# FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

arXiv: https://arxiv.org/abs/2205.03834 · HTML: https://mlsanity.com/paper/2205.03834/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 8 May 2022.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)

## Built on by (most cited first, 30 of 31)

- [SELFormer: Molecular Representation Learning via SELFIES Language Models](https://mlsanity.com/paper/2304.04662/) (2023)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers](https://mlsanity.com/paper/2402.04538/) (2024)
- [Molecular Joint Representation Learning via Multi-modal Information](https://mlsanity.com/paper/2211.14042/) (2022)
- [Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations](https://mlsanity.com/paper/2306.01631/) (2023)
- [Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey](https://mlsanity.com/paper/2402.07249/) (2024)
- [Understanding and Mitigating Under-Confidence in GNNs from the Final Layer](https://mlsanity.com/paper/2505.11335/) (2025)
- [GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions](https://mlsanity.com/paper/2208.05863/) (2022)
- [Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability](https://mlsanity.com/paper/2409.03463/) (2024)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://mlsanity.com/paper/2510.23640/) (2025)
- [Multi-view biomedical foundation models for molecule-target and property prediction](https://mlsanity.com/paper/2410.19704/) (2024)
- [CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening](https://mlsanity.com/paper/2502.11001/) (2025)
- [Rhomboid Tiling for Geometric Graph Deep Learning](https://mlsanity.com/paper/2505.09586/) (2025)
- [Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model](https://mlsanity.com/paper/2507.03920/) (2025)
- [VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery](https://mlsanity.com/paper/2209.13521/) (2022)
- [Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity](https://mlsanity.com/paper/2312.10943/) (2023)
- [ADCNet: a unified framework for predicting the activity of antibody-drug conjugates](https://mlsanity.com/paper/2401.09176/) (2024)
- [Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction](https://mlsanity.com/paper/2405.18724/) (2024)
- [Fragment-Masked Diffusion for Molecular Optimization](https://mlsanity.com/paper/2408.09106/) (2024)
- [Stacked ensemble-based mutagenicity prediction model using multiple modalities with graph attention network](https://mlsanity.com/paper/2409.01731/) (2024)
- [AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery](https://mlsanity.com/paper/2505.11878/) (2025)
- [Cooperative Causal GraphSAGE](https://mlsanity.com/paper/2505.14748/) (2025)
- [PDCNet: a benchmark and general deep learning framework for activity prediction of peptide-drug conjugates](https://mlsanity.com/paper/2506.12821/) (2025)
- [Multi-Level Fusion Graph Neural Network for Molecule Property Prediction](https://mlsanity.com/paper/2507.03430/) (2025)
- [MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks](https://mlsanity.com/paper/2602.20573/) (2026)
- [DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction](https://mlsanity.com/paper/2603.21108/) (2026)
- [BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning](https://mlsanity.com/paper/2604.06336/) (2026)
- [Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programming](https://mlsanity.com/paper/2605.29329/) (2026)
- [SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction](https://mlsanity.com/paper/2608.06441/) (2026)

## Abstract (arXiv)

Deep learning is an important method for molecular design and exhibits considerable ability to predict molecular properties, including physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) properties. In this study, we advanced a novel deep learning architecture, termed FP-GNN, which combined and simultaneously learned information from molecular graphs and fingerprints. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset, and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations.