The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions

Ring strain energy (RSE) is crucial for understanding molecular reactivity, with broad implications in polymerization, click chemistry, drug discovery and beyond. However, quantitatively determining RSE through experiments or quantum mechanics (QM) is resource-intensive, limiting its application on a large scale. We present a machine learning (ML)-based workflow that enables the reliable and efficient prediction of RSE, entirely bypassing traditional QM calculations. Our workflow employs AIMNet2 machine learning interatomic potentials and Auto3D for the identification of low-energy conformers and RSE computation. Remarkably, it achieves an R 2 of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol when benchmarked against the ωB97M-D4/Def2-TZVPP method, while running orders of magnitude faster than DFT calculations. To demonstrate the utility of our workflow, we successfully differentiated reactive from nonreactive molecules in copper-free click chemistry, [3 + 2] cycloaddition reaction and ring-opening metathesis polymerization, underscoring its transferability to diverse molecular systems. Additionally, we compiled the RSE Atlas, a computational database encompassing 16,905 single-ring molecules, offering a valuable resource for investigating factors influencing RSE. Our approach transforms RSE into a readily computable property, facilitating its integration into reaction designs.

We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

AbstractReactions of 2‐nitro‐, 4‐nitro‐ and 2,4‐dinitrophenylglycidyl ethers with bicyclo[2.2.1]hept‐5‐ene‐endo‐2‐ylmethylamine in isopropanol have been studied. The mixtures of products were chromatographed on silica gel and eluted with ether or ether/2‐propanol (1:1), the structures of individual products have been confirmed by IR spectra, NMR 1H, 13C spectra, using experiments that involve homonuclear and heteronuclear scalar coupling interactions (COSY, TOCSY, HMQC, HMBC), and mass spectrometry. Amino alcohols as the major products of regioselective aminolysis of epoxides (according to the Krasusky rule) have been obtained. The minor products were the compounds with two hydroxyalkyl fragments at the nitrogen atom. In case of dinitrophenylglycidyl ether, it was the minor product of aryl nucleophilic substitution (SNAr). The abnormal course of aminolysis has been confirmed by the results of quantum‐chemical calculations of activation barries and Free Gibbs energies of the competitive reactions of epoxides (at the B3LYP/6‐311 + G(d,p) level of theory). Copyright © 2010 John Wiley & Sons, Ltd.

A novel series of cis-2-azetidinones 2(a-c ) was carried out by the cyclo addition reaction of imine 1(a-c ) and acyl chloride in dry dichloromethane at 0-5 oC using triphenylamine. The cyclo addition of the Schiff bases with chloroacetyl chloride resulted corresponding major product cis-2-azetidinone stereoisomers 2(a-c). The synthesized compounds were characterized by analytical and spectral (Infrared, 1H NMR, 13C NMR, and elemental analysis) data. Keywords: Benzothiazole, β-lactam, Schiff base, cis-2-azetidinone, Staudinger reaction Acknowledgements The authors would like to thank the Eskişehir Osmangazi University Scientific Research Projects Council for financial support (Project No: 2014/19A208). References • C. M. L. Delpiccolo, M. A. Fraga, E. G. Mata, J. Comb. Chem. 2003, 5, 208-210. DOI: 10.1021/cc020107d. • R. B. Pawar, V. V. Mulwad, Chem. Heterocycl. Compd. 2004, 40, 219-226. DOI: 10.1023/B:COHC.0000027896.38910.d1. • P. D. Mehta, N. P. S. Sengar, A. K. Pathak, Eur. J. Med. Chem. 2010, 45, 5541-5560. DOI: 10.1016/j.ejmech.2010.09.035. • G. S. Singh, B. J. Mmolotsi, Il Farmaco, 2005, 60, 727-730. DOI: 10.1016/j.farmac.2005.06.008. • C. D. Risi, G. P. Pollini, A. C. Veronese, V. Bertolasi, Tetrahedron Lett. 1999, 40, 6995-6998. DOI: 10.1016/S0040-4039(99)01421-5. • H. G. I. Georg: The Organic Chemistry of β-Lactams, Weinheim/VCH Publishers, New York, 1993, p. 295. DOI: 10.1002/ange.19941060738. • R. F. Abdulla, K. H. Fuhr, J. Med. Chem. 1975, 18, 625-627. DOI: 10.1021/jm00240a022. • W. Dürckheimer, J. Blumbach, R. Lattrell, K. H. Scheunemann, Angew. Chem. Int. Ed. Engl. 1985, 24, 180-202. DOI: 10.1002/anie.198501801. • P. D. Mehta, N. P. S. Sengar, A. K. Pathak, Eur. J. Med. Chem. 2010, 45, 5541-5560. DOI: 10.1016/j.ejmech.2010.09.035. • H. Staudinger, Justus Liebigs Ann. Chem. 1907, 356, 51-123. DOI: 10.1002/jlac.19073560106. • A. K. Bose, M. Jayaraman, A. Okawa, S. S. Bari, E. W. Robb, M. S. Manhas, Tetrahedron Lett. 1996, 37, 6989-6992. DOI: 10.1016/0040-4039(96)01571-7. • A. K. Bose, B. K. Banik, M. S. Manhas, Tetrahedron Lett. 1995, 36, 213-216. DOI: 10.1016/0040-4039(94)02225-Z. • A. Arrieta, B. Lecea, F. P. Cossio, J. Org. Chem. 1998, 63, 5869-5876. DOI: 10.1021/jo9804745. • P. Vicini, A. Geronikaki, M. Incerti, B. Busonera, G. Poni, C. A. Cabras, P. L. Colla, Bioorg. Med. Chem. 2003, 11, 4785-4789. DOI: 10.1016/S0968-0896(03)00493-0. • K. Mogilaiah, R. B. Rao, K. N. Reddy, Indian J. Chem. 1999, 38B, 818-822. • 16. I. Georg, V. T. G. I. Ravikumar, Ed. Verlag Chemie, 1993, 295- 368 New York, • L. Jiao, Y. Liang, J. Xu. J. Am. Chem. Soc. 2006, 128, 6060- 6069 • H. C. Sakarya, M. Yandımoğlu, Croat. Chem. Acta, 2018, 91, 533-541. DOI: 10.5562/cca3386. • D. A. Nelson, J. Org. Chem. 1972, 37, 1447-1449. DOI: 10.1021/jo00974a038. • K. D. Barrow, T. M. Spotswood, Tetrahedron Lett. 1965, 6, 3325-3335. DOI: 10.1016/S0040-4039(01)89203-0. • J. Decazes, J. L. Luche, H. B, Kagan, Tetrahedron Lett. 1970, 11, 3665-3668. DOI: 10.1016/S0040-4039(01)98556-9. • D. A. Nelson, Tetrahedron Lett. 1971, 12, 2543-2546. DOI: 10.1016/S0040-4039(01)96914-X.

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.