mlsanityData to 7 Oct 2026

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

arXiv 2111.15112 ↗First posted 30 Nov 2021cs.LG physics.chem-phcited by 10

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Abstract (arXiv)

Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and Graph Neural Networks(GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 48 of its 85 references that are in the map. First posted on arXiv 30 Nov 2021.

Builds on

48 of 85 references.

References · 48

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2017Graph Attention Networkscited by 8,854
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  8. 2018How Powerful are Graph Neural Networks?cited by 4,355
  9. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  10. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  11. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  12. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
36 more
  1. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  3. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  5. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  6. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  7. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  8. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  9. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  10. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  11. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  12. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  13. 2020Directional Message Passing for Molecular Graphscited by 556
  14. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  15. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  16. 2019Graph U-Netscited by 489
  17. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  18. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  19. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  20. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  21. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  22. 2019A Fourier Perspective on Model Robustness in Computer Visioncited by 288
  23. 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
  24. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
  25. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  26. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  27. 2017Dataset Augmentation in Feature Spacecited by 124
  28. 2017SMILES Enumeration as Data Augmentation for Neural Network Modeling of Moleculescited by 58
  29. 2020SkipGNN: Predicting Molecular Interactions with Skip-Graph Networkscited by 44
  30. 2018Text Data Augmentation Made Simple By Leveraging NLP Cloud APIscited by 40
  31. 2019Deep learning for molecular design - a review of the state of the artcited by 38
  32. 2018Message-passing neural networks for high-throughput polymer screeningcited by 30
  33. 2020Orbital Graph Convolutional Neural Network for Material Property Predictioncited by 29
  34. 2015Do We Need More Training Data?cited by 26
  35. 2018Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learningcited by 4
  36. 2019SMILES-X: autonomous molecular compounds characterization for small datasets without descriptorscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 4; 2026: 1.

10 papers.

  1. 2022Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrastcited by 37
  2. 2022Crystal Twins: Self-supervised Learning for Crystalline Material Property Predictioncited by 34
  3. 2025mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Moleculescited by 8
  4. 2022Graph Contrastive Learning for Materialscited by 7
  5. 2023Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Predictioncited by 7
  6. 2025Supervised Pretraining for Material Property Predictioncited by 1
  7. 2025SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regressioncited by 1
  8. 2026XIMP: Cross Graph Inter-Message Passing for Molecular Property Predictioncited by 1
  9. 2024Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasetscited by 0
  10. 2025Deep Electromagnetic Structure Design Under Limited Evaluation Budgetscited by 0