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Cited by 635Computational Drug Discovery MethodsMachine Learning in Materials ScienceAdvanced Chemical Physics Studies

The DOI matches its Crossref record; 44 of the 46 references it cites were found in a registry or an index.

Large-scale chemical language representations capture molecular structure and properties

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Ross, Jerret, Belgodere, Brian, Chenthamarakshan, Vijil, Padhi, Inkit, Mroueh, Youssef, Das, Payel·2022·Nature Machine Intelligence
DOI 10.1038/s42256-022-00580-7

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Figure 1 Overview of MoLFormer pipeline. The transformer neural network based model is trained on the SMILES sequences corresponding to a large collection of chemical molecules from PubChem and Zinc, two public chemical databases in a self-supervised fashion. MOLFORMER was designed with an efficient linear attention mechanism and relative positional embeddings, with the goal of learning a meaningful and compressed representation of chemical molecules. This foundation model was then adopted to different downstream molecular property prediction tasks via fine-tuning on task-specific data. The representative power was further tested by recovering molecular similarity using the MOLFORMER encodings, as well as by analyzing the correspondence between the interatomic spatial distance and attention value for a given molecule.
Figure 1 Overview of MoLFormer pipeline. The transformer neural network based model is trained on the SMILES sequences corresponding to a large collection of chemical molecules from PubChem and Zinc, two public chemical databases in a self-supervised fashion. MOLFORMER was designed with an efficient linear attention mechanism and relative positional embeddings, with the goal of learning a meaningful and compressed representation of chemical molecules. This foundation model was then adopted to different downstream molecular property prediction tasks via fine-tuning on task-specific data. The representative power was further tested by recovering molecular similarity using the MOLFORMER encodings, as well as by analyzing the correspondence between the interatomic spatial distance and attention value for a given molecule.CC-BY · From the PDF
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Figure 4 Visualization of the learned attention map (using either full or linear attention) under rotary embedding and corresponding molecular structure (bond connectivity and 3D distance) for two random molecules: 'CC1(C)C(C)(O)C1(C)O' (top) and (bottom). The attention map (only tokens that map to
Figure 4 Visualization of the learned attention map (using either full or linear attention) under rotary embedding and corresponding molecular structure (bond connectivity and 3D distance) for two random molecules: 'CC1(C)C(C)(O)C1(C)O' (top) and (bottom). The attention map (only tokens that map toCC-BY · From the PDF

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Research works printed but not linked: 20

  1. Goh, G. B., Hodas, N. O., Siegel, C. & Vishnu, A. SMILES2Vec: an interpretable general-purpose deep neural network for predicting chemical properties. Preprint at https://arxiv.org/abs/1712.02034 (2017). · arXiv
  2. Shin, B., Park, S., Kang, K. & Ho, J. C. Self-attention based molecule representation for predicting drug–target interaction. Proc. Mach. Learn. Res. 106, 230–248 (2019).
  3. Gao, W., Fu, T., Sun, J. & Coley, C. W. Sample efficiency matters: a benchmark for practical molecular optimization. In Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022).
  4. Defferrard, M., Bresson, X. & Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst. 29 (2016).
  5. Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations (OpenReview.net, 2017).
  6. Li, Y., Tarlow, D., Brockschmidt, M. & Zemel, R. Gated graph sequence neural networks. In 4th International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) (OpenReview.net, 2016).
  7. Veličković, P. et al. Graph attention networks. In International Conference on Learning Representations (2018).
  8. Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30, 1025–1035 (Curran Associates Inc., 2017).
  9. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. Proc. Mach. Learn Res. 70, 1263–1272 (2017).
  10. Liao, R., Zhao, Z., Urtasun, R. & Zemel, R. S. LanczosNet: multi-scale deep graph convolutional networks. In 7th International Conference on Learning Representations (OpenReview.net, 2019).
  11. Chen, P., Liu, W., Hsieh, C.-Y., Chen, G. & Zhang, S. Utilizing edge features in graph neural networks via variational information maximization. Preprint at https://arxiv.org/abs/1906.05488 (2019). · arXiv
  12. Chithrananda, S., Grand, G. & Ramsundar, B. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. Preprint at https://arxiv.org/abs/2010.09885 (2020). · arXiv
  13. Katharopoulos, A., Vyas, A., Pappas, N. & Fleuret, F. Transformers are RNNs: fast autoregressive transformers with linear attention. Proc. Mach. Learn. Res. 119, 5156–5165 (2020).
  14. Liu, S., Demirel, M. F. & Liang, Y. N-gram graph: simple unsupervised representation for graphs, with applications to molecules. In NIPS’19: Proc. 33rd International Conference on Neural Information Processing Systems (eds Wallach, H. et al.) 8464–8476 (Curran Associates, Inc., 2019).
  15. Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. Proc. Mach. Learn. Res. 119, 1597–1607 (2020).
  16. Oord, A. V. D., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. Preprint at https://arxiv.org/abs/1807.03748 (2018). · arXiv
  17. Klicpera, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. In 8th International Conference on Learning Representations (OpenReview.net, 2020).
  18. Ke, G., He, D. & Liu, T.-Y. Rethinking positional encoding in language pre-training. In 9th International Conference on Learning Representations (OpenReview.net, 2021).
  19. Beltagy, I., Peters, M. E. & Cohan, A. Longformer: the long-document transformer. Preprint at https://arxiv.org/abs/2004.05150 (2020). · arXiv
  20. Kitaev, N., Kaiser, L. & Levskaya, A. Reformer: the efficient transformer. In 8th International Conference on Learning Representations (OpenReview.net, 2020).

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Metadata

Authors
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., & Das, P.
Affiliations
IBM (United States) (US)
Corresponding author
  • Ross, Jerret
  • Das, Payel
ORCID
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2022-12-21
Volume
4
Issue
12
Pages
1256-1264
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-022-00580-7
OpenAlex
W4313485929
Cited by
590
References
46
Citations by year
2026: 222 · 2025: 212 · 2024: 112 · 2023: 42 · 2022: 2
FWCI
49.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Machine Intelligence (publishedVersion)
  • Research Square (acceptedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
molecular embeddings, Transformer encoder, rotary positional embeddings, linear attention, SMILES, molecular property prediction, quantum chemical properties, attention analysis, drug discovery, material design, self-supervised learning

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