Development of Artificial Intelligence-Assisted Organic Synthesis Platforms for Predictive Reaction Design, Catalyst Discovery, and Sustainable Molecular Manufacturing

References

[1] Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018). Predicting reaction performance in C–N cross-coupling using machine learning. Science, 360(6385), 186–190. Crossref

[2] Aspuru-Guzik, A., Persson, K., & Tee, T. (2018). The rise of autonomous laboratories in chemical discovery. Nature, 557, 496–498.

[3] Atz, K., Mayr, A., & Schneider, G. (2021). Artificial intelligence in chemistry: Current trends and future perspectives. Angewandte Chemie International Edition, 60, 11670–11680.

[4] Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559, 547–555. Crossref

[5] Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019). Autonomous discovery in the chemical sciences part I: Progress. Angewandte Chemie International Edition, 58(20), 6459–6470.

[6] Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V., Hart, T., Fishman, J. S., Rogers, L., Gao, H., Hicklin, R. W., Plehiers, P. P., Byington, J., Piotti, J. S., Green, W. H., Hart, A. J., Jamison, T. F., & Jensen, K. F. (2019). A robotic platform for flow synthesis of organic compounds informed by AI planning. Science, 365(6453), eaax1566. Crossref

[7] Cramer, C. J. (2004). Essentials of Computational Chemistry: Theories and Models (2nd ed.). Wiley.

[8] Dai, H., Tian, Y., Dai, B., Skiena, S., & Song, L. (2019). Syntax-directed variational autoencoder for structured data. International Conference on Learning Representations (ICLR).

[9] Duan, C., & Li, Y. (2022). Machine learning approaches for catalyst discovery and reaction optimization. Chemical Society Reviews, 51, 7998–8023.

[10] Gao, H., Struble, T. J., Coley, C. W., Wang, Y., Green, W. H., & Jensen, K. F. (2018). Using machine learning to predict suitable conditions for organic reactions. ACS Central Science, 4(11), 1465–1476. Crossref

[11] Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268–276. Crossref

[12] Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019). Next-generation experimentation with self-driving laboratories. Trends in Chemistry, 1(3), 282–291. Crossref

[13] Huang, B., & von Lilienfeld, O. A. (2020). Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nature Chemistry, 12, 945–951. Crossref

[14] Jensen, K. F. (2017). Flow chemistry—Microreaction technology comes of age. AIChE Journal, 63(3), 858–869. Crossref

[15] Jorner, K., Tomberg, A., Bauer, C., Sköld, C., & Norrby, P. O. (2021). Organic chemistry as a design process: Machine learning, optimization, and automation. Chemical Science, 12, 11607–11618. Crossref

[16] Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016). Molecular graph convolutions: Moving beyond fingerprints. Journal of Computer-Aided Molecular Design, 30, 595–608. Crossref

[17] Kearnes, S. M., Maser, M. R., Wleklinski, M., Kast, A., Doyle, A. G., Dreher, S. D., Reibarkh, M., & Jensen, K. F. (2021). The future of machine learning in chemistry. Nature Reviews Chemistry, 5, 121–132.

[18] Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. International Conference on Learning Representations (ICLR).

[19] Liu, Y., Zhao, T., Ju, W., & Shi, S. (2019). Materials discovery and design using machine learning. Journal of Materiomics, 5(4), 510–522.

[20] Mulliken, R. S. (1955). Electronic population analysis on LCAO–MO molecular wave functions. Journal of Chemical Physics, 23, 1833–1840.

[21] Parr, R. G., & Yang, W. (1989). Density-Functional Theory of Atoms and Molecules. Oxford University Press.

[22] Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17, 97–113. Crossref

[23] Schneider, N., Stiefl, N., & Landrum, G. A. (2015). What’s what: The (nearly) definitive guide to reaction representation and reaction prediction. Journal of Chemical Information and Modeling, 55(5), 1004–1011. Crossref

[24] Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular Transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS Central Science, 5(9), 1572–1583. Crossref

[25] Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J. L. (2021). Prediction of chemical reaction yields using deep learning. Machine Learning: Science and Technology, 2, 015016. Crossref

[26] Segler, M. H. S., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555, 604–610. Crossref

[27] Szymanski, N. J., Rendy, B., Fei, Y., Kumar, S., He, T., Milsted, D., McDermott, M. J., Gallant, M., & Jain, A. (2023). An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature, 624, 86–91. Crossref

[28] Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C. J., Maruyama, B., & Aspuru-Guzik, A. (2018). Accelerating the discovery of materials for clean energy in the era of smart automation. Nature Reviews Materials, 3, 5–20. Crossref

[29] Venkatasubramanian, V. (2019). The promise of artificial intelligence in chemical engineering. AIChE Journal, 65(2), 466–478.

@article{1723040,
      author = {Dr. K. S. Lamani},
      title = {Development of Artificial Intelligence-Assisted Organic Synthesis Platforms for Predictive Reaction Design, Catalyst Discovery, and Sustainable Molecular Manufacturing},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {2711-2718},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723040.pdf},
      abstract = {The rapid advances in AI, ML, and computational chemistry provide the framework to transition organic synthesis from trial-and-error experimentation to the predictive, data-driven exploration of new molecules, as the synthesis of complex pharmaceutical, agrochemical, natural products, polymers, and organic functional materials involves meticulous control over reaction pathways, selectivity, catalytic efficiency, and experiment parameters. Traditionally, the sequential empirical optimization of conditions and reagents leads to significant waste, requiring many experiment days and the consumption of reagents, chemicals, energy, and the experimental scientist's time; fortunately, burgeoning reaction databases, electronic lab notes, cheminformatic software and computational tools allow researchers to mine this data for the creation of predictive models that support successful and efficient organic synthesis (Schneider et al., 2015; Coley et al., 2019). AI approaches that analyze the chemical structure and its impact on chemical outcomes to accurately predict potential product class, possible product structure, yield, selectivity, and reaction conditions represent this innovation; beyond that, ML (such as neural networks, graphs, etc.) and DL systems now help retrosynthetic planning in the selection of viable disconnections that are experimentally feasible. Further supplementing these developments is the area of catalyst discovery, where machine-learning combined with computation takes a look at catalyst structure; the exploration of binding energies, activation barriers, transition states and reaction mechanism between the catalyst and substrate has already proven capable of systematically sifting through catalyst possibilities faster than traditional experimental searching methods (Butler et al., 2018) through the combination of molecular descriptors, DFT and quantum chemistry.  Here the proposal of a holistic, close-loop framework wherein machine learning, deep learning, DFT and quantum chemistry are coupled with automated synthesizers and robotic experimenters is outlined as a new method for chemical discovery where prediction guides experiment, data improves models, and optimal conditions are measured in terms of speed, efficiency, selectiveness, cost, waste and safety; important considerations are the need for data, avoidance of bias, transparency of prediction and the verification of predictions. The integration of AI methods, with experimental chemistry remaining as the gold standard for verification, will therefore permit the accelerated synthesis and manufacture of the novel organic molecules we seek and it has great potential to both optimize conditions for efficient, sustainable organic synthesis and to contribute to greener manufacturing processes.},
      keywords = {Artificial intelligence, Machine learning, Organic synthesis, Reaction prediction, Computational chemistry},
      month = {May},
      doi = {https://doi.org/10.64388/IREV8I11-1723040}
  }