AI

Bridging chemistry and artificial intelligence by a reaction description language

  • Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).

    Article 

    Google Scholar 

  • Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).

    Article 

    Google Scholar 

  • Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

    Article 

    Google Scholar 

  • Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article 
    MathSciNet 

    Google Scholar 

  • Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    Article 

    Google Scholar 

  • Grisoni, F. Chemical language models for de novo drug design: challenges and opportunities. Curr. Opin. Struct. Biol. 79, 102527 (2023).

    Article 

    Google Scholar 

  • Skinnider, M. A., Stacey, R. G., Wishart, D. S. & Foster, L. J. Chemical language models enable navigation in sparsely populated chemical space. Nat. Mach. Intell. 3, 759–770 (2021).

    Article 

    Google Scholar 

  • Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023).

    Article 

    Google Scholar 

  • Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Article 

    Google Scholar 

  • Krenn, M., Häse, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1, 045024 (2020).

    Article 

    Google Scholar 

  • Kuenneth, C. & Ramprasad, R. polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nat. Commun. 14, 4099 (2023).

    Article 

    Google Scholar 

  • Krenn, M. et al. SELFIES and the future of molecular string representations. Patterns 3, 100588 (2022).

    Article 

    Google Scholar 

  • Schwaller, P. et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572–1583 (2019).

    Article 

    Google Scholar 

  • Liu, B. et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci. 3, 1103–1113 (2017).

    Article 

    Google Scholar 

  • Sun, Y. & Sahinidis, N. V. Computer-aided retrosynthetic design: fundamentals, tools, and outlook. Curr. Opin. Chem. Eng. 35, 100721 (2022).

    Article 

    Google Scholar 

  • Wang, X. et al. RetroPrime: a diverse, plausible and transformer-based method for single-step retrosynthesis predictions. Chem. Eng. J. 420, 129845 (2021).

    Article 

    Google Scholar 

  • Thakkar, A. et al. Unbiasing retrosynthesis language models with disconnection prompts. ACS Cent. Sci. 9, 1488–1498 (2023).

    Article 

    Google Scholar 

  • Huang, T. & Li, Y. Current progress, challenges, and future perspectives of language models for protein representation and protein design. The innovation 4, 100446 (2023).

    Article 

    Google Scholar 

  • Min, B. et al. Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput. Surv. 56, 1–40 (2023).

    Article 

    Google Scholar 

  • Schwaller, P., Hoover, B., Reymond, J.-L., Strobelt, H. & Laino, T. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci. Adv. 7, eabe4166 (2021).

    Article 

    Google Scholar 

  • Schwaller, P. et al. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. 3, 144–152 (2021).

    Article 

    Google Scholar 

  • Strieth-Kalthoff, F. et al. Artificial intelligence for retrosynthetic planning needs both data and expert knowledge. J. Am. Chem. Soc. 146, 11005–11017 (2024).

    Google Scholar 

  • Nugmanov, R. I. et al. CGRtools: Python library for molecule, reaction, and condensed graph of reaction processing. J. Chem. Inf. Model. 59, 2516–2521 (2019).

    Article 

    Google Scholar 

  • Shi, C., Xu, M., Guo, H., Zhang, M. & Tang, J. A graph to graphs framework for retrosynthesis prediction. In Proc. 37th International Conference on Machine Learning (eds Blei, D. et al.) 8818–8827 (PMLR, 2020).

  • Yan, C. et al. Retroxpert: decompose retrosynthesis prediction like a chemist. Adv. Neural Inf. Process. Syst. 33, 11248–11258 (2020).

  • Somnath, V. R., Bunne, C., Coley, C., Krause, A. & Barzilay, R. Learning graph models for retrosynthesis prediction. Adv. Neural Inf. Process. Syst. 34, 9405–9415 (2021).

  • Zhong, W., Yang, Z. & Chen, C. Y.-C. Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing. Nat. Commun. 14, 3009 (2023).

    Article 

    Google Scholar 

  • Wang, Y. et al. Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. Nat. Commun. 14, 6155 (2023).

    Article 

    Google Scholar 

  • Saebi, M. et al. On the use of real-world datasets for reaction yield prediction. Chem. Sci. 14, 4997–5005 (2023).

    Article 

    Google Scholar 

  • Lu, J. & Zhang, Y. Unified deep learning model for multitask reaction predictions with explanation. J. Chem. Inf. Model. 62, 1376–1387 (2022).

    Article 

    Google Scholar 

  • Wan, Y., Hsieh, C.-Y., Liao, B. & Zhang, S. Retroformer: pushing the limits of end-to-end retrosynthesis transformer. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 22475–22490 (PMLR, 2022).

  • Dong, J. et al. Ketones and aldehydes as alkyl radical equivalents for C-H functionalization of heteroarenes. Sci. Adv. 5, eaax9955 (2019).

    Article 

    Google Scholar 

  • Peltzer, R. M., Gauss, J., Eisenstein, O. & Cascella, M. The Grignard reaction–unraveling a chemical puzzle. J. Am. Chem. Soc. 142, 2984–2994 (2020).

    Article 

    Google Scholar 

  • Heravi, M. M., Hashemi, E. & Nazari, N. Negishi coupling: an easy progress for C–C bond construction in total synthesis. Mol. Divers. 18, 441–472 (2014).

    Article 

    Google Scholar 

  • Kotha, S., Lahiri, K. & Kashinath, D. Recent applications of the Suzuki–Miyaura cross-coupling reaction in organic synthesis. Tetrahedron 58, 9633–9695 (2002).

    Article 

    Google Scholar 

  • Zhou, J., Zhao, Z. & Shibata, N. Transition-metal-free silylboronate-mediated cross-couplings of organic fluorides with amines. Nat. Commun. 14, 1847 (2023).

    Article 

    Google Scholar 

  • Vulovic, B., Cinderella, A. P. & Watson, D. A. Palladium-catalyzed cross-coupling of monochlorosilanes and Grignard reagents. ACS Catal. 7, 8113–8117 (2017).

    Article 

    Google Scholar 

  • Xu, W. Q., Xu, X. H. & Qing, F. L. Synthesis and properties of CF3 (OCF3) CH‐substituted arenes and alkenes. Chin. J. Chem. 38, 847–854 (2020).

    Article 

    Google Scholar 

  • Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  • Probst, D., Schwaller, P. & Reymond, J.-L. Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digit. Discov. 1, 91–97 (2022).

    Article 

    Google Scholar 

  • Schneider, N., Lowe, D. M., Sayle, R. A. & Landrum, G. A. Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity. J. Chem. Inf. Model. 55, 39–53 (2015).

    Article 

    Google Scholar 

  • Kajino, M., Hasuoka, A. & Nishida, H. 1-heterocyclylsulfonyl, 2-aminomethyl, 5- (hetero-) aryl substituted 1-H-pyrrole derivatives as acid secretion inhibitors. Patent WO2007026916A1 (2007).

  • Yu, Q.-Y., Zeng, H., Yao, K., Li, J.-Q. & Liu, Y. Novel and practical synthesis of vonoprazan fumarate. Synth. Commun. 47, 1169–1174 (2017).

    Article 

    Google Scholar 

  • Chen, S. & Jung, Y. Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au. 1, 1612–1620 (2021).

    Article 

    Google Scholar 

  • Tetko, I. V., Karpov, P., Van Deursen, R. & Godin, G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11, 5575 (2020).

    Article 

    Google Scholar 

  • Zhong, Z. et al. Root-aligned SMILES: a tight representation for chemical reaction prediction. Chem. Sci. 13, 9023–9034 (2022).

    Article 

    Google Scholar 

  • Irwin, R., Dimitriadis, S., He, J. & Bjerrum, E. J. Chemformer: a pre-trained transformer for computational chemistry. Mach. Learn. Sci. Technol. 3, 015022 (2022).

    Article 

    Google Scholar 

  • Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52, D1180–D1192 (2024).

    Article 

    Google Scholar 

  • Tingle, B. I. et al. ZINC-22—a free multi-billion-scale database of tangible compounds for ligand discovery. J. Chem. Inf. Model. 63, 1166–1176 (2023).

    Article 

    Google Scholar 

  • Chilingaryan, G. et al. BartSmiles: generative masked language models for molecular representations. J. Chem. Inf. Model. 64, 5832–5843 (2024).

    Article 

    Google Scholar 

  • zw-SIMM & Xiong, J. jiachengxiong/ReactSeq: ReactSeq (1.0). Zenodo https://doi.org/10.5281/zenodo.13338263 (2024).

  • Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3, 1237–1245 (2017).

    Article 

    Google Scholar 

  • Segler, M. H. & Waller, M. P. Neural‐symbolic machine learning for retrosynthesis and reaction prediction. Chem-Eur. J. 23, 5966–5971 (2017).

    Article 

    Google Scholar 

  • Dai, H., Li, C., Coley, C., Dai, B. & Song, L. Retrosynthesis prediction with conditional graph logic network. Adv. Neural Inf. Process. Syst. 32, 8872–8882 (2019).

  • Sacha, M. et al. Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits. J. Chem. Inf. Model. 61, 3273–3284 (2021).

    Article 

    Google Scholar 

  • Chen, Z., Ayinde, O. R., Fuchs, J. R., Sun, H. & Ning, X. G2Retro as a two-step graph generative models for retrosynthesis prediction. Commun. Chem. 6, 102 (2023).

    Article 

    Google Scholar 

  • Yao, L. et al. Node-aligned graph-to-graph: elevating template-free deep learning approaches in single-step retrosynthesis. JACS Au. 4, 992–1003 (2024).

    Article 

    Google Scholar 

  • Liu, X. et al. RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation. Bioinformatics 40, btae561 (2024).

    Article 

    Google Scholar 

  • Zheng, S., Rao, J., Zhang, Z., Xu, J. & Yang, Y. Predicting retrosynthetic reactions using self-corrected transformer neural networks. J. Chem. Inf. Model. 60, 47–55 (2019).

    Article 

    Google Scholar 

  • Sun, R., Dai, H., Li, L., Kearnes, S. & Dai, B. Towards understanding retrosynthesis by energy-based models. Adv. Neural Inf. Process. Syst. 34, 10186–10194 (2021).

  • Don’t miss more hot News like this! AI/" target="_blank" rel="noopener">Click here to discover the latest in AI news!

    2025-05-13 00:00:00

    Related Articles

    Back to top button