Authors: Neha Rani | R. Krishna Kumar | Shivnath Mazumder
Source: The Journal of Physical Chemistry A (article, 2024-11-21)
Field: Chemistry — Organic Chemistry

AI-Driven Discovery of Asymmetric Pauson–Khand Reactions: A New Toolbox in a Synthetic Chemist’s Treasure Enantioselective catalytic reactions have a significant impact on chemical synthesis, and they are important components in an experimental chemist's toolbox. However, development of asymmetric catalysts often relies on the chemical intuition and experience of a synthetic chemist, making the process both time-consuming and resource-intensive. The machine-learning-assisted reaction discovery can serve as a very efficient platform for obtaining high-performing catalysts in a time-economical manner without extensive experimentation. Herein, we report a data-driven and machine learning method for reliably predicting enantiomeric excess (%ee) of 211 asymmetric Pauson-Khand reactions (PKR 1-PKR 211) between a variety of 45 unique 1,6-enyne substrates and 12 unique axially chiral biaryl ligands in the presence of different reaction conditions like varying CO gas pressure, temperature, and solvent polarity. Four different machine learning algorithms have been studied: extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LGBM), and neural network (NN). A fivefold cross validation method was applied to our k-means SMOTE-augmented data set to obtain the optimized hyperparameters for the training set, and subsequently, these parameters were used in the test data set. In the case of the out-of-box set, the XGBoost method is found to be superior among all four machine learning methods investigated. Our out-of-box samples contain a total of 12 unique asymmetric Pauson-Khand reactions (PKR 212-PKR 223) arising from three new 1,3-benzodioxole-based SEGPHOS catalysts, which were never included in the training set. The XGBoost algorithm shows an impressive root mean square error (RMSE) of 7.06 (±1.11) in predicting %ee. The XGBoost-predicted %ee values match reasonably well with the experimental results. The absolute difference between the experimental and XGBoost-calculated %ee values ranges from 0.9 to 7.6 for the majority of the out-of-box Pauson-Khand reactions. The reactions with fluoro-substituted-SEGPHOS ligand <b>L14</b> shows smaller deviations from the experimental %ee values compared to the reactions with <b>L13</b> and <b>L15</b> catalysts where the benzodioxole units do not have fluorine atoms. Finally, we have discovered a library of 3357 lead reactions with excellent %ee (≥99) by engaging the experimentally unknown combinations of the catalysts, substrates, and reaction conditions. The axially chiral biaryl catalysts and enyne substrates present in the library are synthetically accessible. The ligand space in the library is dominated by the presence of tol-BINAP and the DTBM-OMe-BIPHEP ligands. The substrate space is predominantly occupied by NTs-tethered, O-tethered, NBn-tethered, and C(CO<sub>2</sub>Me)<sub>2</sub>-tethered 1,6-enynes that have an H or methyl functional group present in the alkyne unit. Our newly discovered library assists a synthetic chemist to develop a highly enantioselective PKR by starting with <i>a priori</i> knowledge without extensive trial-and-error experimentation. Treasure ; Toolbox ; Chemist ; Computer science ; Chemistry ; Philosophy ; Programming language ; Organic chemistry Catalytic Alkyne Reactions ; Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis