{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:51:51Z","timestamp":1781801511742,"version":"3.54.5"},"reference-count":210,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1007\/s11704-025-50127-3","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:34:56Z","timestamp":1771508096000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Survey on recent progress of AI for chemistry: methods, applications, and opportunities"],"prefix":"10.1007","volume":"20","author":[{"given":"Hu","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxiang","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"50127_CR1","series-title":"Technical Report ANL-20\/17","volume-title":"AI for science: report on the Department of Energy (DOE) town halls on artificial intelligence (AI) for science","author":"R Stevens","year":"2020","unstructured":"Stevens R, Taylor V, Nichols J, Maccabe A B, Yelick K, Brown D. AI for science: report on the Department of Energy (DOE) town halls on artificial intelligence (AI) for science. Technical Report ANL-20\/17. Argonne: Argonne National Laboratory, 2020"},{"issue":"7873","key":"50127_CR2","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, \u017d\u00eddek A, Potapenko A, Bridgland A, Meyer C, Kohl S A A, Ballard A J, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior A W, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873): 583\u2013589","journal-title":"Nature"},{"key":"50127_CR3","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686\u2013707","journal-title":"Journal of Computational Physics"},{"key":"50127_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/9783527635641","volume-title":"The Chemical Element: Chemistry\u2019s Contribution to Our Global Future","author":"J Garcia-Martinez","year":"2011","unstructured":"Garcia-Martinez J, Serrano-Torregrosa E. The Chemical Element: Chemistry\u2019s Contribution to Our Global Future. Weinheim: John Wiley & Sons, 2011"},{"key":"50127_CR5","first-page":"30","volume-title":"Proceedings of Hawaii International Conference on System Sciences","author":"B Buchanan","year":"1968","unstructured":"Buchanan B, Sutherland G, Feigenbaum E A. HEURISTIC DENDRAL: a program for generating explanatory hypotheses in organic chemistry. In: Proceedings of Hawaii International Conference on System Sciences. 1968, 30"},{"issue":"11","key":"50127_CR6","doi-asserted-by":"publisher","first-page":"2043","DOI":"10.1021\/jo01299a001","volume":"45","author":"T D Salatin","year":"1980","unstructured":"Salatin T D, Jorgensen W L. Computer-assisted mechanistic evaluation of organic reactions. 1. Overview. The Journal of Organic Chemistry, 1980, 45(11): 2043\u20132051","journal-title":"The Journal of Organic Chemistry"},{"issue":"1","key":"50127_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/0898-5529(88)90006-1","volume":"1","author":"K Funatsu","year":"1988","unstructured":"Funatsu K, Sasaki S I. Computer-assisted organic synthesis design and reaction prediction system, \u201cAIPHOS\u201d. Tetrahedron Computer Methodology, 1988, 1(1): 27\u201337","journal-title":"Tetrahedron Computer Methodology"},{"issue":"1","key":"50127_CR8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1021\/ci00023a005","volume":"35","author":"H Satoh","year":"1995","unstructured":"Satoh H, Funatsu K. SOPHIA, a knowledge base-guided reaction prediction system-utilization of a knowledge base derived from a reaction database. Journal of Chemical Information and Computer Sciences, 1995, 35(1): 34\u201344","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"6385","key":"50127_CR9","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1126\/science.aar5169","volume":"360","author":"D T Ahneman","year":"2018","unstructured":"Ahneman D T, Estrada J G, Lin S, Dreher S D, Doyle A G. Predicting reaction performance in C-N cross-coupling using machine learning. Science, 2018, 360(6385): 186\u2013190","journal-title":"Science"},{"key":"50127_CR10","first-page":"1","volume-title":"Proceedings of the 34th Annual Conference on Neural Information Processing Systems 2020","author":"T B Brown","year":"2020","unstructured":"Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, et al. Language models are few-shot learners. In: Proceedings of the 34th Annual Conference on Neural Information Processing Systems 2020. 2020, 1\u201325"},{"key":"50127_CR11","unstructured":"OpenAI. GPT-4 technical report. 2023, arXiv preprint arXiv: 2303.08774"},{"key":"50127_CR12","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G. LLaMA: open and efficient foundation language models. 2023, arXiv preprint arXiv: 2302.13971"},{"key":"50127_CR13","unstructured":"Zhao W, Zhou K, Li J, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z, Du Y, Yang C, Chen Y, Chen Z, Jiang J, Ren R, Li Y, Tang X, Liu Z, Liu P, Nie J Y, Wen J R. A survey of large language models. 2023, arXiv preprint arXiv: 2303.18223"},{"issue":"5","key":"50127_CR14","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s42256-024-00832-8","volume":"6","author":"A M Bran","year":"2024","unstructured":"Bran A M, Cox S, Schilter O, Baldassari C, White A D, Schwaller P. Augmenting large language models with chemistry tools. Nature Machine Intelligence, 2024, 6(5): 525\u2013535","journal-title":"Nature Machine Intelligence"},{"issue":"7992","key":"50127_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1038\/s41586-023-06792-0","volume":"624","author":"D A Boiko","year":"2023","unstructured":"Boiko D A, MacKnight R, Kline B, Gomes G. Autonomous chemical research with large language models. Nature, 2023, 624(7992): 570\u2013578","journal-title":"Nature"},{"issue":"1","key":"50127_CR16","first-page":"015016","volume":"2","author":"P Schwaller","year":"2021","unstructured":"Schwaller P, Vaucher A C, Laino T, Reymond J L. Prediction of chemical reaction yields using deep learning. Machine Learning: Science and Technology, 2021, 2(1): 015016","journal-title":"Machine Learning: Science and Technology"},{"key":"50127_CR17","first-page":"1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"P Schwaller","year":"2020","unstructured":"Schwaller P, Vaucher A C, Laino T, Reymond J L. Data augmentation strategies to improve reaction yield predictions and estimate uncertainty. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 1\u20136"},{"key":"50127_CR18","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi F E. A survey of deep neural network architectures and their applications. Neurocomputing, 2017, 234: 11\u201326","journal-title":"Neurocomputing"},{"issue":"5","key":"50127_CR19","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1145\/3234150","volume":"51","author":"S Pouyanfar","year":"2019","unstructured":"Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes M P, Shyu M L, Chen S C, Iyengar S S. A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys, 2019, 51(5): 92","journal-title":"ACM Computing Surveys"},{"issue":"16","key":"50127_CR20","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1002\/jcc.24764","volume":"38","author":"G B Goh","year":"2017","unstructured":"Goh G B, Hodas N O, Vishnu A. Deep learning for computational chemistry. Journal of computational chemistry, 2017, 38(16): 1291\u20131307","journal-title":"Journal of computational chemistry"},{"issue":"15","key":"50127_CR21","doi-asserted-by":"publisher","first-page":"6475","DOI":"10.1039\/D2CS00203E","volume":"51","author":"M Sajjan","year":"2022","unstructured":"Sajjan M, Li J, Selvarajan R, Sureshbabu S H, Kale S S, Gupta R, Singh V, Kais S. Quantum machine learning for chemistry and physics. Chemical Society Reviews, 2022, 51(15): 6475\u20136573","journal-title":"Chemical Society Reviews"},{"issue":"8","key":"50127_CR22","doi-asserted-by":"publisher","first-page":"2461","DOI":"10.1007\/s11426-024-2072-4","volume":"67","author":"X Hong","year":"2024","unstructured":"Hong X, Yang Q, Liao K, Pei J, Chen M, Mo F, Lu H, Zhang W B, Zhou H, Chen J, Su L, Zhang S Q, Liu S, Huang X, Sun Y Z, Wang Y, Zhang Z, Yu Z, Luo S, Fu X F, You S L. Ai for organic and polymer synthesis. Science China Chemistry, 2024, 67(8): 2461\u20132496","journal-title":"Science China Chemistry"},{"key":"50127_CR23","unstructured":"Liao C, Yu Y, Mei Y, Wei Y. From words to molecules: a survey of large language models in chemistry. 2024, arXiv preprint arXiv: 2402.01439"},{"key":"50127_CR24","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","volume":"237","author":"L Zhou","year":"2017","unstructured":"Zhou L, Pan S, Wang J, Vasilakos A V. Machine learning on big data: opportunities and challenges. Neurocomputing, 2017, 237: 350\u2013361","journal-title":"Neurocomputing"},{"issue":"29","key":"50127_CR25","doi-asserted-by":"publisher","first-page":"e202204647","DOI":"10.1002\/anie.202204647","volume":"61","author":"F Strieth-Kalthoff","year":"2022","unstructured":"Strieth-Kalthoff F, Sandfort F, K\u00fchnemund M, Sch\u00e4fer F R, Kuchen H, Glorius F. Machine learning for chemical reactivity: the importance of failed experiments. Angewandte Chemie International Edition, 2022, 61(29): e202204647","journal-title":"Angewandte Chemie International Edition"},{"issue":"13","key":"50127_CR26","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1002\/(SICI)1096-987X(199610)17:13<1571::AID-JCC9>3.0.CO;2-P","volume":"17","author":"D Feller","year":"1996","unstructured":"Feller D. The role of databases in support of computational chemistry calculations. Journal of Computational Chemistry, 1996, 17(13): 1571\u20131586","journal-title":"Journal of Computational Chemistry"},{"issue":"7","key":"50127_CR27","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/s41562-023-01562-4","volume":"7","author":"L Liu","year":"2023","unstructured":"Liu L, Jones B F, Uzzi B, Wang D. Data, measurement and empirical methods in the science of science. Nature Human Behaviour, 2023, 7(7): 1046\u20131058","journal-title":"Nature Human Behaviour"},{"issue":"1","key":"50127_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 1988, 28(1): 31\u201336","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"1","key":"50127_CR29","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13321-015-0068-4","volume":"7","author":"S R Heller","year":"2015","unstructured":"Heller S R, McNaught A, Pletnev I, Stein S, Tchekhovskoi D. InChI, the IUPAC international chemical identifier. Journal of Cheminformatics, 2015, 7(1): 23","journal-title":"Journal of Cheminformatics"},{"key":"50127_CR30","unstructured":"Daylight Chemical Information Systems. Smiles arbitrary target specification. See chemeurope.com\/en\/encyclopedia\/Smiles_arbitrary_target_specification.html#google_vignette website, 2025"},{"issue":"4","key":"50127_CR31","first-page":"045024","volume":"1","author":"M Krenn","year":"2020","unstructured":"Krenn M, H\u00e4se F, Nigam A, Friederich P, Aspuru-Guzik A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Machine Learning: Science and Technology, 2020, 1(4): 045024","journal-title":"Machine Learning: Science and Technology"},{"issue":"2","key":"50127_CR32","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1021\/ci00046a002","volume":"25","author":"R E Carhart","year":"1985","unstructured":"Carhart R E, Smith D H, Venkataraghavan R. Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences, 1985, 25(2): 64\u201373","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"6","key":"50127_CR33","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"J L Durant","year":"2002","unstructured":"Durant J L, Leland B A, Henry D R, Nourse J G. Reoptimization of mdl keys for use in drug discovery. Journal of Chemical Information and Computer Sciences, 2002, 42(6): 1273\u20131280","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"1\u20132","key":"50127_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.drudis.2007.09.007","volume":"13","author":"G Wolber","year":"2008","unstructured":"Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discovery Today, 2008, 13(1\u20132): 23\u201329","journal-title":"Drug Discovery Today"},{"issue":"5","key":"50127_CR35","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 2010, 50(5): 742\u2013754","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR36","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ymeth.2014.08.005","volume":"71","author":"A Cereto-Massagu\u00e9","year":"2015","unstructured":"Cereto-Massagu\u00e9 A, Ojeda M J, Valls C, Mulero M, Garcia-Vallv\u00e9 S, Pujadas G. Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71: 58\u201363","journal-title":"Methods"},{"issue":"4","key":"50127_CR37","doi-asserted-by":"publisher","first-page":"332","DOI":"10.2174\/138620709788167980","volume":"12","author":"J L Melville","year":"2009","unstructured":"Melville J L, Burke E K, Hirst J D. Machine learning in virtual screening. Combinatorial Chemistry & High Throughput Screening, 2009, 12(4): 332\u2013343","journal-title":"Combinatorial Chemistry & High Throughput Screening"},{"issue":"6","key":"50127_CR38","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1016\/j.chempr.2020.02.017","volume":"6","author":"F Sandfort","year":"2020","unstructured":"Sandfort F, Strieth-Kalthoff F, K\u00fchnemund M, Beecks C, Glorius F. A structure-based platform for predicting chemical reactivity. Chem, 2020, 6(6): 1379\u20131390","journal-title":"Chem"},{"issue":"2","key":"50127_CR39","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1021\/acs.accounts.0c00699","volume":"54","author":"W P Walters","year":"2021","unstructured":"Walters W P, Barzilay R. Applications of deep learning in molecule generation and molecular property prediction. Accounts of Chemical Research, 2021, 54(2): 263\u2013270","journal-title":"Accounts of Chemical Research"},{"issue":"D1","key":"50127_CR40","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2024","unstructured":"Zdrazil B, Felix E, Hunter F, Manners E J, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Lopez D M, Mosquera J F, Magarinos M P, Bosc N, Arcila R, Kizil\u00f6ren T, Gaulton A, Bento A P, Adasme M F, Monecke P, Landrum G A, Leach A R. The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research, 2024, 52(D1): D1180\u2013D1192","journal-title":"Nucleic Acids Research"},{"issue":"D1","key":"50127_CR41","doi-asserted-by":"publisher","first-page":"D1516","DOI":"10.1093\/nar\/gkae1059","volume":"53","author":"S Kim","year":"2025","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker B A, Thiessen P A, Yu B, Zaslavsky L, Zhang J, Bolton E E. PubChem 2025 update. Nucleic Acids Research, 2025, 53(D1): D1516\u2013D1525","journal-title":"Nucleic Acids Research"},{"issue":"12","key":"50127_CR42","doi-asserted-by":"publisher","first-page":"6065","DOI":"10.1021\/acs.jcim.0c00675","volume":"60","author":"J J Irwin","year":"2020","unstructured":"Irwin J J, Tang K G, Young J, Dandarchuluun C, Wong B R, Khurelbaatar M, Moroz Y S, Mayfield J, Sayle R A. Zinc20\u2014a free ultralarge-scale chemical database for ligand discovery. Journal of Chemical Information and Modeling, 2020, 60(12): 6065\u20136073","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR43","volume-title":"The Principles of Quantum Mechanics","author":"P A M Dirac","year":"1981","unstructured":"Dirac P A M. The Principles of Quantum Mechanics. Oxford: Clarendon Press, 1981"},{"key":"50127_CR44","volume-title":"Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory","author":"A Szabo","year":"1996","unstructured":"Szabo A, Ostlund N S. Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Mineola: Dover Publications, 1996"},{"key":"50127_CR45","volume-title":"Physical Chemistry: A Molecular Approach","author":"D A McQuarrie","year":"1997","unstructured":"McQuarrie D A, Simon J D. Physical Chemistry: A Molecular Approach. Sausalito: University Science Books, 1997"},{"key":"50127_CR46","volume-title":"Introduction to Computational Chemistry","author":"F Jensen","year":"2017","unstructured":"Jensen F. Introduction to Computational Chemistry. Chichester: John Wiley & Sons, 2017"},{"key":"50127_CR47","unstructured":"Quantum machine learning platform. See quantum-machine.org\/ website, 2024"},{"issue":"25","key":"50127_CR48","doi-asserted-by":"publisher","first-page":"8732","DOI":"10.1021\/ja902302h","volume":"131","author":"L C Blum","year":"2009","unstructured":"Blum L C, Reymond J L. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. Journal of the American Chemical Society, 2009, 131(25): 8732\u20138733","journal-title":"Journal of the American Chemical Society"},{"issue":"5","key":"50127_CR49","doi-asserted-by":"publisher","first-page":"058301","DOI":"10.1103\/PhysRevLett.108.058301","volume":"108","author":"M Rupp","year":"2012","unstructured":"Rupp M, Tkatchenko A, M\u00fcller K R, von Lilienfeld O A. Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters, 2012, 108(5): 058301","journal-title":"Physical Review Letters"},{"issue":"9","key":"50127_CR50","doi-asserted-by":"publisher","first-page":"095003","DOI":"10.1088\/1367-2630\/15\/9\/095003","volume":"15","author":"G Montavon","year":"2013","unstructured":"Montavon G, Rupp M, Gobre V, Vazquez-Mayagoitia A, Hansen K, Tkatchenko A, M\u00fcller K R, von Lilienfeld O A. Machine learning of molecular electronic properties in chemical compound space. New Journal of Physics, 2013, 15(9): 095003","journal-title":"New Journal of Physics"},{"issue":"11","key":"50127_CR51","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1021\/ci300415d","volume":"52","author":"L Ruddigkeit","year":"2012","unstructured":"Ruddigkeit L, van Deursen R, Blum L C, Reymond J L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. Journal of Chemical Information and Modeling, 2012, 52(11): 2864\u20132875","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"1","key":"50127_CR52","doi-asserted-by":"publisher","first-page":"140022","DOI":"10.1038\/sdata.2014.22","volume":"1","author":"R Ramakrishnan","year":"2014","unstructured":"Ramakrishnan R, Dral P O, Rupp M, von Lilienfeld O A. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 2014, 1(1): 140022","journal-title":"Scientific Data"},{"issue":"8","key":"50127_CR53","doi-asserted-by":"publisher","first-page":"084111","DOI":"10.1063\/1.4928757","volume":"143","author":"R Ramakrishnan","year":"2015","unstructured":"Ramakrishnan R, Hartmann M, Tapavicza E, von Lilienfeld O A. Electronic spectra from TDDFT and machine learning in chemical space. The Journal of Chemical Physics, 2015, 143(8): 084111","journal-title":"The Journal of Chemical Physics"},{"issue":"2","key":"50127_CR54","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1063\/1.1730376","volume":"31","author":"B J Alder","year":"1959","unstructured":"Alder B J, Wainwright T E. Studies in molecular dynamics. I. General method. The Journal of Chemical Physics, 1959, 31(2): 459\u2013466","journal-title":"The Journal of Chemical Physics"},{"issue":"4","key":"50127_CR55","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1021\/acs.jcim.6b00778","volume":"57","author":"S Riniker","year":"2017","unstructured":"Riniker S. Molecular dynamics fingerprints (MDFP): machine learning from md data to predict free-energy differences. Journal of Chemical Information and Modeling, 2017, 57(4): 726\u2013741","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"5","key":"50127_CR56","doi-asserted-by":"publisher","first-page":"e1603015","DOI":"10.1126\/sciadv.1603015","volume":"3","author":"S Chmiela","year":"2017","unstructured":"Chmiela S, Tkatchenko A, Sauceda H E, Poltavsky I, Sch\u00fctt K T, M\u00fcller K R. Machine learning of accurate energy-conserving molecular force fields. Science Advances, 2017, 3(5): e1603015","journal-title":"Science Advances"},{"issue":"1","key":"50127_CR57","doi-asserted-by":"publisher","first-page":"3887","DOI":"10.1038\/s41467-018-06169-2","volume":"9","author":"S Chmiela","year":"2018","unstructured":"Chmiela S, Sauceda H E, M\u00fcller K R, Tkatchenko A. Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications, 2018, 9(1): 3887","journal-title":"Nature Communications"},{"key":"50127_CR58","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-40245-7_7","volume-title":"Machine Learning Meets Quantum Physics","author":"S Chmiela","year":"2020","unstructured":"Chmiela S, Sauceda H E, Tkatchenko A, M\u00fcller K R. Accurate molecular dynamics enabled by efficient physically constrained machine learning approaches. In: Sch\u00fctt K T, Chmiela S, von Lilienfeld O A, Tkatchenko A, Tsuda K, M\u00fcller K R, eds. Machine Learning Meets Quantum Physics. Cham: Springer, 2020, 129\u2013154"},{"issue":"24","key":"50127_CR59","doi-asserted-by":"publisher","first-page":"7428","DOI":"10.3390\/molecules26247428","volume":"26","author":"H Sakiyama","year":"2021","unstructured":"Sakiyama H, Fukuda M, Okuno T. Prediction of blood-brain barrier penetration (BBBP) based on molecular descriptors of the freeform and in-blood-form datasets. Molecules, 2021, 26(24): 7428","journal-title":"Molecules"},{"issue":"7","key":"50127_CR60","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s10822-014-9747-x","volume":"28","author":"D L Mobley","year":"2014","unstructured":"Mobley D L, Guthrie J P. FreeSolv: a database of experimental and calculated hydration free energies, with input files. Journal of Computer-Aided Molecular Design, 2014, 28(7): 711\u2013720","journal-title":"Journal of Computer-Aided Molecular Design"},{"key":"50127_CR61","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","volume":"3","author":"A Mayr","year":"2016","unstructured":"Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using deep learning. Frontiers in Environmental Science, 2016, 3: 80","journal-title":"Frontiers in Environmental Science"},{"key":"50127_CR62","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3389\/fenvs.2015.00085","volume":"3","author":"R Huang","year":"2016","unstructured":"Huang R, Xia M, Nguyen D T, Zhao T, Sakamuru S, Zhao J, Shahane S A, Rossoshek A, Simeonov A. Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers in Environmental Science, 2016, 3: 85","journal-title":"Frontiers in Environmental Science"},{"key":"50127_CR63","first-page":"287","volume-title":"Proceedings of Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition","author":"K Riesen","year":"2008","unstructured":"Riesen K, Bunke H. IAM graph database repository for graph based pattern recognition and machine learning. In: Proceedings of Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition. 2008, 287\u2013297"},{"issue":"2","key":"50127_CR64","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg E N, Gomes J, Geniesse C, Pappu A S, Leswing K, Pande V. MoleculeNet: a benchmark for molecular machine learning. Chemical Science, 2018, 9(2): 513\u2013530","journal-title":"Chemical Science"},{"key":"50127_CR65","series-title":"Working Paper","volume-title":"The USPTO patent assignment dataset: descriptions and analysis","author":"A C Marco","year":"2015","unstructured":"Marco A C, Myers A, Graham S, D\u2019Agostino P, Apple K. The USPTO patent assignment dataset: descriptions and analysis. Working Paper No. 2015-2. Alexandria: Office of Chief Economist, U. S. Patent and Trademark Office, 2015"},{"issue":"12","key":"50127_CR66","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1021\/acs.jcim.6b00564","volume":"56","author":"N Schneider","year":"2016","unstructured":"Schneider N, Stiefl N, Landrum G A. What\u2019s what: the (nearly) definitive guide to reaction role assignment. Journal of Chemical Information and Modeling, 2016, 56(12): 2336\u20132346","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR67","first-page":"150","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"B Chen","year":"2020","unstructured":"Chen B, Li C, Dai H, Song L. Retro*: learning retrosynthetic planning with neural guided a* search. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 150"},{"issue":"2","key":"50127_CR68","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/s42256-020-00284-w","volume":"3","author":"P Schwaller","year":"2021","unstructured":"Schwaller P, Probst D, Vaucher A C, Nair V H, Kreutter D, Laino T, Reymond J L. Mapping the space of chemical reactions using attention-based neural networks. Nature Machine Intelligence, 2021, 3(2): 144\u2013152","journal-title":"Nature Machine Intelligence"},{"issue":"6374","key":"50127_CR69","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1126\/science.aap9112","volume":"359","author":"D Perera","year":"2018","unstructured":"Perera D, Tucker J W, Brahmbhatt S, Helal C J, Chong A, Farrell W, Richardson P, Sach N W. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science, 2018, 359(6374): 429\u2013434","journal-title":"Science"},{"issue":"19","key":"50127_CR70","doi-asserted-by":"publisher","first-page":"4997","DOI":"10.1039\/D2SC06041H","volume":"14","author":"M Saebi","year":"2023","unstructured":"Saebi M, Nan B, Herr J E, Wahlers J, Guo Z, Zurha\u0144ski A M, Kogej T, Norrby P O, Doyle A G, Chawla N V, Wiest O. On the use of real-world datasets for reaction yield prediction. Chemical Science, 2023, 14(19): 4997\u20135005","journal-title":"Chemical Science"},{"issue":"1","key":"50127_CR71","doi-asserted-by":"publisher","first-page":"5505","DOI":"10.1038\/s41467-020-19267-x","volume":"11","author":"S Stocker","year":"2020","unstructured":"Stocker S, Cs\u00e1nyi G, Reuter K, Margraf J T. Machine learning in chemical reaction space. Nature Communications, 2020, 11(1): 5505","journal-title":"Nature Communications"},{"issue":"10","key":"50127_CR72","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1021\/acscentsci.1c00535","volume":"7","author":"W L Williams","year":"2021","unstructured":"Williams W L, Zeng L, Gensch T, Sigman M S, Doyle A G, Anslyn E V. The evolution of data-driven modeling in organic chemistry. ACS Central Science, 2021, 7(10): 1622\u20131637","journal-title":"ACS Central Science"},{"key":"50127_CR73","volume-title":"Reaxys: a leading chemistry database","author":"Elsevier","year":"2024","unstructured":"Elsevier. Reaxys: a leading chemistry database. See elsevier.com\/products\/reaxys website, 2024"},{"issue":"12","key":"50127_CR74","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1021\/ci900437n","volume":"49","author":"J Goodman","year":"2009","unstructured":"Goodman J. Computer software review: reaxys. Journal of Chemical Information and Modeling, 2009, 49(12): 2897\u20132898","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"6","key":"50127_CR75","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1021\/acsmedchemlett.7b00165","volume":"8","author":"M Shevlin","year":"2017","unstructured":"Shevlin M. Practical high-throughput experimentation for chemists. ACS Medicinal Chemistry Letters, 2017, 8(6): 601\u2013607","journal-title":"ACS Medicinal Chemistry Letters"},{"issue":"12","key":"50127_CR76","doi-asserted-by":"publisher","first-page":"2976","DOI":"10.1021\/acs.accounts.7b00428","volume":"50","author":"S W Krska","year":"2017","unstructured":"Krska S W, DiRocco D A, Dreher S D, Shevlin M. The evolution of chemical high-throughput experimentation to address challenging problems in pharmaceutical synthesis. Accounts of Chemical Research, 2017, 50(12): 2976\u20132985","journal-title":"Accounts of Chemical Research"},{"issue":"22","key":"50127_CR77","doi-asserted-by":"publisher","first-page":"6655","DOI":"10.1039\/D1SC06932B","volume":"13","author":"E Shim","year":"2022","unstructured":"Shim E, Kammeraad J A, Xu Z, Tewari A, Cernak T, Zimmerman P M. Predicting reaction conditions from limited data through active transfer learning. Chemical Science, 2022, 13(22): 6655\u20136668","journal-title":"Chemical Science"},{"issue":"7844","key":"50127_CR78","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-021-03213-y","volume":"590","author":"B J Shields","year":"2021","unstructured":"Shields B J, Stevens J, Li J, Parasram M, Damani F, Alvarado J I M, Janey J M, Adams R P, Doyle A G. Bayesian reaction optimization as a tool for chemical synthesis. Nature, 2021, 590(7844): 89\u201396","journal-title":"Nature"},{"issue":"43","key":"50127_CR79","doi-asserted-by":"publisher","first-page":"14459","DOI":"10.1039\/D1SC02087K","volume":"12","author":"Y Gong","year":"2021","unstructured":"Gong Y, Xue D, Chuai G, Yu J, Liu Q. DeepReac+: deep active learning for quantitative modeling of organic chemical reactions. Chemical Science, 2021, 12(43): 14459\u201314472","journal-title":"Chemical Science"},{"issue":"10","key":"50127_CR80","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"S J Pan","year":"2010","unstructured":"Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345\u20131359","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"9","key":"50127_CR81","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1145\/3472291","volume":"54","author":"P Ren","year":"2022","unstructured":"Ren P, Xiao Y, Chang X, Huang P Y, Li Z, Gupta B B, Chen X, Wang X. A survey of deep active learning. ACM Computing Surveys, 2022, 54(9): 180","journal-title":"ACM Computing Surveys"},{"issue":"8","key":"50127_CR82","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798\u20131828","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"50127_CR83","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1021\/jacsau.2c00498","volume":"2","author":"V Talanquer","year":"2022","unstructured":"Talanquer V. The complexity of reasoning about and with chemical representations. JACS Au, 2022, 2(12): 2658\u20132669","journal-title":"JACS Au"},{"issue":"8","key":"50127_CR84","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. Journal of computer-aided molecular design, 2016, 30(8): 595\u2013608","journal-title":"Journal of computer-aided molecular design"},{"key":"50127_CR85","first-page":"1263","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"J Gilmer","year":"2017","unstructured":"Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G E. Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 1263\u20131272"},{"key":"50127_CR86","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: a review of methods and applications. AI Open, 2020, 1: 57\u201381","journal-title":"AI Open"},{"issue":"1","key":"50127_CR87","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13321-020-00479-8","volume":"13","author":"D Jiang","year":"2021","unstructured":"Jiang D, Wu Z, Hsieh C Y, Chen G, Liao B, Wang Z, Shen C, Cao D, Wu J, Hou T. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptorbased and graph-based models. Journal of Cheminformatics, 2021, 13(1): 12","journal-title":"Journal of Cheminformatics"},{"key":"50127_CR88","first-page":"6000","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000\u20136010"},{"issue":"7","key":"50127_CR89","doi-asserted-by":"publisher","first-page":"e26870","DOI":"10.1002\/qua.26870","volume":"122","author":"S Raghunathan","year":"2022","unstructured":"Raghunathan S, Priyakumar U D. Molecular representations for machine learning applications in chemistry. International Journal of Quantum Chemistry, 2022, 122(7): e26870","journal-title":"International Journal of Quantum Chemistry"},{"issue":"1","key":"50127_CR90","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu P S. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4\u201324","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"50127_CR91","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi A C, Hagenbuchner M, Monfardini G. The graph neural network model. IEEE Transactions on Neural Networks, 2009, 20(1): 61\u201380","journal-title":"IEEE Transactions on Neural Networks"},{"key":"50127_CR92","first-page":"1","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"T N Kipf","year":"2017","unstructured":"Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2017, 1\u201314"},{"key":"50127_CR93","first-page":"1","volume-title":"Proceedings of the 6th International Conference on Learning Representations","author":"P Veli\u010dkovi\u0107","year":"2018","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y. Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations. 2018, 1\u201312"},{"key":"50127_CR94","first-page":"1025","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"W L Hamilton","year":"2017","unstructured":"Hamilton W L, Ying Z, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 1025\u20131035"},{"issue":"1","key":"50127_CR95","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13321-020-00435-6","volume":"12","author":"R Kojima","year":"2020","unstructured":"Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y. kGCN: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics, 2020, 12(1): 32","journal-title":"Journal of Cheminformatics"},{"issue":"5","key":"50127_CR96","doi-asserted-by":"publisher","first-page":"bbac303","DOI":"10.1093\/bib\/bbac303","volume":"23","author":"H Liu","year":"2022","unstructured":"Liu H, Huang Y, Liu X, Deng L. Attention-wise masked graph contrastive learning for predicting molecular property. Briefings in Bioinformatics, 2022, 23(5): bbac303","journal-title":"Briefings in Bioinformatics"},{"key":"50127_CR97","doi-asserted-by":"publisher","first-page":"106524","DOI":"10.1016\/j.compbiomed.2022.106524","volume":"153","author":"J Liu","year":"2023","unstructured":"Liu J, Lei X, Zhang Y, Pan Y. The prediction of molecular toxicity based on BiGRU and GraphSAGE. Computers in Biology and Medicine, 2023, 153: 106524","journal-title":"Computers in Biology and Medicine"},{"key":"50127_CR98","first-page":"1","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"G Zhou","year":"2023","unstructured":"Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Ke G. Uni-mol: a universal 3D molecular representation learning framework. In: Proceedings of the 11th International Conference on Learning Representations. 2023, 1\u201331"},{"issue":"1","key":"50127_CR99","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/s43246-022-00315-6","volume":"3","author":"P Reiser","year":"2022","unstructured":"Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, van Hoesel C, Schopmans H, Sommer T, Friederich P. Graph neural networks for materials science and chemistry. Communications Materials, 2022, 3(1): 93","journal-title":"Communications Materials"},{"key":"50127_CR100","first-page":"1","volume-title":"Proceedings of the 10th International Conference on International Conference on Learning Representations","author":"J Godwin","year":"2022","unstructured":"Godwin J, Schaarschmidt M, Gaunt A L, Sanchez-Gonzalez A, Rubanova Y, Veli\u010dkovi\u0107 P, Kirkpatrick J, Battaglia P. Simple GNN regularisation for 3D molecular property prediction and beyond. In: Proceedings of the 10th International Conference on International Conference on Learning Representations. 2022, 1\u201323"},{"key":"50127_CR101","first-page":"1597","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"T Chen","year":"2020","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 1597\u20131607"},{"key":"50127_CR102","first-page":"533","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"S Feng","year":"2024","unstructured":"Feng S, Ni Y, Li M, Huang Y, Ma Z M, Ma W Y, Lan Y. UniCorn: a unified contrastive learning approach for multi-view molecular representation learning. In: Proceedings of the 41st International Conference on Machine Learning. 2024, 533"},{"key":"50127_CR103","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.neucom.2021.06.037","volume":"457","author":"K Mao","year":"2021","unstructured":"Mao K, Xiao X, Xu T, Rong Y, Huang J, Zhao P. Molecular graph enhanced transformer for retrosynthesis prediction. Neurocomputing, 2021, 457: 193\u2013202","journal-title":"Neurocomputing"},{"key":"50127_CR104","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1145\/3307339.3342186","volume-title":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","author":"S Wang","year":"2019","unstructured":"Wang S, Guo Y, Wang Y, Sun H, Huang J. SMILES-BERT: large scale unsupervised pre-training for molecular property prediction. In: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2019, 429\u2013436"},{"issue":"9","key":"50127_CR105","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1021\/acscentsci.9b00576","volume":"5","author":"P Schwaller","year":"2019","unstructured":"Schwaller P, Laino T, Gaudin T, Bolgar P, Hunter C A, Bekas C, Lee A A. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS central science, 2019, 5(9): 1572\u20131583","journal-title":"ACS central science"},{"issue":"1","key":"50127_CR106","doi-asserted-by":"publisher","first-page":"5575","DOI":"10.1038\/s41467-020-19266-y","volume":"11","author":"I V Tetko","year":"2020","unstructured":"Tetko I V, Karpov P, Van Deursen R, Godin G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nature Communications, 2020, 11(1): 5575","journal-title":"Nature Communications"},{"issue":"1","key":"50127_CR107","doi-asserted-by":"publisher","first-page":"8799","DOI":"10.1038\/s41598-023-35648-w","volume":"13","author":"E Mazuz","year":"2023","unstructured":"Mazuz E, Shtar G, Shapira B, Rokach L. Molecule generation using transformers and policy gradient reinforcement learning. Scientific Reports, 2023, 13(1): 8799","journal-title":"Scientific Reports"},{"key":"50127_CR108","first-page":"4171","volume-title":"Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics","author":"J Devlin","year":"2019","unstructured":"Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics. 2019, 4171\u20134186"},{"issue":"1","key":"50127_CR109","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1021\/ci5006614","volume":"55","author":"N Schneider","year":"2015","unstructured":"Schneider N, Lowe D M, Sayle R A, Landrum G A. Development of a novel fingerprint for chemical reactions and its application to largescale reaction classification and similarity. Journal of Chemical Information and Modeling, 2015, 55(1): 39\u201353","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"2","key":"50127_CR110","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1039\/D1DD00006C","volume":"1","author":"D Probst","year":"2022","unstructured":"Probst D, Schwaller P, Reymond J L. Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital Discovery, 2022, 1(2): 91\u201397","journal-title":"Digital Discovery"},{"issue":"2","key":"50127_CR111","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1039\/C8SC04228D","volume":"10","author":"C W Coley","year":"2019","unstructured":"Coley C W, Jin W, Rogers L, Jamison T F, Jaakkola T S, Green W H, Barzilay R, Jensen K F. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 2019, 10(2): 370\u2013377","journal-title":"Chemical Science"},{"issue":"1","key":"50127_CR112","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s42004-023-00825-5","volume":"6","author":"X Zang","year":"2023","unstructured":"Zang X, Zhao X, Tang B. Hierarchical molecular graph selfsupervised learning for property prediction. Communications Chemistry, 2023, 6(1): 34","journal-title":"Communications Chemistry"},{"issue":"1","key":"50127_CR113","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13321-021-00579-z","volume":"14","author":"Y Kwon","year":"2022","unstructured":"Kwon Y, Lee D, Choi Y S, Kang S. Uncertainty-aware prediction of chemical reaction yields with graph neural networks. Journal of Cheminformatics, 2022, 14(1): 2","journal-title":"Journal of Cheminformatics"},{"key":"50127_CR114","doi-asserted-by":"publisher","first-page":"0292","DOI":"10.34133\/research.0292","volume":"7","author":"X Yin","year":"2024","unstructured":"Yin X, Hsieh C Y, Wang X, Wu Z, Ye Q, Bao H, Deng Y, Chen H, Luo P, Liu H, Hou T, Yao X. Enhancing generic reaction yield prediction through reaction condition-based contrastive learning. Research, 2024, 7: 0292","journal-title":"Research"},{"issue":"2","key":"50127_CR115","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1021\/acs.jcim.8b00803","volume":"59","author":"S Zheng","year":"2019","unstructured":"Zheng S, Yan X, Yang Y, Xu J. Identifying structure-property relationships through SMILES syntax analysis with self-attention mechanism. Journal of Chemical Information and Modeling, 2019, 59(2): 914\u2013923","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR116","first-page":"1052","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence","author":"C Lu","year":"2019","unstructured":"Lu C, Liu Q, Wang C, Huang Z, Lin P, He L. Molecular property prediction: a multilevel quantum interactions modeling perspective. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 1052\u20131060"},{"issue":"9","key":"50127_CR117","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1038\/s42256-022-00526-z","volume":"4","author":"S Chen","year":"2022","unstructured":"Chen S, Jung Y. A generalized-template-based graph neural network for accurate organic reactivity prediction. Nature Machine Intelligence, 2022, 4(9): 772\u2013780","journal-title":"Nature Machine Intelligence"},{"issue":"6","key":"50127_CR118","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1021\/acs.jcim.1c01467","volume":"62","author":"J Lu","year":"2022","unstructured":"Lu J, Zhang Y. Unified deep learning model for multitask reaction predictions with explanation. Journal of Chemical Information and Modeling, 2022, 62(6): 1376\u20131387","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"7997","key":"50127_CR119","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s41586-023-06887-8","volume":"626","author":"F Wong","year":"2024","unstructured":"Wong F, Zheng E J, Valeri J A, Donghia N M, Anahtar M N, Omori S, Li A, Cubillos-Ruiz A, Krishnan A, Jin W, Manson A L, Friedrichs J, Helbig R, Hajian B, Fiejtek D K, Wagner F F, Soutter H H, Earl A M, Stokes J M, Renner L D, Collins J J. Discovery of a structural class of antibiotics with explainable deep learning. Nature, 2024, 626(7997): 177\u2013185","journal-title":"Nature"},{"issue":"1","key":"50127_CR120","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1021\/acs.jcim.3c01250","volume":"64","author":"E Heid","year":"2024","unstructured":"Heid E, Greenman K P, Chung Y, Li S C, Graff D E, Vermeire F H, Wu H, Green W H, McGill C J. Chemprop: a machine learning package for chemical property prediction. Journal of Chemical Information and Modeling, 2024, 64(1): 9\u201317","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"7019","key":"50127_CR121","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1038\/nature03197","volume":"432","author":"B K Shoichet","year":"2004","unstructured":"Shoichet B K. Virtual screening of chemical libraries. Nature, 2004, 432(7019): 862\u2013865","journal-title":"Nature"},{"issue":"4","key":"50127_CR122","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","volume":"180","author":"J M Stokes","year":"2020","unstructured":"Stokes J M, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia N M, MacNair C R, French S, Carfrae L A, Bloom-Ackermann Z, Tran V M, Chiappino-Pepe A, Badran A H, Andrews I W, Chory E J, Church G M, Brown E D, Jaakkola T S, Barzilay R, Collins J J. A deep learning approach to antibiotic discovery. Cell, 2020, 180(4): 688\u2013702","journal-title":"Cell"},{"key":"50127_CR123","first-page":"10684","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"R Rombach","year":"2022","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. Highresolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10684\u201310695."},{"issue":"1","key":"50127_CR124","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"M H S Segler","year":"2018","unstructured":"Segler M H S, Kogej T, Tyrchan C, Waller M P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Science, 2018, 4(1): 120\u2013131","journal-title":"ACS Central Science"},{"issue":"1","key":"50127_CR125","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular denovo design through deep reinforcement learning. Journal of Cheminformatics, 2017, 9(1): 48","journal-title":"Journal of Cheminformatics"},{"issue":"1","key":"50127_CR126","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1021\/acs.jcim.8b00263","volume":"59","author":"S Kang","year":"2019","unstructured":"Kang S, Cho K. Conditional molecular design with deep generative models. Journal of Chemical Information and Modeling, 2019, 59(1): 43\u201352","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"4","key":"50127_CR127","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1561\/2200000056","volume":"12","author":"D P Kingma","year":"2019","unstructured":"Kingma D P, Welling M. An introduction to variational autoencoders. Foundations and Trends\u00ae in Machine Learning, 2019, 12(4): 307\u2013392","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"issue":"7","key":"50127_CR128","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.1021\/acs.jcim.3c00536","volume":"64","author":"J Mao","year":"2024","unstructured":"Mao J, Wang J, Zeb A, Cho K H, Jin H, Kim J, Lee O, Wang Y, No K T. Transformer-based molecular generative model for antiviral drug design. Journal of Chemical Information and Modeling, 2024, 64(7): 2733\u20132745","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"2","key":"50127_CR129","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli R, Wei J N, Duvenaud D, Hern\u00e1ndez-Lobato J M, S\u00e1nchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel T D, Adams R P, Aspuru-Guzik A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 2018, 4(2): 268\u2013276","journal-title":"ACS Central Science"},{"issue":"2","key":"50127_CR130","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1039\/C9SC04026A","volume":"11","author":"R R Griffiths","year":"2020","unstructured":"Griffiths R R, Hern\u00e1ndez-Lobato J M. Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chemical Science, 2020, 11(2): 577\u2013586","journal-title":"Chemical Science"},{"key":"50127_CR131","first-page":"7806","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Q Liu","year":"2018","unstructured":"Liu Q, Allamanis M, Brockschmidt M, Gaunt A L. Constrained graph variational autoencoders for molecule design. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018, 7806\u20137815"},{"key":"50127_CR132","first-page":"2323","volume-title":"Proceedings of the 35th International Conference on machine learning","author":"W Jin","year":"2018","unstructured":"Jin W, Barzilay R, Jaakkola T S. Junction tree variational autoencoder for molecular graph generation. In: Proceedings of the 35th International Conference on machine learning. 2018, 2323\u20132332"},{"key":"50127_CR133","first-page":"4839","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"W Jin","year":"2020","unstructured":"Jin W, Barzilay R, Jaakkola T. Hierarchical generation of molecular graphs using structural motifs. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 4839\u20134848"},{"issue":"4","key":"50127_CR134","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1145\/3626235","volume":"56","author":"L Yang","year":"2024","unstructured":"Yang L, Zhang Z, Song Y, Hong S, Xu R, Zhao Y, Zhang W, Cui B, Yang M H. Diffusion models: a comprehensive survey of methods and applications. ACM Computing Surveys, 2024, 56(4): 105","journal-title":"ACM Computing Surveys"},{"key":"50127_CR135","first-page":"8867","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"E Hoogeboom","year":"2022","unstructured":"Hoogeboom E, Satorras V G, Vignac C, Welling M. Equivariant diffusion for molecule generation in 3D. In: Proceedings of the 39th International Conference on Machine Learning. 2022, 8867\u20138887"},{"issue":"4","key":"50127_CR136","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1038\/s42256-024-00815-9","volume":"6","author":"I Igashov","year":"2024","unstructured":"Igashov I, St\u00e4rk H, Vignac C, Schneuing A, Satorras V G, Frossard P, Welling M, Bronstein M, Correia B. Equivariant 3Dconditional diffusion model for molecular linker design. Nature Machine Intelligence, 2024, 6(4): 417\u2013427","journal-title":"Nature Machine Intelligence"},{"issue":"2","key":"50127_CR137","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1021\/ci00024a021","volume":"35","author":"R P Sheridan","year":"1995","unstructured":"Sheridan R P, Kearsley S K. Using a genetic algorithm to suggest combinatorial libraries. Journal of Chemical Information and Computer Sciences, 1995, 35(2): 310\u2013320","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"1","key":"50127_CR138","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/evco.1996.4.1.1","volume":"4","author":"Z Michalewicz","year":"1996","unstructured":"Michalewicz Z, Schoenauer M. Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 1996, 4(1): 1\u201332","journal-title":"Evolutionary Computation"},{"issue":"5","key":"50127_CR139","doi-asserted-by":"publisher","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","volume":"80","author":"S Katoch","year":"2021","unstructured":"Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021, 80(5): 8091\u20138126","journal-title":"Multimedia Tools and Applications"},{"issue":"12","key":"50127_CR140","doi-asserted-by":"publisher","first-page":"3567","DOI":"10.1039\/C8SC05372C","volume":"10","author":"J H Jensen","year":"2019","unstructured":"Jensen J H. A graph-based genetic algorithm and generative model\/Monte Carlo tree search for the exploration of chemical space. Chemical Science, 2019, 10(12): 3567\u20133572","journal-title":"Chemical Science"},{"key":"50127_CR141","first-page":"1","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"A Nigam","year":"2020","unstructured":"Nigam A, Friederich P, Krenn M, Aspuru-Guzik A. Augmenting genetic algorithms with deep neural networks for exploring the chemical space. In: Proceedings of the 8th International Conference on Learning Representations. 2020, 1\u201314"},{"key":"50127_CR142","first-page":"895","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"T Fu","year":"2022","unstructured":"Fu T, Gao W, Coley C W, Sun J. Reinforced genetic algorithm for structure-based drug design. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 895"},{"key":"50127_CR143","first-page":"6412","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"J You","year":"2018","unstructured":"You J, Liu B, Ying Z, Pande V, Leskovec J. Graph convolutional policy network for goal-directed molecular graph generation. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018, 6412\u20136422"},{"issue":"1","key":"50127_CR144","doi-asserted-by":"publisher","first-page":"10752","DOI":"10.1038\/s41598-019-47148-x","volume":"9","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Kearnes S, Li L, Zare R N, Riley P. Optimization of molecules via deep reinforcement learning. Scientific reports, 2019, 9(1): 10752","journal-title":"Scientific reports"},{"key":"50127_CR145","first-page":"3668","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"S K Gottipati","year":"2020","unstructured":"Gottipati S K, Sattarov B, Niu S, Pathak Y, Wei H, Liu S, Blackburn S, Thomas K, Coley C, Tang J, Chandar S, Bengio Y. Learning to navigate the synthetically accessible chemical space using reinforcement learning. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 3668\u20133679"},{"key":"50127_CR146","first-page":"1","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"T Fu","year":"2022","unstructured":"Fu T, Gao W, Xiao C, Yasonik J, Coley C W, Sun J. Differentiable scaffolding tree for molecule optimization. In: Proceedings of the 10th International Conference on Learning Representations. 2022, 1\u201332"},{"issue":"1","key":"50127_CR147","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1080\/14686996.2017.1401424","volume":"18","author":"X Yang","year":"2017","unstructured":"Yang X, Zhang J, Yoshizoe K, Terayama K, Tsuda K. ChemTS: an efficient python library for de novo molecular generation. Science and Technology of Advanced Materials, 2017, 18(1): 972\u2013976","journal-title":"Science and Technology of Advanced Materials"},{"key":"50127_CR148","first-page":"1007","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"S Ahn","year":"2020","unstructured":"Ahn S, Kim J, Lee H, Shin J. Guiding deep molecular optimization with genetic exploration. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 1007"},{"issue":"4","key":"50127_CR149","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1039\/D2DD00003B","volume":"1","author":"A Nigam","year":"2022","unstructured":"Nigam A, Pollice R, Aspuru-Guzik A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. Digital Discovery, 2022, 1(4): 390\u2013404","journal-title":"Digital Discovery"},{"issue":"6","key":"50127_CR150","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran K, Deisenroth M P, Brundage M, Bharath A A. Deep reinforcement learning: a brief survey. IEEE Signal Processing Magazine, 2017, 34(6): 26\u201338","journal-title":"IEEE Signal Processing Magazine"},{"issue":"12","key":"50127_CR151","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1021\/acscentsci.7b00355","volume":"3","author":"C W Coley","year":"2017","unstructured":"Coley C W, Rogers L, Green W H, Jensen K F. Computer-assisted retrosynthesis based on molecular similarity. ACS Central Science, 2017, 3(12): 1237\u20131245","journal-title":"ACS Central Science"},{"issue":"10","key":"50127_CR152","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1021\/acscentsci.7b00303","volume":"3","author":"B Liu","year":"2017","unstructured":"Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Central Science, 2017, 3(10): 1103\u20131113","journal-title":"ACS Central Science"},{"issue":"7836","key":"50127_CR153","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/s41586-020-2855-y","volume":"588","author":"B Mikulak-Klucznik","year":"2020","unstructured":"Mikulak-Klucznik B, Go\u0142\u0119biowska P, Bayly A A, Popik O, Klucznik T, Szymku\u0107 S, Gajewska E P, Dittwald P, Staszewska-Krajewska O, Beker W, Badowski T, Scheidt K A, Molga K, Mlynarski J, Mrksich M, Grzybowski B A. Computational planning of the synthesis of complex natural products. Nature, 2020, 588(7836): 83\u201388","journal-title":"Nature"},{"issue":"25","key":"50127_CR154","doi-asserted-by":"publisher","first-page":"5966","DOI":"10.1002\/chem.201605499","volume":"23","author":"M H S Segler","year":"2017","unstructured":"Segler M H S, Waller M P. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry-A European Journal, 2017, 23(25): 5966\u20135971","journal-title":"Chemistry-A European Journal"},{"issue":"7698","key":"50127_CR155","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/nature25978","volume":"555","author":"M H S Segler","year":"2018","unstructured":"Segler M H S, Preuss M, Waller M P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 2018, 555(7698): 604\u2013610","journal-title":"Nature"},{"key":"50127_CR156","first-page":"796","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"H Dai","year":"2019","unstructured":"Dai H, Li C, Coley C W, Dai B, Song L. Retrosynthesis prediction with conditional graph logic network. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 796"},{"issue":"10","key":"50127_CR157","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.1021\/jacsau.1c00246","volume":"1","author":"S Chen","year":"2021","unstructured":"Chen S, Jung Y. Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au, 2021, 1(10): 1612\u20131620","journal-title":"JACS Au"},{"issue":"1","key":"50127_CR158","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1021\/acs.jcim.1c01192","volume":"62","author":"E Heid","year":"2022","unstructured":"Heid E, Liu J, Aude A, Green W H. Influence of template size, canonicalization, and exclusivity for retrosynthesis and reaction prediction applications. Journal of Chemical Information and Modeling, 2022, 62(1): 16\u201326","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"12","key":"50127_CR159","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1039\/C9SC05704H","volume":"11","author":"P Schwaller","year":"2020","unstructured":"Schwaller P, Petraglia R, Zullo V, Nair V H, Haeuselmann R A, Pisoni R, Bekas C, Iuliano A, Laino T. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chemical Science, 2020, 11(12): 3316\u20133325","journal-title":"Chemical Science"},{"issue":"25","key":"50127_CR160","doi-asserted-by":"publisher","first-page":"6118","DOI":"10.1002\/chem.201604556","volume":"23","author":"M H S Segler","year":"2017","unstructured":"Segler M H S, Waller M P. Modelling chemical reasoning to predict and invent reactions. Chemistry-A European Journal, 2017, 23(25): 6118\u20136128","journal-title":"Chemistry-A European Journal"},{"key":"50127_CR161","first-page":"944","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"C Yan","year":"2020","unstructured":"Yan C, Ding Q, Zhao P, Zheng S, Yang J, Yu Y, Huang J. RetroXpert: decompose retrosynthesis prediction like a chemist. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 944"},{"issue":"9","key":"50127_CR162","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.3390\/biom12091325","volume":"12","author":"C Yan","year":"2022","unstructured":"Yan C, Zhao P, Lu C, Yu Y, Huang J. Retrocomposer: composing templates for template-based retrosynthesis prediction. Biomolecules, 2022, 12(9): 1325","journal-title":"Biomolecules"},{"issue":"1","key":"50127_CR163","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1021\/acs.jcim.9b00949","volume":"60","author":"S Zheng","year":"2020","unstructured":"Zheng S, Rao J, Zhang Z, Xu J, Yang Y. Predicting retrosynthetic reactions using self-corrected transformer neural networks. Journal of Chemical Information and Modeling, 2020, 60(1): 47\u201355","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR164","unstructured":"Chen B, Shen T, Jaakkola T S, Barzilay R. Learning to make generalizable and diverse predictions for retrosynthesis. 2019, arXiv preprint arXiv: 1910.09688"},{"key":"50127_CR165","first-page":"1","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"W Gao","year":"2022","unstructured":"Gao W, Mercado R, Coley C W. Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design. In: Proceedings of the 10th International Conference on Learning Representations. 2022, 1\u201325"},{"key":"50127_CR166","first-page":"33289","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"S Luo","year":"2024","unstructured":"Luo S, Gao W, Wu Z, Peng J, Coley C W, Ma J. Projecting molecules into synthesizable chemical spaces. In: Proceedings of the 41st International Conference on Machine Learning. 2024, 33289\u201333304"},{"key":"50127_CR167","first-page":"8818","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"C Shi","year":"2020","unstructured":"Shi C, Xu M, Guo H, Zhang M, Tang J. A graph to graphs framework for retrosynthesis prediction. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 8818\u20138827"},{"key":"50127_CR168","first-page":"720","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"V R Somnath","year":"2021","unstructured":"Somnath V R, Bunne C, Coley C W, Krause A, Barzilay R. Learning graph models for retrosynthesis prediction. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 720"},{"issue":"1","key":"50127_CR169","doi-asserted-by":"publisher","first-page":"6404","DOI":"10.1038\/s41467-024-50617-1","volume":"15","author":"Y Han","year":"2024","unstructured":"Han Y, Xu X, Hsieh C Y, Ding K, Xu H, Xu R, Hou T, Zhang Q, Chen H. Retrosynthesis prediction with an iterative string editing model. Nature Communications, 2024, 15(1): 6404","journal-title":"Nature Communications"},{"issue":"12","key":"50127_CR170","doi-asserted-by":"publisher","first-page":"3355","DOI":"10.1039\/C9SC03666K","volume":"11","author":"K Lin","year":"2020","unstructured":"Lin K, Xu Y, Pei J, Lai L. Automatic retrosynthetic route planning using template-free models. Chemical Science, 2020, 11(12): 3355\u20133364","journal-title":"Chemical Science"},{"issue":"1","key":"50127_CR171","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s13321-020-00452-5","volume":"12","author":"R Shibukawa","year":"2020","unstructured":"Shibukawa R, Ishida S, Yoshizoe K, Wasa K, Takasu K, Okuno Y, Terayama K, Tsuda K. ComPret: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration. Journal of Cheminformatics, 2020, 12(1): 52","journal-title":"Journal of Cheminformatics"},{"key":"50127_CR172","first-page":"4014","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","author":"P Han","year":"2022","unstructured":"Han P, Zhao P, Lu C, Huang J, Wu J, Shang S, Yao B, Zhang X. GNN-retro: retrosynthetic planning with graph neural networks. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. 2022, 4014\u20134021"},{"issue":"16","key":"50127_CR173","doi-asserted-by":"publisher","first-page":"9633","DOI":"10.1021\/acs.chemrev.4c00055","volume":"124","author":"G Tom","year":"2024","unstructured":"Tom G, Schmid S P, Baird S G, Cao Y, Darvish K, Hao H, Lo S, Pablo-Garc\u00eda S, Rajaonson E M, Skreta M, Yoshikawa N, Corapi S, Akkoc G D, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-driving laboratories for chemistry and materials science. Chemical Reviews, 2024, 124(16): 9633\u20139732","journal-title":"Chemical Reviews"},{"issue":"3","key":"50127_CR174","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.trechm.2019.02.007","volume":"1","author":"F H\u00e4se","year":"2019","unstructured":"H\u00e4se F, Roch L M, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends in Chemistry, 2019, 1(3): 282\u2013291","journal-title":"Trends in Chemistry"},{"issue":"8","key":"50127_CR175","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1021\/acsmedchemlett.0c00292","volume":"11","author":"E Farrant","year":"2020","unstructured":"Farrant E. Automation of synthesis in medicinal chemistry: progress and challenges. ACS Medicinal Chemistry Letters, 2020, 11(8): 1506\u20131513","journal-title":"ACS Medicinal Chemistry Letters"},{"issue":"7","key":"50127_CR176","doi-asserted-by":"publisher","first-page":"102049","DOI":"10.1016\/j.xcrp.2024.102049","volume":"5","author":"X Jiang","year":"2024","unstructured":"Jiang X, Luo S, Liao K, Jiang S, Ma J, Jiang J, Shuai Z. Artificial intelligence and automation to power the future of chemistry. Cell Reports Physical Science, 2024, 5(7): 102049","journal-title":"Cell Reports Physical Science"},{"issue":"7815","key":"50127_CR177","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1038\/s41586-020-2442-2","volume":"583","author":"B Burger","year":"2020","unstructured":"Burger B, Maffettone P M, Gusev V V, Aitchison C M, Bai Y, Wang X, Li X, Alston B M, Li B, Clowes R, Rankin N, Harris B, Sprick R S, Cooper A I. A mobile robotic chemist. Nature, 2020, 583(7815): 237\u2013241","journal-title":"Nature"},{"key":"50127_CR178","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian Processes for Machine Learning","author":"C E Rasmussen","year":"2005","unstructured":"Rasmussen C E, Williams C K I. Gaussian Processes for Machine Learning. Cambridge: MIT Press, 2005"},{"issue":"10","key":"50127_CR179","doi-asserted-by":"publisher","first-page":"nwac190","DOI":"10.1093\/nsr\/nwac190","volume":"9","author":"Q Zhu","year":"2022","unstructured":"Zhu Q, Zhang F, Huang Y, Xiao H, Zhao L, Zhang X, Song T, Tang X, Li X, He G, Chong B, Zhou J, Zhang Y, Zhang B, Cao J, Luo M, Wang S, Ye G, Zhang W, Chen X, Cong S, Zhou D, Li H, Li J, Zou G, Shang W, Jiang J, Luo Y. An all-round AI-chemist with a scientific mind. National Science Review, 2022, 9(10): nwac190","journal-title":"National Science Review"},{"issue":"7990","key":"50127_CR180","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41586-023-06734-w","volume":"624","author":"N J Szymanski","year":"2023","unstructured":"Szymanski N J, Rendy B, Fei Y, Kumar R E, He T, Milsted D, McDermott M J, Gallant M, Cubuk E D, Merchant A, Kim H, Jain A, Bartel C J, Persson K, Zeng Y, Ceder G. An autonomous laboratory for the accelerated synthesis of novel materials. Nature, 2023, 624(7990): 86\u201391","journal-title":"Nature"},{"issue":"40","key":"50127_CR181","doi-asserted-by":"publisher","first-page":"eabo2626","DOI":"10.1126\/sciadv.abo2626","volume":"8","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Salley D, Sharma A, Keenan G, Mullin M, Cronin L. An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials. Science Advances, 2022, 8(40): eabo2626","journal-title":"Science Advances"},{"key":"50127_CR182","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1145\/2001576.2001606","volume-title":"Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation","author":"J Lehman","year":"2011","unstructured":"Lehman J, Stanley K O. Evolving a diversity of virtual creatures through novelty search and local competition. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. 2011, 211\u2013218"},{"key":"50127_CR183","unstructured":"Mouret J B, Clune J. Illuminating search spaces by mapping elites. 2015, arXiv preprint arXiv: 1504.04909"},{"issue":"2","key":"50127_CR184","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1038\/s42256-023-00788-1","volume":"6","author":"K M Jablonka","year":"2024","unstructured":"Jablonka K M, Schwaller P, Ortega-Guerrero A, Smit B. Leveraging large language models for predictive chemistry. Nature Machine Intelligence, 2024, 6(2): 161\u2013169","journal-title":"Nature Machine Intelligence"},{"issue":"5","key":"50127_CR185","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1039\/D3DD00113J","volume":"2","author":"K M Jablonka","year":"2023","unstructured":"Jablonka K M, Ai Q, Al-Feghali A, Badhwar S, Bocarsly J D, et al. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon. Digital Discovery, 2023, 2(5): 1233\u20131250","journal-title":"Digital Discovery"},{"issue":"8","key":"50127_CR186","first-page":"e","volume":"36","author":"R C Prati","year":"2025","unstructured":"Prati R C. Recent advances in natural language processing in chemistry and materials science. Journal of the Brazilian Chemical Society, 2025, 36(8): e\u201320250067","journal-title":"Journal of the Brazilian Chemical Society"},{"issue":"1","key":"50127_CR187","doi-asserted-by":"publisher","first-page":"4705","DOI":"10.1038\/s41467-024-48998-4","volume":"15","author":"Y Kang","year":"2024","unstructured":"Kang Y, Kim J. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models. Nature Communications, 2024, 15(1): 4705","journal-title":"Nature Communications"},{"issue":"9","key":"50127_CR188","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1021\/acs.jcim.1c00600","volume":"62","author":"V Bagal","year":"2022","unstructured":"Bagal V, Aggarwal R, Vinod P K, Priyakumar U D. MolGPT: molecular generation using a transformer-decoder model. Journal of Chemical Information and Modeling, 2022, 62(9): 2064\u20132076","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR189","unstructured":"Zhang D, Liu W, Tan Q, Chen J, Yan H, Yan Y, Li J, Huang W, Yue X, Zhou D, Zhang S, Su M, Zhong H S, Li Y. ChemLLM: a chemical large language model. 2024, arXiv preprint arXiv: 2402.06852"},{"key":"50127_CR190","unstructured":"Cai Z, Cao M, Chen H, Chen K, Chen K, Chen X, Chen X, Chen Z, Chen Z, Chu P, Dong X, Duan H, Fan Q, Fei Z, Gao Y, Ge J, Gu C, Gu Y, Gui T, Guo A, Guo Q, He C, Hu Y, Huang T, Jiang T, Jiao P, Jin Z, Lei Z, Li J, Li J, Li L, Li S, Li W, Li Y, Liu H, Liu J, Hong J, Liu K, Liu K, Liu X, Lv C, Lv H, Lv K, Ma L, Ma R, Ma Z, Ning W, Ouyang L, Qiu J, Qu Y, Shang F, Shao Y, Song D, Song Z, Sui Z, Sun P, Sun Y, Tang H, Wang B, Wang G, Wang J, Wang J, Wang R, Wang Y, Wang Z, Wei X, Weng Q, Wu F, Xiong Y, Xu C, Xu R, Yan H, Yan Y, Yang X, Ye H, Ying H, Yu J, Yu J, Zang Y, Zhang C, Zhang L, Zhang P, Zhang P, Zhang R, Zhang S, Zhang S, Zhang W, Zhang W, Zhang X, Zhang X, Zhao H, Zhao Q, Zhao X, Zhou F, Zhou Z, Zhuo J, Zou Y, Qiu X, Qiao Y, Lin D. InternLM2 technical report. 2024, arXiv preprint arXiv: 2403.17297"},{"key":"50127_CR191","first-page":"2607","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"T Guo","year":"2023","unstructured":"Guo T, Guo K, Nan B, Liang Z, Guo Z, Chawla N, Wiest O, Zhang X. What can large language models do in chemistry? A comprehensive benchmark on eight tasks. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 2607"},{"key":"50127_CR192","unstructured":"Mirza A, Alampara N, Kunchapu S, Emoekabu B, Krishnan A, Wilhelmi M, Okereke M, Eberhardt J, Elahi A M, Greiner M, Holick C T, Gupta T, Asgari M, Glaubitz C, Klepsch L C, K\u00f6ster Y, Meyer J, Miret S, Hoffmann T, Kreth F A, Ringleb M, Roesner N, Schubert U S, Stafast L M, Wonanke A D D, Pieler M, Schwaller P, Jablonka K M. Are large language models superhuman chemists? 2024, arXiv preprint arXiv: 2404.01475"},{"issue":"1","key":"50127_CR193","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1038\/s41524-020-0308-7","volume":"6","author":"Z Pei","year":"2020","unstructured":"Pei Z, Yin J, Hawk J A, Alman D E, Gao M C. Machine-learning informed prediction of high-entropy solid solution formation: beyond the Hume-Rothery rules. npj Computational Materials, 2020, 6(1): 50","journal-title":"npj Computational Materials"},{"issue":"6","key":"50127_CR194","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1021\/acs.jcim.3c00285","volume":"63","author":"C M Castro Nascimento","year":"2023","unstructured":"Castro Nascimento C M, Pimentel A S. Do large language models understand chemistry? a conversation with ChatGPT. Journal of Chemical Information and Modeling, 2023, 63(6): 1649\u20131655","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"7","key":"50127_CR195","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/s41570-023-00502-0","volume":"7","author":"A D White","year":"2023","unstructured":"White A D. The future of chemistry is language. Nature Reviews Chemistry, 2023, 7(7): 457\u2013458","journal-title":"Nature Reviews Chemistry"},{"key":"50127_CR196","series-title":"Technical Report #1648","volume-title":"Active learning literature survey","author":"B Settles","year":"2009","unstructured":"Settles B. Active learning literature survey. Technical Report #1648. University of Wisconsin-Madison, 2009"},{"issue":"1","key":"50127_CR197","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q. A comprehensive survey on transfer learning. Proceedings of the IEEE, 2021, 109(1): 43\u201376","journal-title":"Proceedings of the IEEE"},{"issue":"1","key":"50127_CR198","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1039\/D3DD00213F","volume":"3","author":"S Back","year":"2024","unstructured":"Back S, Aspuru-Guzik A, Ceriotti M, Gryn\u2019ova G, Grzybowski B, Gu G H, Hein J, Hippalgaonkar K, Horm\u00e1zabal R, Jung Y, Kim S, Kim W Y, Moosavi S M, Noh J, Park C, Schrier J, Schwaller P, Tsuda K, Vegge T, von Lilienfeld O A, Walsh A. Accelerated chemical science with AI. Digital Discovery, 2024, 3(1): 23\u201333","journal-title":"Digital Discovery"},{"issue":"7773","key":"50127_CR199","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1038\/s41586-019-1540-5","volume":"573","author":"X Jia","year":"2019","unstructured":"Jia X, Lynch A, Huang Y, Danielson M, Lang\u2019at I, Milder A, Ruby A E, Wang H, Friedler S A, Norquist A J, Schrier J. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature, 2019, 573(7773): 251\u2013255","journal-title":"Nature"},{"issue":"1","key":"50127_CR200","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/s41467-019-08483-9","volume":"10","author":"S M Moosavi","year":"2019","unstructured":"Moosavi S M, Chidambaram A, Talirz L, Haranczyk M, Stylianou K C, Smit B. Capturing chemical intuition in synthesis of metal-organic frameworks. Nature Communications, 2019, 10(1): 539","journal-title":"Nature Communications"},{"key":"50127_CR201","unstructured":"Rahimian H, Mehrotra S. Distributionally robust optimization: a review. 2019, arXiv preprint arXiv: 1908.05659"},{"issue":"6","key":"50127_CR202","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1038\/s41570-022-00391-9","volume":"6","author":"A Bender","year":"2022","unstructured":"Bender A, Schneider N, Segler M, Patrick Walters W, Engkvist O, Rodrigues T. Evaluation guidelines for machine learning tools in the chemical sciences. Nature Reviews Chemistry, 2022, 6(6): 428\u2013442","journal-title":"Nature Reviews Chemistry"},{"issue":"16","key":"50127_CR203","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","volume":"63","author":"R Rodr\u00edguez-P\u00e9rez","year":"2020","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. Journal of Medicinal Chemistry, 2020, 63(16): 8761\u20138777","journal-title":"Journal of Medicinal Chemistry"},{"issue":"10","key":"50127_CR204","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","volume":"34","author":"R Rodr\u00edguez-P\u00e9rez","year":"2020","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J. Interpretation of machine learning models using Shapley values: application to compound potency and multi-target activity predictions. Journal of Computer-Aided Molecular Design, 2020, 34(10): 1013\u20131026","journal-title":"Journal of Computer-Aided Molecular Design"},{"key":"50127_CR205","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"S M Lundberg","year":"2017","unstructured":"Lundberg S M, Lee S I. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 4768\u20134777"},{"issue":"12","key":"50127_CR206","doi-asserted-by":"publisher","first-page":"5026","DOI":"10.1021\/acs.jcim.9b00538","volume":"59","author":"S Ishida","year":"2019","unstructured":"Ishida S, Terayama K, Kojima R, Takasu K, Okuno Y. Prediction and interpretable visualization of retrosynthetic reactions using graph convolutional networks. Journal of Chemical Information and Modeling, 2019, 59(12): 5026\u20135033","journal-title":"Journal of Chemical Information and Modeling"},{"key":"50127_CR207","first-page":"3319","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"M Sundararajan","year":"2017","unstructured":"Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 3319\u20133328"},{"issue":"5","key":"50127_CR208","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1038\/s42256-023-00654-0","volume":"5","author":"Y Fang","year":"2023","unstructured":"Fang Y, Zhang Q, Zhang N, Chen Z, Zhuang X, Shao X, Fan X, Chen H. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nature Machine Intelligence, 2023, 5(5): 542\u2013553","journal-title":"Nature Machine Intelligence"},{"key":"50127_CR209","doi-asserted-by":"crossref","unstructured":"Hossain D, Saghapour E, Chen J Y. NeSyDPP4-QSAR: a neuro-symbolic AI approach for potent DPP-4-inhibitor discovery in diabetes treatment. 2025, bioRxiv preprint bioRxiv: 2025.03.31.646336","DOI":"10.1101\/2025.03.31.646336"},{"issue":"1","key":"50127_CR210","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41467-024-55374-9","volume":"16","author":"X Zhang","year":"2025","unstructured":"Zhang X, Lin H, Zhang M, Zhou Y, Ma J. A data-driven group retrosynthesis planning model inspired by neurosymbolic programming. Nature Communications, 2025, 16(1): 192","journal-title":"Nature Communications"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-025-50127-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-025-50127-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-025-50127-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:04:50Z","timestamp":1771513490000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-025-50127-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":210,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["50127"],"URL":"https:\/\/doi.org\/10.1007\/s11704-025-50127-3","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"9 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests or financial conflicts to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2011358"}}