Merging Directed C−H Activations with High-Throughput Experimentation: Development of Predictable Iridium-Catalyzed C−H Aminations Applicable to Late-Stage Functionalization

Herein, we report an iridium-catalyzed directed C−H amination methodology developed using a high-throughput experimentation (HTE)-based strategy, applicable for the needs of automated modern drug discovery. The informer library approach for investigating accessible directing group chemical space for the reaction, in combination with functional group tolerance screening and substrate scope investigations, allowed for the generation of an empirical predictive model to guide future users. Applicability to late-stage functionalization of complex drugs and natural products, in combination with multiple deprotection protocols leading to the desirable aniline matched pairs, serve to demonstrate the utility of the method for drug discovery. Finally reaction miniaturization to a nano molar range highlights the opportunities for more sustainable screening with decreased material consumption.

An Optical Probe for Real-Time Monitoring of Self-Replicator Emergence and Distinguishing between Replicators

Self-replicating systems play an important role in research on the synthesis and origin of life. Monitoring of these systems has mostly relied on techniques such as NMR or chromatography, which are limited in throughput and demanding when monitoring replication in real time. To circumvent these problems, we now developed a pattern-generating fluorescent molecular probe (an ID-probe) capable of discriminating replicators of different chemical composition and monitoring the process of replicator formation in real time, giving distinct signatures for starting materials, intermediates and final products. Optical monitoring of replicators dramatically reduces the analysis time and sample quantities compared to most currently used methods and opens the door for future high-throughput experimentation in protocell environments.

Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing

The advances of high-throughput experimentation technology and chemometrics have revolutionized the pace of scientific progress and enabled previously inconceivable discoveries, in particular when used in tandem. Here we show that the combination of chirality sensing based on small-molecule optical probes that bind to amines and amino alcohols via dynamic covalent or click chemistries and powerful chemometric tools that achieve orthogonal data fusion and spectral deconvolution yields a streamlined multi-modal sensing protocol that allows analysis of the absolute configuration, enantiomeric composition and concentration of structurally analogous—and therefore particularly challenging—chiral target compounds without laborious and time-consuming physical separation. The practicality, high accuracy, and speed of this approach are demonstrated with complicated quaternary and octonary mixtures of varying chemical and chiral compositions. The advantages over chiral chromatography and other classical methods include operational simplicity, increased speed, reduced waste production, low cost, and compatibility with multiwell plate technology if high-throughput analysis of hundreds of samples is desired.

On the Use of Real-World Datasets for Reaction Yield Prediction

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as good or better than the best previous models on two HTE datasets for the Suzuki and Buchwald-Hartwig reactions. However, training of the AGNN on the ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.