Synthesis of challenging cyclic tetrapeptides using machine learning-assisted high-throughput continuous flow technology

Abstract

Cyclic tetrapeptides (CTPs), which possess unique structures and diverse biological activities, are significant compounds in pharmaceutical and therapeutic applications. However, the inherent ring strain in CTPs poses challenges in minimizing racemization and achieving high yields. The antiviral CTP cyclo-(Pro-Leu)₂ and the anticancer CTP cyclo-(Pro-Val)₂ were previously reported with yields of only 5% and 7%, respectively. The wide range of peptide cyclization conditions significantly influences the reaction outcomes, making comprehensive optimization a labor-intensive process. Herein, we integrated high-throughput continuous flow technology with machine learning to achieve rapid and comprehensive optimization for the synthesis of challenging CTPs, achieving a 5- to 7-fold increase in yields for both cyclo-(Pro-Val)₂ and cyclo-(Pro-Leu)₂ compared to those reported in the literature. Notably, with the aid of machine learning, which achieves a root mean square error of 3.6, the optimization workload can be reduced by up to 90%. These advancements may offer a solution for the rapid optimization and synthesis of valuable CTPs.