The Evolution of Data-Driven Modeling in Organic Chemistry

Overview

Journal ACS Cent Sci

Specialty Chemistry

Date 2021 Nov 3

PMID 34729406

Citations 37

Authors

Wendy L Williams

Lingyu Zeng

Tobias Gensch

Matthew S Sigman

Abigail G Doyle

Eric V Anslyn

Affiliations

Soon will be listed here.

Abstract

Organic chemistry is replete with complex relationships: for example, how a reactant's structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst's structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure-activity and structure-property relationships.

Citing Articles

Connecting the complexity of stereoselective synthesis to the evolution of predictive tools.

Li J, Reid J Chem Sci. 2025; 16(9):3832-3851.

PMID: 39911341 PMC: 11791519. DOI: 10.1039/d4sc07461k.

Cross-Coupling Reactions with Nickel, Visible Light, and -Butylamine as a Bifunctional Additive.

Duker J, Philipp M, Lentner T, Cadge J, Lavarda J, Gschwind R ACS Catal. 2025; 15(2):817-827.

PMID: 39839851 PMC: 11744660. DOI: 10.1021/acscatal.4c07185.

Applying statistical modeling strategies to sparse datasets in synthetic chemistry.

Haas B, Kalyani D, Sigman M Sci Adv. 2025; 11(1):eadt3013.

PMID: 39742471 PMC: 11691635. DOI: 10.1126/sciadv.adt3013.

Compartmentalizing Donor-Acceptor Stenhouse Adducts for Structure-Property Relationship Analysis.

Reyes C, Karr A, Ramsperger C, K A, Lee H, Picazo E J Am Chem Soc. 2024; 147(1):10-26.

PMID: 39729546 PMC: 11726581. DOI: 10.1021/jacs.4c14198.

Data-Driven Discovery of a New Fluorescent BASHY Dye for Bioimaging.

Ravasco J, Felicidade J, Pinto M, Santos F, Campos-Gonzalez R, Arteaga J JACS Au. 2024; 4(11):4212-4222.

PMID: 39610736 PMC: 11600176. DOI: 10.1021/jacsau.4c00473.

References

Cai Y, Liu X, Xu X, Chou K . Support Vector Machines for predicting HIV protease cleavage sites in protein. J Comput Chem. 2002; 23(2):267-74. DOI: 10.1002/jcc.10017. View

Coley C, Jin W, Rogers L, Jamison T, Jaakkola T, Green W . A graph-convolutional neural network model for the prediction of chemical reactivity. Chem Sci. 2019; 10(2):370-377. PMC: 6335848. DOI: 10.1039/c8sc04228d. View

Holmes E, Antti H . Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst. 2003; 127(12):1549-57. DOI: 10.1039/b208254n. View

Vanhaelen Q, Lin Y, Zhavoronkov A . The Advent of Generative Chemistry. ACS Med Chem Lett. 2020; 11(8):1496-1505. PMC: 7429972. DOI: 10.1021/acsmedchemlett.0c00088. View

Mater A, Coote M . Deep Learning in Chemistry. J Chem Inf Model. 2019; 59(6):2545-2559. DOI: 10.1021/acs.jcim.9b00266. View

Durand D, Fey N . Computational Ligand Descriptors for Catalyst Design. Chem Rev. 2019; 119(11):6561-6594. DOI: 10.1021/acs.chemrev.8b00588. View

Schwaller P, Laino T, Gaudin T, Bolgar P, Hunter C, Bekas C . Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction. ACS Cent Sci. 2019; 5(9):1572-1583. PMC: 6764164. DOI: 10.1021/acscentsci.9b00576. View

Coley C, Eyke N, Jensen K . Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl. 2019; 59(51):22858-22893. DOI: 10.1002/anie.201909987. View

Ahneman D, Estrada J, Lin S, Dreher S, Doyle A . Predicting reaction performance in C-N cross-coupling using machine learning. Science. 2018; 360(6385):186-190. DOI: 10.1126/science.aar5169. View

10.

Biancolillo A, Marini F . Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis. Front Chem. 2018; 6:576. PMC: 6258797. DOI: 10.3389/fchem.2018.00576. View

11.

Schwaller P, Hoover B, Reymond J, Strobelt H, Laino T . Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci Adv. 2021; 7(15). PMC: 8026122. DOI: 10.1126/sciadv.abe4166. View

12.

Mitchell J . Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci. 2014; 4(5):468-481. PMC: 4180928. DOI: 10.1002/wcms.1183. View

13.

Granda J, Donina L, Dragone V, Long D, Cronin L . Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018; 559(7714):377-381. PMC: 6223543. DOI: 10.1038/s41586-018-0307-8. View

14.

Svetnik V, Liaw A, Tong C, Culberson J, Sheridan R, Feuston B . Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003; 43(6):1947-58. DOI: 10.1021/ci034160g. View

15.

Carpenter K, Cohen D, Jarrell J, Huang X . Deep learning and virtual drug screening. Future Med Chem. 2018; 10(21):2557-2567. PMC: 6563286. DOI: 10.4155/fmc-2018-0314. View

16.

Chen H, Bakshi B, Goel P . Toward Bayesian chemometrics--a tutorial on some recent advances. Anal Chim Acta. 2007; 602(1):1-16. DOI: 10.1016/j.aca.2007.08.044. View

17.

Zhao S, Gensch T, Murray B, Niemeyer Z, Sigman M, Biscoe M . Enantiodivergent Pd-catalyzed C-C bond formation enabled through ligand parameterization. Science. 2018; 362(6415):670-674. PMC: 6613548. DOI: 10.1126/science.aat2299. View

18.

Brereton R, Lloyd G . Support vector machines for classification and regression. Analyst. 2010; 135(2):230-67. DOI: 10.1039/b918972f. View

19.

Goh G, Hodas N, Vishnu A . Deep learning for computational chemistry. J Comput Chem. 2017; 38(16):1291-1307. DOI: 10.1002/jcc.24764. View

20.

Meyer B, Sawatlon B, Heinen S, Anatole von Lilienfeld O, Corminboeuf C . Machine learning meets volcano plots: computational discovery of cross-coupling catalysts. Chem Sci. 2018; 9(35):7069-7077. PMC: 6137445. DOI: 10.1039/c8sc01949e. View