning. However, there is still a lack of study about different molecular graph representations on model learning and interpretation. Interpretability is also crucial for drug discovery as it can offer chemical insights and inspiration for optimization. Numerous studies attempt to include model interpretation to explain the rationale behind predictions, but most of them focus solely on individual prediction with little analysis of the interpretation on different molecular graph representations. This research introduces multiple molecular graph representations that incorporate higher-level information and investigates their effects on model learning and interpretation from diverse perspectives. The results indicate that combining atom graph representation with reduced molecular graph representation can yield promising model performance. Furthermore, the interpretation results can provide significant features and potential substructures consistently aligning with background knowledge. These multiple molecular graph representations and interpretation analysis can bolster model comprehension and facilitate relevant applications in drug discovery.