Another noteworthy approach for the optimization of both continuous and categorical variables for a Suzuki-Mayura coupling reaction was reported by Christensen et al. using in-house developed BO algorithms called Phoenics and Gryffin (Table ). The Gryffin algorithm uses Bayesian Neural Networks to construct the surrogate model, circumventing the limitations of GPR to fit categorical variables. The authors selected a total of four continuous reaction variables and the selection of a catalyst ligand as the unique categorical variable for the optimization. The algorithm targeted to find optimal reaction variables for four objectives including the maximization of the targeted stereoisomers (E-39, Table ) and minimization of undesirable ones (Z-39, Table ), catalyst loading, and reagent equivalents. Twelve ligands were initially selected based on domain expert knowledge and after 120 trials the best-performing conditions were similar to those previously reported in the literature. To further improve the performance of the reaction, the authors used DFT simulations to compute the chemical properties of 365 commercially available phosphine ligands, and by using k-means clustering they grouped the ligands in 24 distinct regions. Through the strategic selection of a representative ligand from each distinct region, the researchers identified a novel set of ligands, differing from conventional recommendations based on domain expertise. These ligands were discovered through ML clustering techniques, sampling distinct groups of molecules. Following optimization of reaction conditions integrating these 23 new ligands, the authors observed enhanced performance, surpassing that of previously reported ligands (Figure ). This study showcased how data science, ML algorithms and chemical reaction optimization can be used to optimize and discover reaction conditions that would have been missed by sampling of ligands using human intuition. Another great example of a combination of ML/AI chemoinformatic tools and reaction optimization was reported by Nambiar et al. , where a computer-aided synthesis planning (CASP) tool was used to find a 3-step reaction pathway for the synthesis of the API Sonidegib . After the generation of multiple reaction pathways by the CASP tool, the author manually selected a highly ranked route based on synthetic feasibility. This three-step reaction comprised a SNAr , hydrogenation reduction of a nitro group and an amide coupling as presented (Table , entry 10 and 11). Using an automated flow reactor, the researchers attempted to perform the optimization of the fully telescoped reaction. However, the optimization campaign had to be restructured into two independent optimizations due to the subproducts of the SNAr reaction poisoned the Pd catalyst used in the hydrogenation reaction. Thus, the MOBO of the SNAr reaction was performed to maximize yield of 55, productivity and minimize cost of reagents per mole of product by optimizing 4 continuous and a categorical variable. The second optimization campaign was performed on the telescoped reaction that included the hydrogenation step and the amide coupling. In this case the objectives for the optimization were maximizing yield and productivity by optimizing two categorical and three continuous variables. Dragonfly, an open-sourced Bayesain optimization package was used to optimize both categorical and continuous reaction variables. An increase in yield and productivity was observed as the optimization progressed. The authors found that the selection of F as a leaving group led to the highest yield (98.3%) and productivity (5.97 g/h) for the synthesis of 58. However, if Cl was selected as the leaving group only a marginal reduction in yield and productivity was observed (93.8%, 5.70 g/h) but a 33% reduction in the cost. In the second reaction, both high yields and productivity were achieved concurrently. Because these objectives were positively correlated, no trade-offs were observed in the optimization suggestions.