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A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning

  • żrański, AM, Martinez alvarado, Ji, Shields, BJ & Doyle, AG expect revenue revenue through the learning subject to supervision. ACC. Chemical. Accuracy. 541856-1865 (2021).

    The scientific researcher from Google

  • Zahrt, AF, ATHAVALE, SV & Denmark, SE quantitative structure and order in selective incentive: past, present and future. Chemical. pastor 1201620-1689 (2020).

    The scientific researcher from Google

  • Corey, EJ, Long, AK & Rubenstein, SD analysis with the help of the computer in organic synthesis. sciences 228408-418 (1985).

    The scientific researcher from Google

  • Todd, MH Concrete membership with the help of the computer. Chemical. Suk. pastor 34247 (2005).

    The scientific researcher from Google

  • Cheong, PH-Y. , Legaut, CY, UM, JM, çelebi-Oulçüm, N. & Houk, Kn Quantum Mechanical Investigations of Morticocatalysion: Mechanisms, Interactions, and Specifications. Chemical. pastor 1115042-5137 (2011).

    The scientific researcher from Google

  • Klucznik, T. et al. Effective combinations of various goals related to the planned doctor by the computer and implementing them in the laboratory. Chemical 4522-532 (2018).

    The scientific researcher from Google

  • Crawford, JM, Kingston, C., Toste, FD & Sigman, MS Data Science meet physical organic chemistry. ACC. Chemical. Accuracy. 543136-3148 (2021).

    The scientific researcher from Google

  • RineHart, Ni, Zahrt, AF, Henle, JJ & Denmark, SE DREAMS, false beginnings, closed ends, and redemption: a date for the development of chemical workflow to improve selective stimuli. ACC. Chemical. Accuracy. 542041-2054 (2021).

    The scientific researcher from Google

  • Renhardt, Ni and others. Automated learning tool to predict the conditions that adapt the substrate to the CN connections stimulated by PD. sciences 381965-972 (2023).

    The scientific researcher from Google

  • Zahrt, AF et al. Predicting the higher stimuli in the workflow that depends on computer and machine learning. sciences 363Eau5631 (2019).

    The scientific researcher from Google

  • Reid, JP & Sigman, MS Comprehensive Fading from EnanTIOSELECTIVITIVITIVITION in the asymmetric stimulus. nature 571343-348 (2019).

    The scientific researcher from Google

  • Ahneman, DT, Estrada, JG, LIN, S., Dreher, SD & Doyle, AG predicts the performance of the reaction in the cross -off using machine learning. sciences 360186-190 (2018).

    The scientific researcher from Google

  • SANDFORT, F., Strieth-Klethoff, F., Kühnemund, M., Beecks, C. & Glorius, F. Plastic Template to predict chemical reaction. Chemical 61379-1390 (2020).

    The scientific researcher from Google

  • Schwaller, P., Vaucher, AC, Laino, T. & Reymond, J.-L. Chemical reaction predictions using deep learning. Mach. Learn. Sci. technique. 2015016 (2021).

    The scientific researcher from Google

  • To me, b. And others. A deep educational work frame for the accurate prediction of interaction and its application to highly productive experience. J. Chemical Information 1572 (2023).

    The scientific researcher from Google

  • Coley, CW, Green, Who & Jensen, KF Machine Learning in Complement Synthesis playsisis. ACC. Chemical. Accuracy. 511281-1289 (2018).

    The scientific researcher from Google

  • Segler, MHS, Press, M. & Waller, MP MP Chemical Planning with Deep Nervous Networks and Avoid AI. nature 555604-610 (2018).

    The scientific researcher from Google

  • Coley, CW, Barzilay, R., Jaakkola, TS, Green, Who & Jensen, KF organic reaction predictions using machine learning. ACS Center. Sci. 3434-443 (2017).

    The scientific researcher from Google

  • Tu, Z. & Coley, the sneaking chart form to the CW sequence for the affected by the prediction of molds and the prediction of the response. J. Chem. Inf. model. 623503-3513 (2022).

    The scientific researcher from Google

  • Schwaller, P. et al. Molecular transformer: a model for predicting normative chemical reaction. ACS Center. Sci. 51572-1583 (2019).

    The scientific researcher from Google

  • Lynn, k. Chemical. Sci. 113355-3364 (2020).

    The scientific researcher from Google

  • Zheng, S., RAO, J., Zhang, Z., Xu, J J. Chem. Inf. model. 6047-55 (2020).

    The scientific researcher from Google

  • Kim, E., Lee, D., KWON, Y., Park, MS & Choi, Y.-S. Health, reasonable, and the diversity of cracker Retrospiesis using dual -directional transformers associated with the underlying variables. J. Chem. Inf. model. 61123-133 (2021).

    The scientific researcher from Google

  • Sasha, M. And others. The editing of the Point Point Network: Chemical Reactions Modeling as Series of the Liberation of the Graphic. J. Chem. Inf. model. 613273-3284 (2021).

    The scientific researcher from Google

  • Mao, K. And others. The molecular graph is converter to predict the creation columns. Nervous computing 457193-202 (2021).

    The scientific researcher from Google

  • Irwin, R., Dimitriadis, S., He, J Mach. Learn. Sci. technique. 3015022 (2022).

    The scientific researcher from Google

  • Zhu, J. et al. Double molecular display before training. in Brook. ACM Sigkdd 29 Conference to discover knowledge and extract data (Eds Singh, AK Et Al.) 3615-3627 (ACM, 2023).

  • Lu, J J. Chem. Inf. model. 621376-1387 (2022).

    The scientific researcher from Google

  • LI, S.-w. , XU, L.-C. , Zhang, C., Zhang, S.-Q. & Hong, X. Prediction to perform interaction with an investment and interpretation graphic form based on chemical knowledge. Nat. communication. 143569 (2023).

    The scientific researcher from Google

  • SHI, R., Yu, G., HUO, X. & Yang, Y. Chemical Interaction Fores with multiple wide training. J. Chemical Information 1622 (2024).

    The scientific researcher from Google

  • Xia, c. And others. Mall-Bert: Reflection on the graph before training the nerve networks of the particles. in Brook. The eleventh international conference on learning representations (Eds Yan, L. Et al

  • Ying, C and others. Is the transformer really badly to represent the graph? in The thirty -fifth conference for neurological information processing systems (Neurips 2021) https://openreview.net/pdf?

  • Shi, j. And others. Graphormer measurement on wide molecular modeling collections. Preprint at https://arxiv.org/abs/2203.04810 (2023).

  • Vaswani, a. And others. Attention is all you need. in The thirty -first conference for neurological information processing systems (NIPS 2017) https://papers.nips.cc/paper_files/Paper/2017/file/3f5ee243547deee 91fbd053c1c4a845aa-paper.pdf (2017).

  • Pearlra, D. et al. A platform to examine automatic reaction and the synthesis of the micromol in the flow. sciences 359429-434 (2018).

    The scientific researcher from Google

  • LI, X., Zhang, S., XU, L. & Hong, X. Prediction to re -detect the Root -H – H of heterogeneous bikes through machine learning. Ango. Chemical. int. Mr. Dr. 5913253-13259 (2020).

    The scientific researcher from Google

  • Schneider, n. Stepl, n. J. Chem. Inf. model. 562336-2346 (2016).

    The scientific researcher from Google

  • Dai, H., LI, C., Coley, C., Dai, B. & Song, L. Retrosynthesis predicting with a police graph logic network. in The thirty -third conference for neurological information processing systems (Neurips 2019) https://proceds.neurips.cc/Paper_files/paper/2019/file/0D2B2061826A5df3221116A5085a6052-paper.pdf (neups, 2019).

  • Schwaller, P., Gaudin, T., Lányi, D. Chemical. Sci. 96091-6098 (2018).

    The scientific researcher from Google

  • Jin, and. in The thirty -first conference for neurological information processing systems (NIPS 2017) https://papers.nips.cc/paper_files/paper/2017/file/ced556cd9f9c0c8315cfbe0744A3BAF0-paper.pdf (2017).

  • Arjovsky, M., Bottou, L., Gullrajani, I. & Lopez-paz, D. Preprint in https://arxiv.org/abs/1907.02893 (2020).

  • Schwaller, P. et al. Set the space of chemical reactions using attention -based nerve networks. Nat. Mach. Minds. 3144-152 (2021).

    The scientific researcher from Google

  • Schneider, n. J. Chem. Inf. model. 5539-53 (2015).

    The scientific researcher from Google

  • Schleinitz, J. et al. Nicolit learning predictions of Nicolit, a group of small literature data from Co stimulating nickel. J. AM. Chemical. Suk. 14414722-14730 (2022).

    The scientific researcher from Google

  • Xu, L. et al. Towards the design of data from the asymmetric hydrogenity of the OLC: database and hierarchical learning. Ango. Chemical. int. Mr. Dr. 6022804-22811 (2021).

    The scientific researcher from Google

  • Xu, L.-C. And others. Enantioselectivity by activating Christhh using the knowledge of the state of transition in machine learning. Nat. Informed. 2321-330 (2023).

    The scientific researcher from Google

  • Xu, L.-C. , Tang, M.-J. , A, j. Zenudo https://doi.org/10.5281/zenodo.15770470 (2025).

  • Xu, L.-C. , Tang, M.-J. , A, j. Figshare https://doi.org/10.6084/m9.figshare.28356077 (2025).

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    2025-08-19 00:00:00

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