Special Topic: AI for Chemistry
Open Access
Natl Sci Open
Volume 3, Number 2, 2024
Special Topic: AI for Chemistry
Article Number 20230037
Number of page(s) 30
Section Chemistry
DOI https://doi.org/10.1360/nso/20230037
Published online 02 November 2023
  • Woodward RB, Doering WE. The total synthesis of quinine. J Am Chem Soc 1945; 67: 860-874. [Article] [Google Scholar]
  • Kohn W, Sham LJ. Self-consistent equations including exchange and correlation effects. Phys Rev 1965; 140: A1133-A1138. [Article] [Google Scholar]
  • Pople JA, Hehre WJ. Computation of electron repulsion integrals involving contracted Gaussian basis functions. J Comput Phys 1978; 27: 161-168. [Article] [Google Scholar]
  • Cole JM. The chemistry of errors. Nat Chem 2022; 14: 973-975. [Article] [Google Scholar]
  • Zhao Q, Savoie BM. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nat Comput Sci 2021; 1: 479-490. [Article] [Google Scholar]
  • Wołos A, Roszak R, Żądło-Dobrowolska A, et al. Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry. Science 2000; 369: eaaw1955. [Article] [Google Scholar]
  • Chen Y, Zhou T, Wu J, et al. Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission. Sci Adv 2023; 9: eadf8437. [Article] [Google Scholar]
  • Gaunt MJ, Janey JM, Schultz DM, et al. Myths of high-throughput experimentation and automation in chemistry. Chem 2021; 7: 2259-2260. [Article] [Google Scholar]
  • Isbrandt ES, Sullivan RJ, Newman SG. High throughput strategies for the discovery and optimization of catalytic reactions. Angew Chem Int Ed 2019; 58: 7180-7191. [Article] [Google Scholar]
  • Burés J, Larrosa I. Organic reaction mechanism classification using machine learning. Nature 2023; 613: 689-695. [Article] [Google Scholar]
  • Shields BJ, Stevens J, Li J, et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 2021; 590: 89-96. [Article] [Google Scholar]
  • Hueffel JA, Sperger T, Funes-Ardoiz I, et al. Accelerated dinuclear palladium catalyst identification through unsupervised machine learning. Science 2021; 374: 1134-1140. [Article] [Google Scholar]
  • Xiouras C, Cameli F, Quilló GL, et al. Applications of artificial intelligence and machine learning algorithms to crystallization. Chem Rev 2022; 122: 13006-13042. [Article] [Google Scholar]
  • Huang HY, Broughton M, Mohseni M, et al. Power of data in quantum machine learning. Nat Commun 2021; 12: 2631. [Article] [Google Scholar]
  • Liu Y, Yang Q, Li Y, et al. Application of machine learning in organic chemistry. Chin J Org Chem 2020; 40: 3812. [Article] [Google Scholar]
  • Strieth-Kalthoff F, Sandfort F, Segler MHS, et al. Machine learning the ropes: Principles, applications and directions in synthetic chemistry. Chem Soc Rev 2020; 49: 6154-6168. [Article] [Google Scholar]
  • Biamonte J, Wittek P, Pancotti N, et al. Quantum machine learning. Nature 2017; 549: 195-202. [Article] [Google Scholar]
  • Merrifield RB. Automated synthesis of peptides. Science 1965; 150: 178-185. [Article] [Google Scholar]
  • Delgado-Licona F, Abolhasani M. Research acceleration in self-driving labs: Technological roadmap toward accelerated materials and molecular discovery. Adv Intell Syst 2023; 5: 2200331. [Article] [Google Scholar]
  • Fasano V, Mykura RC, Fordham JM, et al. Automated stereocontrolled assembly-line synthesis of organic molecules. Nat Synth 2022; 1: 902-907. [Article] [Google Scholar]
  • Buglioni L, Raymenants F, Slattery A, et al. Technological innovations in photochemistry for organic synthesis: Flow chemistry, high-throughput experimentation, scale-up, and photoelectrochemistry. Chem Rev 2022; 122: 2752-2906. [Article] [Google Scholar]
  • Movsisyan M, Delbeke EIP, Berton JKET, et al. Taming hazardous chemistry by continuous flow technology. Chem Soc Rev 2016; 45: 4892-4928. [Article] [Google Scholar]
  • Tsubogo T, Oyamada H, Kobayashi S. Multistep continuous-flow synthesis of (R)- and (S)-rolipram using heterogeneous catalysts. Nature 2015; 520: 329-332. [Article] [Google Scholar]
  • Newby JA, Blaylock DW, Witt PM, et al. Design and application of a low-temperature continuous flow chemistry platform. Org Process Res Dev 2014; 18: 1211-1220. [Article] [Google Scholar]
  • Nambiar AMK, Breen CP, Hart T, et al. Bayesian optimization of computer-proposed multistep synthetic routes on an automated robotic flow platform. ACS Cent Sci 2022; 8: 825-836. [Article] [Google Scholar]
  • Trobe M, Burke MD. The molecular industrial revolution: Automated synthesis of small molecules. Angew Chem Int Ed 2018; 57: 4192-4214. [Article] [Google Scholar]
  • Service RF. The synthesis machine. Science 2015; 347: 1190-1193. [Article] [Google Scholar]
  • Li J, Ballmer SG, Gillis EP, et al. Synthesis of many different types of organic small molecules using one automated process. Science 2015; 347: 1221-1226. [Article] [Google Scholar]
  • Buitrago Santanilla A, Regalado EL, Pereira T, et al. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 2015; 347: 49-53. [Article] [Google Scholar]
  • Ruiz-Castillo P, Buchwald SL. Applications of palladium-catalyzed C-N cross-coupling reactions. Chem Rev 2016; 116: 12564-12649. [Article] [Google Scholar]
  • Ahneman DT, Estrada JG, Lin S, et al. Predicting reaction performance in C-N cross-coupling using machine learning. Science 2018; 360: 186-190. [Article] [Google Scholar]
  • Lin S, Dikler S, Blincoe WD, et al. Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS. Science 2018; 361: eaar6236. [Article] [Google Scholar]
  • Günther A, Jensen KF. Multiphase microfluidics: From flow characteristics to chemical and materials synthesis. Lab Chip 2006; 6: 1487-1503. [Article] [Google Scholar]
  • Hartman RL, McMullen JP, Jensen KF. Deciding whether to go with the flow: Evaluating the merits of flow reactors for synthesis. Angew Chem Int Ed 2011; 50: 7502-7519. [Article] [Google Scholar]
  • Morse PD, Beingessner RL, Jamison TF. Enhanced reaction efficiency in continuous flow. Israel J Chem 2017; 57: 218-227. [Article] [Google Scholar]
  • Chatterjee S, Guidi M, Seeberger PH, et al. Automated radial synthesis of organic molecules. Nature 2020; 579: 379-384. [Article] [Google Scholar]
  • Perera D, Tucker JW, Brahmbhatt S, et al. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 2018; 359: 429-434. [Article] [Google Scholar]
  • Bédard AC, Adamo A, Aroh KC, et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 2018; 361: 1220-1225. [Article] [Google Scholar]
  • Reis M, Gusev F, Taylor NG, et al. Machine-learning-guided discovery of 19F MRI agents enabled by automated copolymer synthesis. J Am Chem Soc 2021; 143: 17677-17689. [Article] [Google Scholar]
  • Steiner S, Wolf J, Glatzel S, et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 2019; 363: eaav2211. [Article] [Google Scholar]
  • Kitson PJ, Marie G, Francoia JP, et al. Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals. Science 2018; 359: 314-319. [Article] [Google Scholar]
  • Granda JM, Donina L, Dragone V, et al. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 2018; 559: 377-381. [Article] [Google Scholar]
  • Mehr SHM, Craven M, Leonov AI, et al. A universal system for digitization and automatic execution of the chemical synthesis literature. Science 2020; 370: 101-108. [Article] [Google Scholar]
  • Rohrbach S, Šiaučiulis M, Chisholm G, et al. Digitization and validation of a chemical synthesis literature database in the ChemPU. Science 2022; 377: 172-180. [Article] [Google Scholar]
  • Manzano JS, Hou W, Zalesskiy SS, et al. An autonomous portable platform for universal chemical synthesis. Nat Chem 2022; 14: 1311-1318. [Article] [Google Scholar]
  • Schneider G. Automating drug discovery. Nat Rev Drug Discov 2018; 17: 97-113. [Article] [Google Scholar]
  • Blair DJ, Chitti S, Trobe M, et al. Automated iterative Csp3-C bond formation. Nature 2022; 604: 92-97. [Article] [Google Scholar]
  • Dasgupta A, Stefkova K, Babaahmadi R, et al. Site-selective Csp3-Csp/Csp3-Csp2 cross-coupling reactions using frustrated lewis pairs. J Am Chem Soc 2021; 143: 4451-4464. [Article] [Google Scholar]
  • Angello NH, Rathore V, Beker W, et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science 2022; 378: 399-405. [Article] [Google Scholar]
  • Burger B, Maffettone PM, Gusev VV, et al. A mobile robotic chemist. Nature 2020; 583: 237-241. [Article] [Google Scholar]
  • Coley CW, Thomas Iii DA, Lummiss JAM, et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 2019; 365: eaax1566. [Article] [Google Scholar]
  • Xu H, Lin J, Liu Q, et al. High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques. Chem 2022; 8: 3202-3214. [Article] [Google Scholar]
  • Xu H, Zhang D, Mo F. High-throughput automated platform for thin layer chromatography analysis. STAR Protocols 2022; 3: 101893. [Article] [Google Scholar]
  • Vaucher AC, Zipoli F, Geluykens J, et al. Automated extraction of chemical synthesis actions from experimental procedures. Nat Commun 2020; 11: 3601. [Article] [Google Scholar]
  • Hammer AJS, Leonov AI, Bell NL, et al. Chemputation and the standardization of chemical informatics. ACS Au 2021; 1: 1572-1587. [Article] [Google Scholar]
  • Coley CW, Eyke NS, Jensen KF. Autonomous discovery in the chemical sciences Part I: Progress. Angew Chem Int Ed 2020; 59: 22858-22893. [Article] [Google Scholar]
  • Gao W, Raghavan P, Coley CW. Autonomous platforms for data-driven organic synthesis. Nat Commun 2022; 13: 1075. [Article] [Google Scholar]
  • Corey EJ, Wipke WT. Computer-assisted design of complex organic syntheses. Science 1969; 166: 178-192. [Article] [Google Scholar]
  • Krenn M, Pollice R, Guo SY, et al. On scientific understanding with artificial intelligence. Nat Rev Phys 2022; 4: 761-769. [Article] [Google Scholar]
  • Baylon JL, Cilfone NA, Gulcher JR, et al. Enhancing retrosynthetic reaction prediction with deep learning using multiscale reaction classification. J Chem Inf Model 2019; 59: 673-688. [Article] [Google Scholar]
  • Corey E, Cheng X. The Logic of Chemical Synthesis. Boston: Wiley, 1989 [Google Scholar]
  • Klucznik T, Mikulak-Klucznik B, McCormack MP, et al. Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem 2018; 4: 522-532. [Article] [Google Scholar]
  • Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018; 555: 604-610. [Article] [Google Scholar]
  • Schwaller P, Hoover B, Reymond JL, et al. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci Adv 2021; 7: eabe4166. [Article] [Google Scholar]
  • Fletcher TL, Davie SJ, Popelier PLA. Prediction of intramolecular polarization of aromatic amino acids using kriging machine learning. J Chem Theor Comput 2014; 10: 3708-3719. [Article] [Google Scholar]
  • Hansen K, Biegler F, Ramakrishnan R, et al. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J Phys Chem Lett 2015; 6: 2326-2331. [Article] [Google Scholar]
  • Xu H, Lin J, Zhang D, et al. Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network. Nat Commun 2023; 14: 3095. [Article] [Google Scholar]
  • Milo A, Neel AJ, Toste FD, et al. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis. Science 2015; 347: 737-743. [Article] [Google Scholar]
  • Zhao S, Gensch T, Murray B, et al. Enantiodivergent Pd-catalyzed C-C bond formation enabled through ligand parameterization. Science 2018; 362: 670-674. [Article] [Google Scholar]
  • Reid JP, Sigman MS. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature 2019; 571: 343-348. [Article] [Google Scholar]
  • Newman-Stonebraker SH, Smith SR, Borowski JE, et al. Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis. Science 2021; 374: 301-308. [Article] [Google Scholar]
  • Zahrt AF, Henle JJ, Rose BT, et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 2019; 363: eaau5631. [Article] [Google Scholar]
  • Ruan Y, Lin S, Mo Y. AROPS: A framework of automated reaction optimization with parallelized scheduling. J Chem Inf Model 2023; 63: 770-781. [Article] [Google Scholar]
  • Xu Y, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation 2021; 2: 100179. [Article] [Google Scholar]
  • Taylor CJ, Pomberger A, Felton KC, et al. A brief introduction to chemical reaction optimization. Chem Rev 2023; 123: 3089-3126. [Article] [Google Scholar]
  • Adamo A, Beingessner RL, Behnam M, et al. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science 2016; 352: 61-67. [Article] [Google Scholar]
  • Qiu J, Xie J, Su S, et al. Selective functionalization of hindered meta-C-H bond of o-alkylaryl ketones promoted by automation and deep learning. Chem 2022; 8: 3275-3287. [Article] [Google Scholar]
  • Yu Z, Kong Y, Li B, et al. HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation. Chem Commun 2023; 59: 2935. [Article] [Google Scholar]
  • Yang Q, Li Y, Yang J, et al. Holistic prediction of the pKa in diverse solvents based on a machine-learning approach. Angew Chem Int Ed 2020; 59: 19282-19291. [Article] [Google Scholar]
  • Li L, Mayer P, Stephenson DS, et al. An overlooked pathway in 1,3-dipolar cycloadditions of diazoalkanes with enamines. Angew Chem Int Ed 2022; 61: e202117047. [Article] [Google Scholar]
  • Liu Y, Yang Q, Cheng J, et al. Prediction of nucleophilicity and electrophilicity based on a machine-learning approach. ChemPhysChem 2023; 24: e202300162. [Article] [Google Scholar]
  • Wagen CC, McMinn SE, Kwan EE, et al. Screening for generality in asymmetric catalysis. Nature 2022; 610: 680-686. [Article] [Google Scholar]
  • Kim H, Gerosa G, Aronow J, et al. A multi-substrate screening approach for the identification of a broadly applicable Diels-Alder catalyst. Nat Commun 2019; 10: 770. [Article] [Google Scholar]
  • Rosales AR, Wahlers J, Limé E, et al. Rapid virtual screening of enantioselective catalysts using CatVS. Nat Catal 2019; 2: 41-45. [Article] [Google Scholar]
  • Shcherbakova EG, James TD, Anzenbacher Jr. P. High-throughput assay for determining enantiomeric excess of chiral diols, amino alcohols, and amines and for direct asymmetric reaction screening. Nat Protoc 2020; 15: 2203-2229. [Article] [Google Scholar]
  • Xu LC, Frey J, Hou X, et al. Enantioselectivity prediction of pallada-electrocatalysed C-H activation using transition state knowledge in machine learning. Nat Synth 2023; 2: 321-330. [Article] [Google Scholar]
  • Pereyaslavets L, Kamath G, Butin O, et al. Accurate determination of solvation free energies of neutral organic compounds from first principles. Nat Commun 2022; 13: 414. [Article] [Google Scholar]
  • Manzhos S, Carrington Jr T. Neural network potential energy surfaces for small molecules and reactions. Chem Rev 2021; 121: 10187-10217. [Article] [Google Scholar]
  • Mills AW, Goings JJ, Beck D, et al. Exploring potential energy surfaces using reinforcement machine learning. J Chem Inf Model 2022; 62: 3169-3179. [Article] [Google Scholar]
  • Zhang J, Zhang H, Qin Z, et al. Quasiclassical trajectory simulation as a protocol to build locally accurate machine learning potentials. J Chem Inf Model 2023; 63: 1133-1142. [Article] [Google Scholar]
  • Karniadakis GE, Kevrekidis IG, Lu L, et al. Physics-informed machine learning. Nat Rev Phys 2022; 3: 422-440. [Article] [Google Scholar]
  • Chen Y, Zhang D. Integration of knowledge and data in machine learning. ArXiv: https://arxiv.org/abs/2202.10337 [Google Scholar]
  • Sun M, Zhao S, Gilvary C, et al. Graph convolutional networks for computational drug development and discovery. Brief BioInf 2020; 21: 919-935. [Article] [Google Scholar]
  • Zhang S, Tong H, Xu J, et al. Graph convolutional networks: A comprehensive review. Comput Soc Netw 2019; 6: 11. [Article] [Google Scholar]
  • Li SW, Xu LC, Zhang C, et al. Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge. Nat Commun 2023; 14: 3569. [Article] [Google Scholar]
  • Zhang S, Xu L, Li S, et al. Bridging chemical knowledge and machine learning for performance prediction of organic synthesis. Chem Eur J 2023; 29: e202202834. [Article] [Google Scholar]
  • Nowotka MM, Gaulton A, Mendez D, et al. Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert Opin Drug Dis 2017; 12: 757-767. [Google Scholar]
  • Guo J, Ranković B, Schwaller P. Bayesian optimization for chemical reactions. Chimia 2023; 77: 31-38. [Article] [Google Scholar]
  • Borgeaud S, Mensch A, Hoffmann J, et al. Improving language models by retrieving from trillions of tokens. In: Proceedings of the 39th International Conference on Machine Learning, PMLR. Baltimore, 2022. 2206-2240. [Google Scholar]
  • White AD. The future of chemistry is language. Nat Rev Chem 2023; 7: 457-458. [Article] [Google Scholar]
  • Li L, Fan Y, Tse M, et al. A review of applications in federated learning. Comput Ind Eng 2020; 149: 106854. [Article] [Google Scholar]
  • Zhu W, Luo J, White AD. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 2022; 3: 100521. [Article] [Google Scholar]
  • Biyani SA, Moriuchi YW, Thompson DH. Advancement in organic synthesis through high throughput experimentation. Chem Methods 2021; 1: 323-339. [Article] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.