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Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS

Authors :
Huaming Sheng
Shishi Lin
Tim Cernak
Christopher J. Welch
Kerstin Zawatzky
William D. Blincoe
Ian W. Davies
Zhengwei Peng
Ron Ferguson
Robert P. Sheridan
Sergei Dikler
Daniel A. DiRocco
Spencer D. Dreher
Heather Wang
Donald V. Conway
Source :
Science. 361
Publication Year :
2018
Publisher :
American Association for the Advancement of Science (AAAS), 2018.

Abstract

INTRODUCTION The invention of new chemical reactions provides new bond construction strategies for improved access to diverse regions of structural space. However, a pervasive, long-standing bias toward reporting successful results means that the shortcomings of even mature reaction methods remain poorly defined, making practical syntheses of structurally diverse targets far from certain. Distinct tools and experimental approaches are required to expose and record the problematic structural elements that limit different synthetic methods. The experimental space required to systematically survey reaction failure is vast, and existing ultrahigh-throughput (uHT) reaction screening approaches are inadequate for exploring the diversity of conditions pertaining in modern synthetic methods. Additionally, analytical approaches must continuously improve to meet the throughput demands of this expansive reaction screening. RATIONALE We report a nanomole-scale screening protocol that can be used to execute heterogeneous reactions with heating and agitation, use of volatile solvents, and capacity for photoredox chemistry. These advances in miniaturized chemistry screening were combined with the use of matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS), enabling analysis of 1536 reactions in ~10 min. Together, these advances create a platform that can enable systematic reaction evaluation and data capture to survey the dark space of chemical reactions. RESULTS Using the Buchwald-Hartwig C–N coupling reaction to exemplify this process, an uHT Glorius fragment additive poisons diagnostic approach was first applied to demonstrate that MALDI-MS could provide adequate data quality to monitor the formation of a single product under a wide variety of different synthetic conditions. Four catalytic methods—Ir/Ni and Ru/Ni dual-metal photoredox catalysis, as well as heterogeneous and high-temperature Cu and Pd catalysis—with extended nanomole chemistry requirements were evaluated for the synthesis of a single product in the presence of 383 structurally diverse simple and complex potential poisons. Using a normalizing internal standard that was closely related to the product and optimized operating parameters, MALDI-MS provided good correlation with existing ultra performance liquid chromatography (UPLC)–MS approaches (coefficient of determination R2 up to 0.85), allowing correct binning of “hits” and “misses” (defined as >50% product signal knockdown) up to 95% of the time. Next, the more challenging goal of exploring diverse whole-molecule C–N couplings was explored. In this case, it was not practical to have either product standards or closely related internal standards to enable analytical quantitation. A “simplest-partner test” was employed, in which 192 aryl bromides and 192 secondary amines were each coupled with a MS-active “simplest partner,” guaranteeing a somewhat normalized MS response for all products. The formation of 384 different products using the four aforementioned synthetic methods was monitored by MALDI-MS, with pass-fail binning of results correlating well with UPLC-MS in the identification of common structural elements (such as functional group counts, H-bond donors and acceptors, and polar surface area) that lead to reaction failure. CONCLUSION In the near future, each problematic structural element that is identified through systematic dark-space exploration can be promoted for in-depth examination to precisely define the specific parameters that determine reaction outcome at the atomic and quantum molecular level. Predictive machine learning models will use this focused data to enable synthetic practitioners to select the most appropriate reactions for use in a particular synthetic setting. In addition, functionality that persistently fails across synthetic methods can sharply define important challenges for the invention of improved chemical reactions.

Details

ISSN :
10959203 and 00368075
Volume :
361
Database :
OpenAIRE
Journal :
Science
Accession number :
edsair.doi.dedup.....c61431e9cc955e0f2d7412a6d8da3428