Identifying Rxn conditions for Atazanavir
The Challenge:
The Suzuki coupling is a powerful reaction used to form carbon-carbon bonds. However, its efficiency is highly dependent on a number of variables, including the choice of catalyst, ligand, solvent, base, temperature, and reaction time. Optimizing these variables through traditional experimental methods is a tedious and lengthy process.
Our Solution: Predictive Optimization
Using our predictive platform, we analyzed a large dataset of Suzuki coupling reactions to identify the key parameters that influence yield.
- Initial Analysis: The platform first generated a series of predictive charts to identify the best options for each variable.
- For the catalyst, C1 showed the highest yield, reaching 77.7%.
- For the solvent, with S2 being the highest at 77.7%.
- For the ligand, L6 emerged as the best performer with a yield of 77.7%.
- For the base, B4 gave the best result at 77.7%.
- The optimal temperature was predicted to be Tem 4 (77.7% yield).
- The optimal time was T5 (77.7% yield).
- Yield Optimization: Based on these initial findings, the platform then generated a table of predicted yield results for various combinations of the top-performing conditions. The results showed that by using the optimal combination of reagents and conditions, the predicted yield could be significantly increased. The initial reaction provided a predicted yield of 77.31%. By optimizing the conditions, the platform was able to predict a new yield of 90.15%.
Outcome:
This case study demonstrates the effectiveness of our data-driven approach in rapidly identifying optimal reaction conditions. By predicting the highest-yielding combination of catalyst, ligand, solvent, base, temperature, and time, we were able to increase the yield of the Suzuki coupling reaction from an initial value to over 90%. This rapid, predictive optimization process saves significant time and resources compared to traditional methods, enabling a faster and more efficient path to drug manufacturing.