# Learning Topology-Specific Experts for Molecular Property Prediction

arXiv: https://arxiv.org/abs/2302.13693 · HTML: https://mlsanity.com/paper/2302.13693/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 52 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 30 of 33)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [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)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Accurate Learning of Graph Representations with Graph Multiset Pooling](https://mlsanity.com/paper/2102.11533/) (2021)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Factorizable Graph Convolutional Networks](https://mlsanity.com/paper/2010.05421/) (2020)
- [Mixture Models for Diverse Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/1902.07816/) (2019)
- [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://mlsanity.com/paper/1906.12192/) (2019)
- [Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective](https://mlsanity.com/paper/1906.11081/) (2019)
- [Hierarchical Inter-Message Passing for Learning on Molecular Graphs](https://mlsanity.com/paper/2006.12179/) (2020)
- [Network of Experts for Large-Scale Image Categorization](https://mlsanity.com/paper/1604.06119/) (2016)
- [Rethinking Graph Regularization for Graph Neural Networks](https://mlsanity.com/paper/2009.02027/) (2020)
- [DE-RRD: A Knowledge Distillation Framework for Recommender System](https://mlsanity.com/paper/2012.04357/) (2020)
- [Topology Distillation for Recommender System](https://mlsanity.com/paper/2106.08700/) (2021)
- [Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks](https://mlsanity.com/paper/2202.00529/) (2022)
- [Deep Learning of High-Order Interactions for Protein Interface Prediction](https://mlsanity.com/paper/2007.09334/) (2020)
- [Graph Classification by Mixture of Diverse Experts](https://mlsanity.com/paper/2103.15622/) (2021)

## Built on by (most cited first, 15 of 15)

- [Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling](https://mlsanity.com/paper/2304.02806/) (2023)
- [Graph Sparsification via Mixture of Graphs](https://mlsanity.com/paper/2405.14260/) (2024)
- [GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation](https://mlsanity.com/paper/2410.16606/) (2024)
- [Rank and Align: Towards Effective Source-free Graph Domain Adaptation](https://mlsanity.com/paper/2408.12185/) (2024)
- [Cooperation of Experts: Fusing Heterogeneous Information with Large Margin](https://mlsanity.com/paper/2505.20853/) (2025)
- [Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.08975/) (2025)
- [Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction](https://mlsanity.com/paper/2411.01158/) (2024)
- [Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures](https://mlsanity.com/paper/2510.16411/) (2025)
- [Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation](https://mlsanity.com/paper/2507.07621/) (2025)
- [A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation](https://mlsanity.com/paper/2508.04645/) (2025)
- [Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection](https://mlsanity.com/paper/2508.09801/) (2025)
- [Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property Prediction](https://mlsanity.com/paper/2601.12637/) (2026)
- [Fragment-Masked Diffusion for Molecular Optimization](https://mlsanity.com/paper/2408.09106/) (2024)
- [Adaptive Substructure-Aware Expert Model for Molecular Property Prediction](https://mlsanity.com/paper/2504.05844/) (2025)
- [Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection](https://mlsanity.com/paper/2602.19025/) (2026)

## Abstract (arXiv)

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.