摘要：SummaryThe application of machine learning toward DNA encoded library (DEL) technology is lacking despite obvious synergy between these two advancing technologies. Herein, a machine learning algorithm has been developed that predicts the conversion rate for the DNA-compatible reaction of a building block with a model DNA-conjugate. We exemplify the value of this technique with a challenging reaction, the Pictet-Spengler, where acidic conditions are normally required to achieve the desired cyclization between tryptophan and aldehydes to provide tryptolines. This is the first demonstration of using a machine learning algorithm to cull potential building blocks prior to their purchase and testing for DNA-encoded library synthesis. Importantly, this allows for a challenging reaction, with an otherwise very low building block pass rate in the test reaction, to still be used in DEL synthesis. Furthermore, because our protocol is solution phase it is directly applicable to standard plate-based DEL synthesis.Graphical AbstractDisplay OmittedHighlights•A mild solution-phase, plate applicable DNA-compatible Pictet-Spengler (PS) reaction•An efficient strategy for DNA-encoded diversified tryptoline libraries synthesis•A machine learning algorithm of building blocks filtering for DEL synthesis•An elegant application of machine learning for DNA-encoded library technologyOrganic Chemistry; Organic Reaction; Artificial Intelligence
