Machine Learning-Based Yield Prediction for First-Row Transition Metal Catalyzed Cross-Coupling Reactions

Abstract

Abstract The advent of first-row transition metal-catalyzed cross-coupling reactions has marked a significant milestone in the field of organic chemistry, primarily due to their pivotal role in facilitating the construction of carbon-carbon and carbon-heteroatom bonds. Traditionally, the determination of reaction yields has relied on experimental methods, but in recent times, the integration of efficient machine learning techniques has revolutionized this process. Developing a highly accurate predictive model for reaction yields applicable to diverse categories of cross-coupling reactions, however, remains a formidable challenge. In our study, we curated an extendable dataset encompassing a wide range of yields of cross-coupling reactions catalyzed by first-row transition metals through rigorous literature mining efforts. Using this dataset, we have developed an automated and open-access reaction model, employing both regression and classification methodologies. Our ML model could be used even by non-expert users, who can solely input the reaction components as datasets to predict the yields. We have achieved a correlation of 0.46 using the Random Forest regression approach and an accuracy of 0.54 using the K-Nearest Neighbours (KNN) classification which employs hyperparameter tuning. Considering the vast chemical space of our small dataset encompassing various transition metals catalysts and different categories of reactions, the above results are commendable. By releasing an open-access dataset comprising cross-coupling reactions catalyzed by 3d-transition metal, our study is anticipated to make a substantial contribution to the progression of predictive modeling for sustainable transition metal catalysis, thereby shaping the future landscape of synthetic chemistry.