mlsanityData to 7 Oct 2026

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science

arXiv 2106.14232 ↗First posted 27 Jun 2021cs.LG q-bio.QMcited by 38

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) constitute a class of deep learning methods for graph data. They have wide applications in chemistry and biology, such as molecular property prediction, reaction prediction and drug-target interaction prediction. Despite the interest, GNN-based modeling is challenging as it requires graph data pre-processing and modeling in addition to programming and deep learning. Here we present DGL-LifeSci, an open-source package for deep learning on graphs in life science. DGL-LifeSci is a python toolkit based on RDKit, PyTorch and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based modeling on custom datasets for molecular property prediction, reaction prediction and molecule generation. With its command-line interfaces, users can perform modeling without any background in programming and deep learning. We test the command-line interfaces using standard benchmarks MoleculeNet, USPTO, and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed up by up to 6x. For modeling flexibility, DGL-LifeSci provides well-optimized modules for various stages of the modeling pipeline. In addition, DGL-LifeSci provides pre-trained models for reproducing the test experiment results and applying models without training. The code is distributed under an Apache-2.0 License and is freely accessible at https://github.com/awslabs/dgl-lifesci.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 27 Jun 2021.

Builds on

15 of 34 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  9. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  10. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  11. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  12. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
3 more
  1. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  2. 2018PotentialNet for Molecular Property Predictioncited by 46
  3. 2020Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Propertiescited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 10; 2023: 9; 2024: 7; 2025: 8; 2026: 2.

38 papers.

Sort:

By area:

  1. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  2. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  3. 2024DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaborationcited by 40
  4. 2022Interpretable bilinear attention network with domain adaptation improves drug-target predictioncited by 39
  5. 2023Retrosynthesis Prediction with Local Template Retrievalcited by 15
  6. 2023Efficiently predicting high resolution mass spectra with graph neural networkscited by 13
  7. 2023Conformal Drug Property Prediction with Density Estimation under Covariate Shiftcited by 13
  8. 2022Spectral Adversarial Training for Robust Graph Neural Networkcited by 12
  9. 2022ChemicalX: A Deep Learning Library for Drug Pair Scoringcited by 11
  10. 2022BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learningcited by 8
  11. 2024Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNNcited by 8
  12. 2022Deep learning methods for drug response prediction in cancer: predominant and emerging trendscited by 6
26 more
  1. 2023Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representationscited by 6
  2. 2021Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spacescited by 5
  3. 2024Chemist-aligned retrosynthesis by ensembling diverse inductive bias modelscited by 5
  4. 2022Partial Product Aware Machine Learning on DNA-Encoded Librariescited by 3
  5. 2022Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Propertiescited by 3
  6. 2023Censoring chemical data to mitigate dual use riskcited by 3
  7. 2024FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learningcited by 3
  8. 2021MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformerscited by 2
  9. 2024KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Predictioncited by 2
  10. 2025Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Reviewcited by 2
  11. 2025Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learningcited by 2
  12. 2025OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learningcited by 2
  13. 2023Graph Neural Networks with a Distribution of Parametrized Graphscited by 1
  14. 2024Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspectivecited by 1
  15. 2024DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learningcited by 1
  16. 2025ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Designcited by 1
  17. 2026KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matchingcited by 1
  18. 2022Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphscited by 0
  19. 2023Multi-Dimensional Ability Diagnosis for Machine Learning Algorithmscited by 0
  20. 2023CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representationscited by 0
  21. 2023Drug Interaction Vectors Neural Network: DrIVeNNcited by 0
  22. 2025SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Predictioncited by 0
  23. 2025SMPR: A structure-enhanced multimodal drug-disease prediction model for drug repositioning and cold startcited by 0
  24. 2025KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Predictioncited by 0
  25. 2025Data-Driven Extended Corresponding State Approach for Residual Property Prediction of Hydrofluoroolefinscited by 0
  26. 2026Rudder: Steering Prefetching in Distributed GNN Training using LLM Agentscited by 0