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Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields.
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J. Drews · 2000
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Clustering by fast search and find of density peaks
A. Rodriguez and A. Laio · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. H. Cho, and Y. Bengio · 2015
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Zinc 15–ligand discovery for everyone
T. Sterling and J. J. Irwin · 2015
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Study on density peaks clustering based on k-nearest neighbors and principal component analysis
M. Du, S. Ding, and H. Jia · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. D. M.-W. C. Kenton and L. K. Toutanova · 2019
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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A survey of embedding space alignment methods for language and knowledge graphs
A. Kalinowski and Y. An · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
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Align before fuse: Vision and language representation learning with momentum distillation
J. Li, R. Selvaraju, A. Gotmare, S. Joty, C. Xiong, and S. C. H. Hoi · 2021
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Translation between molecules and natural language
C. Edwards, T. Lai, K. Ros, G. Honke, K. Cho, and H. Ji · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
J. Li, D. Li, C. Xiong, and S. Hoi · 2022
Cited alongside, same era.
Pre-training molecular graph representation with 3d geometry
S. Liu, H. Wang, W. Liu, J. Lasenby, H. Guo, and J. Tang · 2022
Cited alongside, same era.
Cross-dependent graph neural networks for molecular property prediction
H. Ma, Y. Bian, Y. Rong, W. Huang, T. Xu, W. Xie, G. Ye, and J. Huang · 2022
Cited alongside, same era.
A molecular multimodal foundation model associating molecule graphs with natural language
B. Su, D. Du, Z. Yang, Y. Zhou, J. Li, A. Rao, H. Sun, Z. Lu, and J.-R. Wen · 2022
Cited alongside, same era.
MolCA: Molecular graph-language modeling with cross-modal projector and uni-modal adapter
Z. Liu, S. Li, Y. Luo, H. Fei, Y. Cao, K. Kawaguchi, X. Wang, and T.-S. Chua · 2023
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MolXPT: Wrapping molecules with text for generative pre-training
Z. Liu, W. Zhang, Y. Xia, L. Wu, S. Xie, T. Qin, M. Zhang, and T.-Y. Liu · 2023
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Molfm: A multimodal molecular foundation model
Y. Luo, K. Yang, M. Hong, X. Liu, and Z. Nie · 2023
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BioT5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations
Q. Pei, W. Zhang, J. Zhu, K. Wu, K. Gao, L. Wu, Y. Xia, and R. Yan · 2023
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Integrating structure-based approaches in generative molecular design
M. Thomas, A. Bender, and C. de Graaf · 2023
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Q. Wang, Y. Zhang, Y. Zheng, P. Pan, and X.-S. Hua · 2022
Cited alongside, same era.
Z. Zeng, Y. Yao, Z. Liu, and M. Sun · 2022
Cited alongside, same era.
H. Cao, Z. Liu, X. Lu, Y. Yao, and Y. Li · 2023
Cited alongside, same era.
Unifying molecular and textual representations via multi-task language modelling
D. Christofidellis, G. Giannone, J. Born, O. Winther, T. Laino, and M. Manica · 2023
Cited alongside, same era.
Unimap: Universal smiles-graph representation learning
S. Feng, L. Yang, W. Ma, and Y. Lan · 2023
Cited alongside, same era.
Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods
M. Goel, R. Aggarwal, B. Sridharan, P. K. Pal, and U. D. Priyakumar · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
J. Li, D. Li, S. Savarese, and S. Hoi
Cited in the paper.
Understanding the limitations of deep models for molecular property prediction: Insights and solutions
J. Xia, L. Zhang, X. Zhu, Y. Liu, Z. Gao, B. Hu, C. Tan, J. Zheng, S. Li, and S. Z. Li · 2023
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Drugassist: A large language model for molecule optimization
G. Ye, X. Cai, H. Lai, X. Wang, J. Huang, L. Wang, W. Liu, and X. Zeng · 2023
Later among the works it cites.
Knowledge graphs meet multi-modal learning: A comprehensive survey
Z. Chen, Y. Zhang, Y. Fang, Y. Geng, L. Guo, X. Chen, Q. Li, W. Zhang, J. Chen, Y. Zhu, et al · 2024
Closest in time.
Git-mol: A multi-modal large language model for molecular science with graph, image, and text
P. Liu, Y. Ren, J. Tao, and Z. Ren · 2024
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Multimodal molecular pretraining via modality blending
Q. Yu, Y. Zhang, Y. Ni, S. Feng, Y. Lan, H. Zhou, and J. Liu · 2024
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