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Molecule property prediction has gained significant attention in recent years.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
Earlier work this paper cites.
Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J. (2019) · 1905
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019c) · 1907
Earlier work this paper cites.
Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Honda, S., Shi, S., and Ueda, H. R. (2019) · 1911
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Graph-bert: Only attention is needed for learning graph representations
Zhang, J., Zhang, H., Xia, C., and Sun, L. (2020) · 2001
Earlier work this paper cites.
Molecule attention transformer
Maziarka, Ł., Danel, T., Mucha, S., Rataj, K., Tabor, J., and Jastrzkebski, S. (2020) · 2002
Earlier work this paper cites.
Molecular strategies to inhibit hiv-1 replication
Nielsen, M. H., Pedersen, F. S., and Kjems, J. (2005) · 2005
Earlier work this paper cites.
Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G., and Ramsundar, B. (2020) · 2010
Earlier work this paper cites.
The turking test: Can language models understand instructions?
Efrat, A. and Levy, O. (2020) · 2010
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X. (2020) · 2012
Earlier work this paper cites.
Chembl: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · 2012
Earlier work this paper cites.
Pubchem’s bioassay database
Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., Zhou, Z., Han, L., Karapetyan, K., Dracheva, S., Shoemaker, B. A., et al. (2012) · 2012
Earlier work this paper cites.
Theoretical pka calculations with continuum model solvents, alternative protocols to thermodynamic cycles
Casasnovas, R., Ortega-Castro, J., Frau, J., Donoso, J., and Munoz, F. (2014) · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P. (2015) · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2016) · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Earlier work this paper cites.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017) · 2017
Earlier work this paper cites.
Prediction of human cytochrome p450 inhibition using a multitask deep autoencoder neural network
Li, X., Xu, Y., Lai, L., and Pei, J. (2018) · 2018
Earlier work this paper cites.
Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A. (2018) · 2018
Earlier work this paper cites.
Self-attention with relative position representations
Shaw, P., Uszkoreit, J., and Vaswani, A. (2018) · 2018
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Matched molecular pair analysis on large melting point datasets: a big data perspective
Withnall, M., Chen, H., and Tetko, I. V. (2018) · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V. (2018) · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018) · 2018
Earlier work this paper cites.
Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J. (2018) · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Earlier work this paper cites.
Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more
Ramsundar, B., Eastman, P., Walters, P., and Pande, V. (2019) · 2019
Earlier work this paper cites.
Deep graph infomax
Velickovic, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D. (2019) · 2019
Earlier work this paper cites.
Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Wang, S., Guo, Y., Wang, Y., Sun, H., and Huang, J. (2019) · 2019
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H. (2020) · 2020
Cited alongside, same era.
Hierarchical generation of molecular graphs using structural motifs
Jin, W., Barzilay, R., and Jaakkola, T. (2020) · 2020
Cited alongside, same era.
Understanding the difficulty of training transformers
Liu, L., Liu, X., Gao, J., Chen, W., and Han, J. (2020) · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2020) · 2020
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · 2022
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Investigating expressiveness of transformer in spectral domain for graphs
Bastos, A., Nadgeri, A., Singh, K., Kanezashi, H., Suzumura, T., and Mulang, I. O. (2022) · 2022
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From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers
Choromanski, K., Lin, H., Chen, H., Zhang, T., Sehanobish, A., Likhosherstov, V., Parker-Holder, J., Sarlos, T., Weller, A., and Weingarten, T. (2022) · 2022
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · 2022
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Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J. (2020) · 2020
Cited alongside, same era.
Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Sun, F.-Y., Hoffman, J., Verma, V., and Tang, J. (2020) · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020) · 2020
Cited alongside, same era.
Rezero is all you need: Fast convergence at large depth
Bachlechner, T., Majumder, B. P., Mao, H., Cottrell, G., and McAuley, J. (2021) · 2021
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Bao, H., Dong, L., Piao, S., and Wei, F. (2021) · 2021
Cited alongside, same era.
Text2mol: Cross-modal molecule retrieval with natural language queries
Edwards, C., Zhai, C., and Ji, H. (2021) · 2021
Cited alongside, same era.
Central nervous system delivery of molecules across the blood-brain barrier
Gosselet, F., Loiola, R. A., Roig, A., Rosell, A., and Culot, M. (2021) · 2021
Cited alongside, same era.
Edwards, C., Lai, T., Ros, K., Honke, G., and Ji, H. (2022) · 2022
Later among the works it cites.
Molecular contrastive learning with chemical element knowledge graph
Fang, Y., Zhang, Q., Yang, H., Zhuang, X., Deng, S., Zhang, W., Qin, M., Chen, Z., Fan, X., and Chen, H. (2022) · 2022
Later among the works it cites.
Unleashing the power of transformer for graphs
Guo, L., Zhang, Q., and Chen, H. (2022) · 2022
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Chemformer: a pre-trained transformer for computational chemistry
Irwin, R., Dimitriadis, S., He, J., and Bjerrum, E. J. (2022) · 2022
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Pure transformers are powerful graph learners
Kim, J., Nguyen, T. D., Min, S., Cho, S., Lee, M., Lee, H., and Hong, S. (2022) · 2022
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Multi-modal molecule structure-text model for text-based retrieval and editing
Liu, S., Nie, W., Wang, C., Lu, J., Qiao, Z., Liu, L., Tang, J., Xiao, C., and Anandkumar, A. (2022) · 2022
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Cross-task generalization via natural language crowdsourcing instructions
Mishra, S., Khashabi, D., Baral, C., and Hajishirzi, H. (2022) · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · 2022
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Grpe: Relative positional encoding for graph transformer
Park, W., Chang, W.-G., Lee, D., Kim, J., et al. (2022) · 2022
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In-boxbart: Get instructions into biomedical multi-task learning
Parmar, M., Mishra, S., Purohit, M., Luo, M., Mohammad, M., and Baral, C. (2022) · 2022
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Molformer: Large scale chemical language representations capture molecular structure and properties
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., and Das, P. (2022) · 2022
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A molecular multimodal foundation model associating molecule graphs with natural language
Su, B., Du, D., Yang, Z., Zhou, Y., Li, J., Rao, A., Sun, H., Lu, Z., and Wen, J.-R. (2022) · 2022
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Does gnn pretraining help molecular representation?
Sun, R., Dai, H., and Yu, A. W. (2022) · 2022
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Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R. (2022) · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., et al. (2022b) · 2022
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Simgrace: A simple framework for graph contrastive learning without data augmentation
Xia, J., Wu, L., Chen, J., Hu, B., and Li, S. Z. (2022a) · 2022
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Pre-training via denoising for molecular property prediction
Zaidi, S., Schaarschmidt, M., Martens, J., Kim, H., Teh, Y. W., Sanchez-Gonzalez, A., Battaglia, P., Pascanu, R., and Godwin, J. (2022) · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zeng, Z., Yao, Y., Liu, Z., and Sun, M. (2022) · 2022
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Are more layers beneficial to graph transformers?
Zhao, H., Ma, S., Zhang, D., Deng, Z.-H., and Wei, F. (2022) · 2022
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Li, J., Li, D., Savarese, S., and Hoi, S. (2023) · 2023
Closest in time.
Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Liu, S., Guo, H., and Tang, J. (2023) · 2023
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Enhancing activity prediction models in drug discovery with the ability to understand human language
Seidl, P., Vall, A., Hochreiter, S., and Klambauer, G. (2023) · 2023
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Molgpt: molecular generation using a transformer-decoder model
Bagal, V., Aggarwal, R., Vinod, P., and Priyakumar, U. D. (2021) · 2076
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