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The recent advances in neural language models have also been successfully applied to the field of chemistry, offering generative solutions for classical problems in molecular design and synthesis planning.
Language models are few-shot learners
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Elementary mathematical theory of classification and prediction
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Reoptimization of mdl keys for use in drug discovery
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Bleu: a method for automatic evaluation of machine translation
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Rouge: A package for automatic evaluation of summaries
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A. (2005) · 2005
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · 2010
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M. (2010) · 2010
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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
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., and Klambauer, G. (2018) · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018) · 2018
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“found in translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Schwaller, P., Gaudin, T., Lanyi, D., Bekas, C., and Laino, T. (2018) · 2018
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PyTorch Lightning
Falcon, W. and The PyTorch Lightning team (2019) · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019) · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A. (2019) · 2019
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Krenn, M., Häse, F., Nigam, A., Friederich, P., and Aspuru-Guzik, A. (2020) · 2020
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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., Liu, P. J., et al. (2020) · 2020
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Language models are few-shot multilingual learners
Winata, G. I., Madotto, A., Lin, Z., Liu, R., Yosinski, J., and Fung, P. (2021) · 2021
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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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Translation between molecules and natural language
Edwards, C., Lai, T., Ros, K., Honke, G., and Ji, H. (2022) · 2022
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Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T. (2022) · 2022
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Solving quantitative reasoning problems with language models
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Schwaller, P., Petraglia, R., Zullo, V., Nair, V. H., Haeuselmann, R. A., Pisoni, R., Bekas, C., Iuliano, A., and Laino, T. (2020) · 2020
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Automated extraction of chemical synthesis actions from experimental procedures
Vaucher, A. C., Zipoli, F., Geluykens, J., Nair, V. H., Schwaller, P., and Laino, T. (2020) · 2020
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Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M. (2020) · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · 2021
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Text2mol: Cross-modal molecule retrieval with natural language queries
Edwards, C., Zhai, C., and Ji, H. (2021) · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · 2021
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Towards understanding and mitigating social biases in language models
Liang, P. P., Wu, C., Morency, L.-P., and Salakhutdinov, R. (2021) · 2021
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Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al. (2022) · 2022
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Can large language models reason about medical questions?
Liévin, V., Hother, C. E., and Winther, O. (2022) · 2022
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Unified deep learning model for multitask reaction predictions with explanation
Lu, J. and Zhang, Y. (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. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022) · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (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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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D. (2022) · 2022
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Regression transformer enables concurrent sequence regression and generation for molecular language modelling
Born, J. and Manica, M. (2023) · 2023
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Accelerating material design with the generative toolkit for scientific discovery
Manica, M., Born, J., Cadow, J., Christofidellis, D., Dave, A., Clarke, D., Teukam, Y. G. N., Giannone, G., Hoffman, S. C., Buchan, M., Chenthamarakshan, V., Donovan, T., Hsu, H. H., Zipoli, F., Schilter, O., Kishimoto, A., Hamada, L., Padhi, I., Wehden, K., McHugh, L., Khrabrov, A., Das, P., Takeda, S., and Smith, J. R. (2023) · 2023
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Nextmove software pistachio
Nextmove (2023) · 2023
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