mlsanityData to 14 Oct 2026

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

arXiv 2205.03834 ↗First posted 8 May 2022q-bio.MN cs.LGcited by 31

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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.

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.

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9 of 72 references.

References · 9

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
  6. 2017Representation Learning on Graphs: Methods and Applicationscited by 457
  7. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 375
  8. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
  9. 2016Discriminative Embeddings of Latent Variable Models for Structured Datacited by 131

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 9; 2025: 9; 2026: 6.

31 papers.

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  1. 2023SELFormer: Molecular Representation Learning via SELFIES Language Modelscited by 40
  2. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  3. 2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformerscited by 12
  4. 2022Molecular Joint Representation Learning via Multi-modal Informationcited by 8
  5. 2023Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representationscited by 6
  6. 2024Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Surveycited by 6
  7. 2025Understanding and Mitigating Under-Confidence in GNNs from the Final Layercited by 6
  8. 2022GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactionscited by 5
  9. 2024Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretabilitycited by 2
  10. 2024DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptationcited by 2
  11. 2025Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learningcited by 2
  12. 2024Multi-view biomedical foundation models for molecule-target and property predictioncited by 1
19 more
  1. 2025CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screeningcited by 1
  2. 2025Rhomboid Tiling for Geometric Graph Deep Learningcited by 1
  3. 2025Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Modelcited by 1
  4. 2022VDDB: a comprehensive resource and machine learning platform for antiviral drug discoverycited by 0
  5. 2023Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversitycited by 0
  6. 2024ADCNet: a unified framework for predicting the activity of antibody-drug conjugatescited by 0
  7. 2024Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Predictioncited by 0
  8. 2024Fragment-Masked Diffusion for Molecular Optimizationcited by 0
  9. 2024Stacked ensemble-based mutagenicity prediction model using multiple modalities with graph attention networkcited by 0
  10. 2025AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discoverycited by 0
  11. 2025Cooperative Causal GraphSAGEcited by 0
  12. 2025PDCNet: a benchmark and general deep learning framework for activity prediction of peptide-drug conjugatescited by 0
  13. 2025Multi-Level Fusion Graph Neural Network for Molecule Property Predictioncited by 0
  14. 2026MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Taskscited by 0
  15. 2026DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Predictioncited by 0
  16. 2026BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learningcited by 0
  17. 2026Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programmingcited by 0
  18. 2026SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Predictioncited by 0
  19. 2026SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusioncited by 0