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Solution Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering

98 Pages Posted: 6 Feb 2020 Sneak Peek Status: Review Complete

See all articles by Ke Li

Ke Li

WuXi AppTec - DNA Encoded Library Platform

Xiaohong Liu

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

Sixiu Liu

Chinese Academy of Sciences (CAS) - Shanghai Institute of Materia Medica

Yulong An

WuXi AppTec - DNA Encoded Library Platform

Yanfang Shen

WuXi AppTec - DNA Encoded Library Platform

Qingxia Sun

WuXi AppTec - DNA Encoded Library Platform

Xiaodong Shi

Peking University, Capital Institute of Pediatrics Children's Hospital, Department of Hematology and Oncology

Wenji Su

WuXi AppTec - DNA Encoded Library Platform

Weiren Cui

WuXi AppTec - DNA Encoded Library Platform

Zhiqiang Duan

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

Letian Kuai

WuXi AppTec - DNA Encoded Library Platform

Hongfang Yang

WuXi AppTec - DNA Encoded Library Platform

Alexander L. Satz

WuXi AppTec - DNA Encoded Library Platform

Kaixian Chen

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

Hualiang Jiang

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

Mingyue Zheng

Chinese Academy of Sciences (CAS) - Shanghai Institute of Materia Medica

Xuanjia Peng

WuXi AppTec - DNA Encoded Library Platform

Xiaojie Lu

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

More...

Abstract

The application of machine learning towards 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. To avoid damaging the DNA our reaction conditions must be exceptionally mild, and therefore most building blocks fail to provide acceptable yields of desired product (<20% pass rate) in a test reaction employing our optimized protocol. In contrast, building blocks selected by our trained machine learning algorithm have a >78% pass rate. 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, we discuss herein the rational design of DNA conjugated tryptophan substrates for our Pictet-Spengler reaction, and optimization of the reaction protocols. Lastly, because our protocol is solution-phase it is directly applicable to standard plate-based DEL synthesis.

Keywords: DNA Encoded Library, Machine Learning, DNA Compatible Reaction, Pictet-Spengler Reaction

Suggested Citation

Li, Ke and Liu, Xiaohong and Liu, Sixiu and An, Yulong and Shen, Yanfang and Sun, Qingxia and Shi, Xiaodong and Su, Wenji and Cui, Weiren and Duan, Zhiqiang and Kuai, Letian and Yang, Hongfang and Satz, Alexander L. and Chen, Kaixian and Jiang, Hualiang and Zheng, Mingyue and Peng, Xuanjia and Lu, Xiaojie, Solution Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering. Available at SSRN: https://ssrn.com/abstract=3529715 or http://dx.doi.org/10.2139/ssrn.3529715
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Ke Li

WuXi AppTec - DNA Encoded Library Platform

China

Xiaohong Liu

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research

52 Sanlihe Rd.
Datun Road, Anwai
Beijing, Xicheng District 100864
China

Sixiu Liu

Chinese Academy of Sciences (CAS) - Shanghai Institute of Materia Medica

200031
China

Yulong An

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Yanfang Shen

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Qingxia Sun

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Xiaodong Shi

Peking University, Capital Institute of Pediatrics Children's Hospital, Department of Hematology and Oncology ( email )

Beijing
China

Wenji Su

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Weiren Cui

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Zhiqiang Duan

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research ( email )

52 Sanlihe Rd.
Datun Road, Anwai
Beijing, Xicheng District 100864
China

Letian Kuai

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Hongfang Yang

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Alexander L. Satz

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Kaixian Chen

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research ( email )

52 Sanlihe Rd.
Datun Road, Anwai
Beijing, Xicheng District 100864
China

Hualiang Jiang

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research ( email )

52 Sanlihe Rd.
Datun Road, Anwai
Beijing, Xicheng District 100864
China

Mingyue Zheng

Chinese Academy of Sciences (CAS) - Shanghai Institute of Materia Medica

200031
China

Xuanjia Peng

WuXi AppTec - DNA Encoded Library Platform ( email )

China

Xiaojie Lu (Contact Author)

Chinese Academy of Sciences (CAS) - State Key Laboratory of Drug Research ( email )

52 Sanlihe Rd.
Datun Road, Anwai
Beijing, Xicheng District 100864
China

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