A map of the amine-carboxylic acid coupling system.

Chemical transformations determine the structure of a product, and therefore its properties, which in turn affect complex macroscopic functions such as the metabolic stability of pharmaceuticals or the volatility of perfumes. Therefore, reaction selection can influence the success or failure of a candidate molecule to meet a functional objective. The coupling of an amine with a carboxylic acid to form an amide bond is the most popular chemical reaction used for drug discovery1. However, there are many other ways to connect these two common functional groups together. Here we show computationally that amines and acids can couple via hundreds of hypothetical yet plausible transformations, and we demonstrate experimentally the application of a dozen such reactions. To investigate the contribution of chemical transformations to properties, we developed a string-based notation and used an enumerative combinatorics approach to produce a map of conceivable amine-acid coupling transformations, which can be charted using chemoinformatic techniques. We find that critical physicochemical parameters of the products, such as partition coefficient and polar surface area, vary considerably depending on the transformation chosen. Data mining the amine-acid coupling system produced here should enable reaction discovery, which we demonstrate by developing an esterification reaction found within the mapped space. Complex molecules with distinct property profiles can also be discovered within the amine-acid coupling system, as we show here via the late-stage diversification of drugs and natural products.

Impact of C-H Cross-Coupling Reactions in the One-Step Retrosynthesis of Drug Molecules.

Pharmaceutical synthesis requires a diversity of chemical reactions. The discovery of new reactions enable novel retrosynthetic disconnections, potentially expediting access to complex molecules. This Synopsis demonstrates the use of enumerative combinatorics to find impactful underdeveloped reactions for drug synthesis. By mapping pharmaceutical target molecules onto available building blocks using just one retrosynthetic disconnection even if the requisite reaction is not yet known, we highlight the importance of site-selective C-H cross-coupling methods. This cheminformatics-driven retrosynthetic analysis identifies novel reaction methods of value to the synthesis toolbox.

RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries.

SMARTS is a widely used language in cheminformatics for defining substructural queries for database lookups, reaction templates for chemical transformations, and other applications. As an extension to SMILES, many SMARTS patterns can represent the same query. Despite this, no canonicalization algorithm invariant of the line notation sequence or atomic numbering is publicly available. Here, we introduce RDCanon, an open-source Python package that can be used to standardize SMARTS queries. RDCanon is designed to ensure that the sequence of atomic queries remains consistent for all graphs representing the same substructure query and to ensure a canonical sequence of primitives within each individual atom query; furthermore, the algorithm can be applied to canonicalize the order of reactants, agents, and products and their atom map numbers in reaction SMARTS templates. As part of its canonicalization algorithm, RDCanon provides a mechanism in which the canonicalized SMARTS is optimized for speed against specific molecular databases. Several case studies are provided to showcase improved efficiency in substructure matching and retrosynthetic analysis.

Formal Cross-Coupling of Amines and Carboxylic Acids to Form sp3-sp2 Carbon-Carbon Bonds.

Amines and carboxylic acids are abundant synthetic building blocks that are classically united to form an amide bond. To access new pockets of chemical space, we are interested in the development of amine-acid coupling reactions that complement the amide coupling. In particular, the formation of carbon-carbon bonds by formal deamination and decarboxylation would be an impactful addition to the synthesis toolbox. Here, we report a formal cross-coupling of alkyl amines and aryl carboxylic acids to form C(sp3)-C(sp2) bonds following preactivation of the amine-acid building blocks as a pyridinium salt and N-acyl-glutarimide, respectively. Under nickel-catalyzed reductive cross-coupling conditions, a diversity of simple and complex substrates are united in good to excellent yield, and numerous pharmaceuticals are successfully diversified. High-throughput experimentation was leveraged in the development of the reaction and the discovery of performance-enhancing additives such as phthalimide, RuCl3, and GaCl3. Mechanistic investigations suggest phthalimide may play a role in stabilizing productive Ni complexes rather than being involved in oxidative addition of the N-acyl-imide and that RuCl3 supports the decarbonylation event, thereby improving reaction selectivity.

Ultrahigh-Throughput Experimentation for Information-Rich Chemical Synthesis.

The incorporation of data science is revolutionizing organic chemistry. It is becoming increasingly possible to predict reaction outcomes with accuracy, computationally plan new retrosynthetic routes to complex molecules, and design molecules with sophisticated functions. Critical to these developments has been statistical analysis of reaction data, for instance with machine learning, yet there is very little reaction data available upon which to build models. Reaction data can be mined from the literature, but experimental data tends to be reported in a text format that is difficult for computers to read. Compounding the issue, literature data are heavily biased toward "productive" reactions, and few "negative" reaction data points are reported even though they are critical for training of statistical models. High-throughput experimentation (HTE) has evolved over the past few decades as a tool for experimental reaction development. The beauty of HTE is that reactions are run in a systematic format, so data points are internally consistent, the reaction data are reported whether the desired product is observed or not, and automation may reduce the occurrence of false positive or negative data points. Additionally, experimental workflows for HTE lead to datasets with reaction metadata that are captured in a machine-readable format. We believe that HTE will play an increasingly important role in the data revolution of chemical synthesis. This Account details the miniaturization of synthetic chemistry culminating in ultrahigh-throughput experimentation (ultraHTE), wherein reactions are run in ∼1 µL droplets inside of 1536-well microtiter plates to minimize the use of starting materials while maximizing the output of experimental information. The performance of ultraHTE in 1536-well microtiter plates has led to an explosion of available reaction data, which have been used to identify specific substrate-catalyst pairs for maximal efficiency in novel cross-coupling reactions. The first iteration of ultraHTE focused on the use of dimethyl sulfoxide (DMSO) as a high-boiling solvent that is compatible with the plastics most commonly used in consumable well plates, which generated homogeneous reaction mixtures that are perfect for use with nanoliter-dosing liquid handling robotics. In this way, DMSO enabled diverse reagents to be arrayed in ∼1 µL droplets. Reactions were run at room temperature with no agitation and could be scaled up from the ∼0.05 mg reaction scale to the 1 g scale. Engineering enhancements enabled the use of ultraHTE with diverse and semivolatile solvents, photoredox catalysis, heating, and acoustic agitation. A main driver in the development of ultraHTE was the recognition of the opportunity for a direct merger between miniaturized reactions and biochemical assays. Indeed, a strategy was developed to feed ultraHTE reaction mixtures directly to a mass-spectrometry-based affinity selection bioassay. Thus, micrograms of starting materials could be used in the synthesis and direct biochemical testing of drug-like molecules. Reactions were performed at a reactant concentration of ∼0.1 M in an inert atmosphere, enabling even challenging transition-metal-catalyzed reactions to be used. Software to enable the workflow was developed. We recently initiated the mapping of reaction space, dreaming of a future where transformations, reaction conditions, structure, properties and function are studied in a systems chemistry approach.

HaplotagLR: An efficient and configurable utility for haplotagging long reads.

Understanding the functional effects of sequence variation is crucial in genomics. Individual human genomes contain millions of variants that contribute to phenotypic variability and disease risks at the population level. Because variants rarely act in isolation, we must consider potential interactions of neighboring variants to accurately predict functional effects. We can accomplish this using haplotagging, which matches sequencing reads to their parental haplotypes using alleles observed at known heterozygous variants. However, few published tools for haplotagging exist and these share several technical and usability-related shortcomings that limit applicability, in particular a lack of insight or control over error rates, and lack of key metrics on the underlying sources of haplotagging error. Here we present HaplotagLR: a user-friendly tool that haplotags long sequencing reads based on a multinomial model and existing phased variant lists. HaplotagLR is user-configurable and includes a basic error model to control the empirical FDR in its output. We show that HaplotagLR outperforms the leading haplotagging method in simulated datasets, especially at high levels of specificity, and displays 7% greater sensitivity in haplotagging real data. HaplotagLR advances both the immediate utility of haplotagging and paves the way for further improvements to this important method.

Exploring the combinatorial explosion of amine-acid reaction space via graph editing.

Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine-acid reaction space via systematic enumeration of reactions involving a simple amine-carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.

Rapid planning and analysis of high-throughput experiment arrays for reaction discovery.

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE in the chemical laboratory has evolved significantly in recent years, there remains a need for software solutions to navigate data-rich experiments. Here we have developed phactor™, a software that facilitates the performance and analysis of HTE in a chemical laboratory. phactor™ allows experimentalists to rapidly design arrays of chemical reactions or direct-to-biology experiments in 24, 96, 384, or 1,536 wellplates. Users can access online reagent data, such as a chemical inventory, to virtually populate wells with experiments and produce instructions to perform the reaction array manually, or with the assistance of a liquid handling robot. After completion of the reaction array, analytical results can be uploaded for facile evaluation, and to guide the next series of experiments. All chemical data, metadata, and results are stored in machine-readable formats that are readily translatable to various software. We also demonstrate the use of phactor™ in the discovery of several chemistries, including the identification of a low micromolar inhibitor of the SARS-CoV-2 main protease. Furthermore, phactor™ has been made available for free academic use in 24- and 96-well formats via an online interface.

LRphase: an efficient method for assigning haplotype identity to long reads.

Understanding the functional effects of sequence variation is among the primary goals of contemporary genomics. Individual human genomes contain millions of variants which are thought to contribute to phenotypic variability and differential disease risks at the population level. However, because variants rarely act in isolation, we cannot accurately predict functional effects without first considering the potential effects of other interacting variants on the same chromosome. This information can be obtained by phasing the read data from sequencing experiments. However, no standalone tools are available to simply phase reads based on known haplotypes. Here we present LRphase: a user-friendly utility for simple phasing of long sequencing reads.

Development of copper-catalyzed deaminative esterification using high-throughput experimentation.

Repurposing of amine and carboxylic acid building blocks provides an enormous opportunity to expand the accessible chemical space, because amine and acid feedstocks are typically low cost and available in high diversity. Herein, we report a copper-catalyzed deaminative esterification based on C-N activation of aryl amines via diazonium salt formation. The reaction was specifically designed to complement the popular amide coupling reaction. A chemoinformatic analysis of commercial building blocks demonstrates that by utilizing aryl amines, our method nearly doubles the available esterification chemical space compared to classic Fischer esterification with phenols. High-throughput experimentation in microliter reaction droplets was used to develop the reaction, along with classic scope studies, both of which demonstrated robust performance against hundreds of substrate pairs. Furthermore, we have demonstrated that this new esterification is suitable for late-stage diversification and for building-block repurposing to expand chemical space.

Reinforcing the supply chain of umifenovir and other antiviral drugs with retrosynthetic software.

The global disruption caused by the 2020 coronavirus pandemic stressed the supply chain of many products, including pharmaceuticals. Multiple drug repurposing studies for COVID-19 are now underway. If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it, will be available in the quantities required. Here, we utilize retrosynthetic software to arrive at alternate chemical supply chains for the antiviral drug umifenovir, as well as eleven other antiviral and anti-inflammatory drugs. We have experimentally validated four routes to umifenovir and one route to bromhexine. In one route to umifenovir the software invokes conversion of six C-H bonds into C-C bonds or functional groups. The strategy we apply of excluding known starting materials from search results can be used to identify distinct starting materials, for instance to relieve stress on existing supply chains.