# Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction

arXiv: https://arxiv.org/abs/2411.13688 · HTML: https://mlsanity.com/paper/2411.13688/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 174 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 24 of 24)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Towards Sparse Hierarchical Graph Classifiers](https://mlsanity.com/paper/1811.01287/) (2018)
- [Molecular representation learning with language models and domain-relevant auxiliary tasks](https://mlsanity.com/paper/2011.13230/) (2020)
- [Learning Graph-Level Representation for Drug Discovery](https://mlsanity.com/paper/1709.03741/) (2017)
- [Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models](https://mlsanity.com/paper/1706.06689/) (2017)
- [Evaluating Deep Graph Neural Networks](https://mlsanity.com/paper/2108.00955/) (2021)
- [Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective](https://mlsanity.com/paper/2206.07743/) (2022)
- [Edge Attention-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1802.04944/) (2018)
- [AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions](https://mlsanity.com/paper/2012.13248/) (2020)
- [Continuous Representation of Molecules Using Graph Variational Autoencoder](https://mlsanity.com/paper/2004.08152/) (2020)
- [Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2012.04444/) (2020)
- [Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?](https://mlsanity.com/paper/2104.02604/) (2021)
- [Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction](https://mlsanity.com/paper/1912.03702/) (2019)
- [Substructure-Atom Cross Attention for Molecular Representation Learning](https://mlsanity.com/paper/2210.08243/) (2022)
- [Molecular Contrastive Learning of Representations via Graph Neural Networks](https://mlsanity.com/paper/2102.10056/) (2021)

## Built on by (most cited first, 2 of 2)

- [Topological Feature Compression for Molecular Graph Neural Networks](https://mlsanity.com/paper/2508.07807/) (2025)
- [Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework](https://mlsanity.com/paper/2511.08314/) (2025)

## Abstract (arXiv)

Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computational drug discovery. Recently, message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs. While such techniques hold great promise, further investigations are needed to clarify if and when they indeed manage to definitively outcompete classical molecular featurisations such as extended-connectivity fingerprints (ECFPs) and physicochemical-descriptor vectors (PDVs). We systematically explore and further develop classical and graph-based molecular featurisation methods for two important tasks: molecular property prediction, in particular, quantitative structure-activity relationship (QSAR) prediction, and the largely unexplored challenge of activity-cliff (AC) prediction. We first give a technical description and critical analysis of PDVs, ECFPs and message-passing GNNs, with a focus on graph isomorphism networks (GINs). We then conduct a rigorous computational study to compare the performance of PDVs, ECFPs and GINs for QSAR and AC-prediction. Following this, we mathematically describe and computationally evaluate a novel twin neural network model for AC-prediction. We further introduce an operation called substructure pooling for the vectorisation of structural fingerprints as a natural counterpart to graph pooling in GNN architectures. We go on to propose Sort & Slice, a simple substructure-pooling technique for ECFPs that robustly outperforms hash-based folding at molecular property prediction. Finally, we outline two ideas for future research: (i) a graph-based self-supervised learning strategy to make classical molecular featurisations trainable, and (ii) trainable substructure-pooling via differentiable self-attention.