Nanoscale synthesis and affinity ranking

Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets

Efficiently exploring organic molecules through multi-step processes demands a transition from conventional laboratory synthesis to automated systems. Existing platforms for machine-assistant synthetic workflows compatible with multiple liquid-phases require substantial engineering investments for setup, thereby hindering quick customization and throughput increasement. Here we present a droplet-based chip that facilitates the self-organization of various liquid phases into stacked layers for conducting chemical transformations. The chip's precision polymer printing capability, enabled by digital micromirror device (DMD)-maskless photolithography and dual post-chemical modifications, allows it to create customized, sub-10 µm featured patterns to confine diverse liquids, regardless of density, within each droplet. The robustness and open design of surface-templated liquid layers actualize machine-assistant droplet manipulation, synchronous reaction triggering, local oscillation, and real-time monitoring of individual layers into a reality. We propose that, with further integration of machine operation line and self-learning, this droplet-based platform holds the potential to become a valuable addition to the toolkit of chemistry process, operating autonomously and with high-throughput.

Automated Synthesis of DNA Nanostructures

DNA nanotechnology has revolutionized the ability to position matter at the nanoscale, but the preparation of DNA-based architectures remains laborious. To facilitate the formation of custom structures, a fully automated method is reported to produce sequence- and size-defined DNA nanotubes. By programming the sequential addition of desired building blocks, rigid DX-tile-based DNA nanotubes and flexible wireframe DNA structures are attained, where the total number of possible constructs increases as a power function of the number of different units available. Using single-molecule fluorescence imaging, the kinetics and yield of each synthetic step can be quantitatively determined, revealing differences in self-assembly dynamics as the nanotube is built up from the solid support and providing new insights into DNA self-assembly. The exploitation of automation for both assembly and analysis (through an ad-hoc developed K-means clustering algorithm) facilitates a workflow wherein the synthesis parameters may be iteratively improved upon, demonstrating how a single-molecule "assembly-analysis-optimization" sequence can be used to generate complex, noncovalent materials in good yield. The presented synthetic strategy is generalizable, making use of equipment already available in most standard laboratories and represents the first fully automated supramolecular assembly on a solid support.

A strategy for the controllable generation of organic superbases from benchtop-stable salts

Organic superbases are a distinct class of strong base that enable numerous modern reaction applications. Despite their great synthetic potential, widespread use and study of superbases are limited by their air sensitivity and difficult preparation. To address this, we report air-stable carboxylate salts of BTPP and P2-t-Bu phosphazene superbases that, when added to solution with an epoxide, spontaneously generate freebase. These systems function as effective precatalysts and stoichiometric prereagents for superbase-promoted addition, substitution and polymerization reactions. In addition to improving the synthesis, shelf stability, handling and recycling of phosphazenes, this approach enables precise regulation of the rate of base generation in situ. The activation strategy effectively mimics manual slow addition techniques, allowing for control over a reaction's rate or induction period and improvement of reactions that require strong base but are also sensitive to its presence, such as Pd-catalyzed coupling reactions.

Accelerating PROTACs Discovery Through a Direct-to-Biology Platform Enabled by Modular Photoclick Chemistry

Proteolysis targeting chimeras (PROTACs) have emerged as a promising strategy for drug discovery and exploring protein functions, offering a revolutionary therapeutic modality. Currently, the predominant approach to PROTACs discovery mainly relies on an empirical design-synthesis-evaluation process involving numerous cycles of labor-intensive synthesis-purification and bioassay data collection. Therefore, the development of innovative methods to expedite PROTAC synthesis and exploration of chemical space remains highly desired. Here, a direct-to-biology strategy is reported to streamline the synthesis of PROTAC libraries on plates, enabling the seamless transfer of reaction products to cell-based bioassays without the need for additional purification. By integrating amide coupling and light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) photoclick chemistry into a plate-based synthetic process, this strategy produces PROTAC libraries with high efficiency and structural diversity. Moreover, by employing this platform for PROTACs screening, we smoothly found potent PROTACs effectively inhibit triple-negative breast cancer (TNBC) cell growth and induce rapid, selective targeted degradation of cyclin-dependent kinase 9 (CDK9). The study introduces a versatile platform for assembling PROTACs on plates, followed by direct biological evaluation. This approach provides a promising opportunity for high-throughput synthesis of PROTAC libraries, thereby enhancing the efficiency of exploring chemical space and accelerating the discovery of PROTACs.

Ultrapotent influenza hemagglutinin fusion inhibitors developed through SuFEx-enabled high-throughput medicinal chemistry

Seasonal and pandemic-associated influenza strains cause highly contagious viral respiratory infections that can lead to severe illness and excess mortality. Here, we report on the optimization of our small-molecule inhibitor F0045(S) targeting the influenza hemagglutinin (HA) stem with our Sulfur-Fluoride Exchange (SuFEx) click chemistry-based high-throughput medicinal chemistry (HTMC) strategy. A combination of SuFEx- and amide-based lead molecule diversification and structure-guided design led to identification and validation of ultrapotent influenza fusion inhibitors with subnanomolar EC50 cellular antiviral activity against several influenza A group 1 strains. X-ray structures of six of these compounds with HA indicate that the appended moieties occupy additional pockets on the HA surface and increase the binding interaction, where the accumulation of several polar interactions also contributes to the improved affinity. The compounds here represent the most potent HA small-molecule inhibitors to date. Our divergent HTMC platform is therefore a powerful, rapid, and cost-effective approach to develop bioactive chemical probes and drug-like candidates against viral targets.

Innovative, combinatorial and high-throughput approaches to degrader synthesis

Targeted protein degraders such as PROTACs and molecular glues are a rapidly emerging therapeutic modality within industry and academia. Degraders possess unique mechanisms of action that lead to the removal of specific proteins by co-opting the cell's natural degradation mechanisms via induced proximity. Their optimisation thus far has often been largely empirical, requiring the synthesis and screening of a large number of analogues. In addition, the synthesis and development of degraders is often challenging, leading to lengthy optimisation campaigns to deliver candidate-quality compounds. This review highlights how the synthesis of degraders has evolved in recent years, in particular focusing on means of applying high-throughput chemistry and screening approaches to expedite these timelines, which we anticipate to be valuable in shaping the future of degrader optimisation campaigns.

Late-Stage Saturation of Drug Molecules

The available methods of chemical synthesis have arguably contributed to the prevalence of aromatic rings, such as benzene, toluene, xylene, or pyridine, in modern pharmaceuticals. Many such sp2-carbon-rich fragments are now easy to synthesize using high-quality cross-coupling reactions that click together an ever-expanding menu of commercially available building blocks, but the products are flat and lipophilic, decreasing their odds of becoming marketed drugs. Converting flat aromatic molecules into saturated analogues with a higher fraction of sp3 carbons could improve their medicinal properties and facilitate the invention of safe, efficacious, metabolically stable, and soluble medicines. In this study, we show that aromatic and heteroaromatic drugs can be readily saturated under exceptionally mild rhodium-catalyzed hydrogenation, acid-mediated reduction, or photocatalyzed-hydrogenation conditions, converting sp2 carbon atoms into sp3 carbon atoms and leading to saturated molecules with improved medicinal properties. These methods are productive in diverse pockets of chemical space, producing complex saturated pharmaceuticals bearing a variety of functional groups and three-dimensional architectures. The rhodium-catalyzed method tolerates traces of dimethyl sulfoxide (DMSO) or water, meaning that pharmaceutical compound collections, which are typically stored in wet DMSO, can finally be reformatted for use as substrates for chemical synthesis. This latter application is demonstrated through the late-stage saturation (LSS) of 768 complex and densely functionalized small-molecule drugs.

Medicinal Chemistry Gone Wild

Earth is currently experiencing a mass extinction event. The flora and fauna of our planet are experiencing mass die-offs from a multitude of factors, with wildlife disease emerging as one parameter where medicinal chemists are equipped to intervene. While contemporary medicinal chemistry focuses on human health, many traditional pharmaceutical companies have historic roots in human health, animal health, and plant health. This trifecta of health sciences perfectly maps to the current field of One Health, which recognizes that optimal health outcomes can only be achieved through the health of humans, plants, animals, and their shared environments. This Perspective imagines a world where state-of-the-art medicinal chemistry tactics are used to prevent the extinction of endangered species and points to preliminary work in the emerging area of conservation medicine.

Development of an α-Klotho Recognizing High-Affinity Peptide Probe from In-Solution Enrichment

The kidney, parathyroid gland, and choroid plexus express the aging-related transmembrane protein α-Klotho, a coreceptor of the fibroblast growth factor 23 (FGF23) receptor complex. Reduced α-Klotho levels are correlated with chronic kidney disease and other age-related diseases, wherein they are released from membranes into circulation. Klotho's potential physiological action as a hormone is of current scientific interest. Part of the challenges associated with advancing these studies, however, has been the long-standing difficulty in detecting soluble α-Klotho in biofluids. Here, we describe the discovery of peptides that recognize α-Klotho with high affinity and selectivity by applying in-solution size-exclusion-based affinity selection-mass spectrometry (AS-MS). After two rounds of AS-MS and subsequent N-terminal modifications, the peptides improved their binding affinity to α-Klotho by approximately 2300-fold compared to the reported starting peptide Pep-10, previously designed based on the C-terminal region of FGF23. The lead peptide binders were shown to enrich α-Klotho from cell lysates and to label α-Klotho in kidney cells. Our results further support the utility of in-solution, label-free AS-MS protocols to discover peptide-based binders to target proteins of interest with high affinity and selectivity, resulting in functional probes for biological studies.

Multicomponent Synthesis of α-Branched Amines via a Zinc-Mediated Carbonyl Alkylative Amination Reaction

Methods for the synthesis of α-branched alkylamines are important due to their ubiquity in biologically active molecules. Despite the development of many methods for amine preparation, C(sp3)-rich nitrogen-containing compounds continue to pose challenges for synthesis. While carbonyl reductive amination (CRA) between ketones and alkylamines is the cornerstone method for α-branched alkylamine synthesis, it is sometimes limited by the sterically demanding condensation step between dialkyl ketones and amines and the more restricted availability of ketones compared to aldehydes. We recently reported a "higher-order" variant of this transformation, carbonyl alkylative amination (CAA), which utilized a halogen atom transfer (XAT)-mediated radical mechanism, enabling the streamlined synthesis of complex α-branched alkylamines. Despite the efficacy of this visible-light-driven approach, it displayed scalability issues, and competitive reductive amination was a problem for certain substrate classes, limiting applicability. Here, we report a change in the reaction regime that expands the CAA platform through the realization of an extremely broad zinc-mediated CAA reaction. This new strategy enabled elimination of competitive CRA, simplified purification, and improved reaction scope. Furthermore, this new reaction harnessed carboxylic acid derivatives as alkyl donors and facilitated the synthesis of α-trialkyl tertiary amines, which cannot be accessed via CRA. This Zn-mediated CAA reaction can be carried out at a variety of scales, from a 10 μmol setup in microtiter plates enabling high-throughput experimentation, to the gram-scale synthesis of medicinally-relevant compounds. We believe that this transformation enables robust, efficient, and economical access to α-branched alkylamines and provides a viable alternative to the current benchmark methods.

Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.

High-Throughput Capillary Liquid Chromatography Using a Droplet Injection and Application to Reaction Screening

The cycle time of a standard liquid chromatography (LC) system is the sum of the time for the chromatographic run and the autosampler injection sequence. Although LC separation times in the 1-10 s range have been demonstrated, injection sequences are commonly >15 s, limiting throughput possible with LC separations. Further, such separations are performed on relatively large bore columns requiring flow rates of ≥5 mL/min, thus generating large volumes of mobile phase waste when used for large scale screening and increasing the difficulty in interfacing to mass spectrometry. Here, a droplet injector system was established that replaces the autosampler with a four-port, two-position valve equipped with a 20 nL internal loop interfaced to a syringe pump and a three-axis positioner to withdraw sample droplets from a well plate. In the system, sample and immiscible fluid are pulled alternately from a well plate into a capillary and then through the injection valve. The valve is actuated when sample fills the loop to allow sequential injection of samples at high throughput. Capillary LC columns with 300 μm inner diameter were used to reduce the consumption of mobile phase and sample. The system achieved 96 separations of 20 nL droplet samples containing 3 components in as little as 8.1 min with 5-s cycle time. This system was coupled to a mass spectrometer through an electrospray ionization source for high-throughput chemical reaction screening.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

Rapid PROTAC Discovery Platform: Nanomole-Scale Array Synthesis and Direct Screening of Reaction Mixtures

Precise length, shape, and linker attachment points are all integral components to designing efficacious proteolysis targeting chimeras (PROTACs). Due to the synthetic complexity of these heterobifunctional degraders and the difficulty of computational modeling to aid PROTAC design, the exploration of structure-activity relationships remains mostly empirical, which requires a significant investment of time and resources. To facilitate rapid hit finding, we developed capabilities for PROTAC parallel synthesis and purification by harnessing an array of preformed E3-ligand-linker intermediates. In the next iteration of this approach, we developed a rapid, nanomole-scale PROTAC synthesis methodology using amide coupling that enables direct screening of nonpurified reaction mixtures in cell-based degradation assays, as well as logD and EPSA measurements. This approach greatly expands and accelerates PROTAC SAR exploration (5 days instead of several weeks) as well as avoids laborious and solvent-demanding purification of the reaction mixtures, thus making it an economical and more sustainable methodology for PROTAC hit finding.

Integrated Direct-to-Biology Platform for the Nanoscale Synthesis and Biological Evaluation of PROTACs

Proteolysis targeting chimeras (PROTACs) are heterobifunctional molecules that co-opt the cell's natural proteasomal degradation mechanisms to degrade undesired proteins. A challenge associated with PROTACs is the time and resource-intensive optimization; thus, the development of high-throughput platforms for their synthesis and biological evaluation is required. In this study, we establish an ultra-high-throughput experimentation (ultraHTE) platform for PROTAC synthesis, followed by direct addition of the crude reaction mixtures to cellular degradation assays without any purification. This 'direct-to-biology' (D2B) approach was validated and then exemplified in a medicinal chemistry campaign to identify novel BRD4 PROTACs. Using the D2B platform, the synthesis of 650 PROTACs was carried out in a 1536-well plate, and subsequent biological evaluation was performed by a single scientist in less than 1 month. Due to its ability to hugely accelerate the optimization of new degraders, we anticipate our platform will transform the synthesis and testing of PROTACs.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.

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.

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.

Discovery and Structure-Based Design of Inhibitors of the WD Repeat-Containing Protein 5 (WDR5)-MYC Interaction

WDR5 is a critical chromatin cofactor of MYC. WDR5 interacts with MYC through the WBM pocket and is hypothesized to anchor MYC to chromatin through its WIN site. Blocking the interaction of WDR5 and MYC impairs the recruitment of MYC to its target genes and disrupts the oncogenic function of MYC in cancer development, thus providing a promising strategy for the treatment of MYC-dysregulated cancers. Here, we describe the discovery of novel WDR5 WBM pocket antagonists containing a 1-phenyl dihydropyridazinone 3-carboxamide core that was identified from high-throughput screening and subsequent structure-based design. The leading compounds showed sub-micromolar inhibition in the biochemical assay. Among them, compound 12 can disrupt WDR5-MYC interaction in cells and reduce MYC target gene expression. Our work provides useful probes to study WDR5-MYC interaction and its function in cancers, which can also be used as the starting point for further optimization toward drug-like small molecules.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

'Chemistry at the speed of sound': automated 1536-well nanoscale synthesis of 16 scaffolds in parallel

Screening of large and diverse libraries is the 'bread and butter' in the first phase of the discovery of novel drugs. However, maintenance and periodic renewal of high-quality large compound collections pose considerable logistic, environmental and monetary problems. Here, we exercise an alternative, the 'on-the-fly' synthesis of large and diverse libraries on a nanoscale in a highly automated fashion. For the first time, we show the feasibility of the synthesis of a large library based on 16 different chemistries in parallel on several 384-well plates using the acoustic dispensing ejection (ADE) technology platform. In contrast to combinatorial chemistry, we produced 16 scaffolds at the same time and in a sparse matrix fashion, and each compound was produced by a random combination of diverse large building blocks. The synthesis, analytics, resynthesis of selected compounds, and chemoinformatic analysis of the library are described. The advantages of the herein described automated nanoscale synthesis approach include great library diversity, absence of library storage logistics, superior economics, speed of synthesis by automation, increased safety, and hence sustainable chemistry.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.

Integrated and automated high-throughput purification of libraries on microscale

We herein report the development of an automation platform for rapid purification and quantification of chemical libraries including reformatting of chemical matter to 10 mM DMSO stock solutions. This fully integrated workflow features tailored conditions for preparative reversed-phase (RP) HPLC-MS on microscale based on analytical data, online fraction QC and CAD-based quantification as well as automated reformatting to enable rapid purification of chemical libraries. This automated workflow is entirely solution-based, eliminating the need to weigh or handle solids. This increases process efficiency and creates a link between high-throughput synthesis and profiling of novel chemical matter with respect to biological and physicochemical properties in relevant assays.

Optimization of Covalent MKK7 Inhibitors via Crude Nanomole-Scale Libraries

High-throughput nanomole-scale synthesis allows for late-stage functionalization (LSF) of compounds in an efficient and economical manner. Here, we demonstrated that copper-catalyzed azide-alkyne cycloaddition could be used for the LSF of covalent kinase inhibitors at the nanoscale, enabling the synthesis of hundreds of compounds that did not require purification for biological assay screening, thus reducing experimental time drastically. We generated crude libraries of inhibitors for the kinase MKK7, derived from two different parental precursors, and analyzed them via the high-throughput In-Cell Western assay. Select inhibitors were resynthesized, validated via conventional biological and biochemical methods such as western blots and liquid chromatography-mass spectrometry (LC-MS) labeling, and successfully co-crystallized. Two of these compounds showed over 20-fold increased inhibitory activity compared to the parental compound. This study demonstrates that high-throughput LSF of covalent inhibitors at the nanomole-scale level can be an auspicious approach in improving the properties of lead chemical matter.

Tricyclic Aza-Andrographolide Derivatives from Late-Stage Hydroamination and Their Anti-human Coronavirus (Anti-HCoV) Activity

A late-stage functionalization (LSF) of the natural product andrographolide for the efficient assembly of a range of structurally interesting and diverse tricyclic-aza derivatives was developed. The key to the diversification is a photo-catalyzed intramolecular hydroamination reaction, and acridinium derivatives were demonstrated to be the optimal catalysts. Additionally, the synthesized tricyclic aza-andrographolide derivatives were found to inhibit human coronavirus with high potency.

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.

Direct-to-Biology Accelerates PROTAC Synthesis and the Evaluation of Linker Effects on Permeability and Degradation

A platform to accelerate optimization of proteolysis targeting chimeras (PROTACs) has been developed using a direct-to-biology (D2B) approach with a focus on linker effects. A large number of linker analogs-with varying length, polarity, and rigidity-were rapidly prepared and characterized in four cell-based assays by streamlining time-consuming steps in synthesis and purification. The expansive dataset informs on linker structure-activity relationships (SAR) for in-cell E3 ligase target engagement, degradation, permeability, and cell toxicity. Unexpected aspects of linker SAR was discovered, consistent with literature reports on "linkerology", and the method dramatically speeds up empirical optimization. Physicochemical property trends emerged, and the platform has the potential to rapidly expand training sets for more complex prediction models. In-depth validation studies were carried out and confirm the D2B platform is a valuable tool to accelerate PROTAC design-make-test cycles.

Synthesis and direct assay of large macrocycle diversities by combinatorial late-stage modification at picomole scale

Macrocycles have excellent potential as therapeutics due to their ability to bind challenging targets. However, generating macrocycles against new targets is hindered by a lack of large macrocycle libraries for high-throughput screening. To overcome this, we herein established a combinatorial approach by tethering a myriad of chemical fragments to peripheral groups of structurally diverse macrocyclic scaffolds in a combinatorial fashion, all at a picomole scale in nanoliter volumes using acoustic droplet ejection technology. In a proof-of-concept, we generate a target-tailored library of 19,968 macrocycles by conjugating 104 carboxylic-acid fragments to 192 macrocyclic scaffolds. The high reaction efficiency and small number of side products of the acylation reactions allowed direct assay without purification and thus a large throughput. In screens, we identify nanomolar inhibitors against thrombin (K_i = 44 ± 1 nM) and the MDM2:p53 protein-protein interaction (K_d MDM2 = 43 ± 18 nM). The increased efficiency of macrocycle synthesis and screening and general applicability of this approach unlocks possibilities for generating leads against any protein target.

Macrocycles have potential as therapeutics, but their libraries are currently not large enough for high-throughput screening. Here, the authors show a combinatorial approach to generate a library of almost 20’000 macrocycles by conjugating carboxylic-acid fragments to macrocyclic scaffolds, identifying nanomolar inhibitors against thrombin and binders of MDM2.

Thiocyanatoarylation of Methyl Vinyl Ketone under Meerwein Conditions for the Synthesis of 2-Aminothiazole-Based Heterocyclic Systems

4-Aryl-3-thiocyanatobutan-2-ones were prepared by Meerwein reactions from methyl vinyl ketone and aryldiazonium salts under copper(II) catalysis in 35-75% yields. α-Thiocyanato ketones regioselectively react with 1-methyl-3-aminopyrazole forming N-(3-pyrazolyl)-substituted 2-aminothiazoles in 80-91% yields. An ester group in position 3 of the pyrazole induced a regioselective ring-closure reaction followed by an intramolecular cyclization, which gave first representatives of a new heterocyclic system, pyrazolo[4,3-e]thiazolo[3,2-a]pyrimidine, in 74-93% yields. In addition, the preparations of 5-benzyl-4-methylthiazol-2-ones in 84-93% yields are described.

Homogeneous Organic Electron Donors in Nickel-Catalyzed Reductive Transformations

Many contemporary organic transformations, such as Ni-catalyzed cross-electrophile coupling (XEC), require a reductant. Typically, heterogeneous reductants, such as Zn0 or Mn0, are used as the electron source in these reactions. Although heterogeneous reductants are highly practical for preparative-scale batch reactions, they can lead to complications in performing reactions on process scale and are not easily compatible with modern applications, such as flow chemistry. In principle, homogeneous organic reductants can address some of the challenges associated with heterogeneous reductants and also provide greater control of the reductant strength, which can lead to new reactivity. Nevertheless, homogeneous organic reductants have rarely been used in XEC. In this Perspective, we summarize recent progress in the use of homogeneous organic electron donors in Ni-catalyzed XEC and related reactions, discuss potential synthetic and mechanistic benefits, describe the limitations that inhibit their implementation, and outline challenges that need to be solved in order for homogeneous organic reductants to be widely utilized in synthetic chemistry. Although our focus is on XEC, our discussion of the strengths and weaknesses of different methods for introducing electrons is general to other reductive transformations.

Accelerated Identification of Cell Active KRAS Inhibitory Macrocyclic Peptides using Mixture Libraries and Automated Ligand Identification System (ALIS) Technology

Macrocyclic peptides can disrupt previously intractable protein–protein interactions (PPIs) relevant to oncology targets such as KRAS. Early hits often lack cellular activity and require meticulous improvement of affinity, permeability, and metabolic stability to become viable leads. We have validated the use of the Automated Ligand Identification System (ALIS) to screen oncogenic KRAS^G12D (GDP) against mass-encoded mini-libraries of macrocyclic peptides and accelerate our structure–activity relationship (SAR) exploration. These mixture libraries were generated by premixing various unnatural amino acids without the need for the laborious purification of individual peptides. The affinity ranking of the peptide sequences provided SAR-rich data sets that led to the selection of novel potency-enhancing substitutions in our subsequent designs. Additional stability and permeability optimization resulted in the identification of peptide 7 that inhibited pERK activity in a pancreatic cancer cell line. More broadly, this methodology offers an efficient alternative to accelerate the fastidious hit-to-lead optimization of PPI peptide inhibitors.

Predicting reaction conditions from limited data through active transfer learning

Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.

Amino-oxetanes as amide isosteres by an alternative defluorosulfonylative coupling of sulfonyl fluorides

Bioisosteres provide valuable design elements that medicinal chemists can use to adjust the structural and pharmacokinetic characteristics of bioactive compounds towards viable drug candidates. Aryl oxetane amines offer exciting potential as bioisosteres for benzamides-extremely common pharmacophores-but are rarely examined due to the lack of available synthetic methods. Here we describe a class of reactions for sulfonyl fluorides to form amino-oxetanes by an alternative pathway to the established SuFEx (sulfonyl-fluoride exchange) click reactivity. A defluorosulfonylation forms planar oxetane carbocations simply on warming. This disconnection, comparable to a typical amidation, will allow the application of vast existing amine libraries. The reaction is tolerant to a wide range of polar functionalities and is suitable for array formats. Ten oxetane analogues of bioactive benzamides and marketed drugs are prepared. Kinetic and computational studies support the formation of an oxetane carbocation as the rate-determining step, followed by a chemoselective nucleophile coupling step.

Multiplexed Small-Molecule-Ligand Binding Assays by Affinity Labeling and DNA Sequence Analysis

Small-molecule binding assays to target proteins are a core component of drug discovery and development. While a number of assay formats are available, significant drawbacks still remain in cost, sensitivity, and throughput. To improve assays by capitalizing on the power of DNA sequence analysis, we have developed an assay method that combines DNA encoding with split-and-pool sample handling. The approach involves affinity labeling of DNA-linked ligands to a protein target. Critically, the labeling event assesses ligand binding and enables subsequent pooling of several samples. Application of a purifying selection on the pool for protein-labeled DNAs allows detection of ligand binding by quantification of DNA barcodes. We demonstrate the approach in both ligand displacement and direct binding formats and demonstrate its utility in determination of relative ligand affinity, profiling ligand specificity, and high-throughput small-molecule screening.

Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients

The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.

Tunable and Practical Homogeneous Organic Reductants for Cross-Electrophile Coupling

The syntheses of four new tunable homogeneous organic reductants based on a tetraaminoethylene scaffold are reported. The new reductants have enhanced air stability compared to current homogeneous reductants for metal-mediated reductive transformations, such as cross-electrophile coupling (XEC), and are solids at room temperature. In particular, the weakest reductant is indefinitely stable in air and has a reduction potential of -0.85 V versus ferrocene, which is significantly milder than conventional reductants used in XEC. All of the new reductants can facilitate C(sp²)-C(sp³) Ni-catalyzed XEC reactions and are compatible with complex substrates that are relevant to medicinal chemistry. The reductants span a range of nearly 0.5 V in reduction potential, which allows for control over the rate of electron transfer events in XEC. Specifically, we report a new strategy for controlled alkyl radical generation in Ni-catalyzed C(sp²)-C(sp³) XEC. The key to our approach is to tune the rate of alkyl radical generation from Katritzky salts, which liberate alkyl radicals upon single electron reduction, by varying the redox potentials of the reductant and Katritzky salt utilized in catalysis. Using our method, we perform XEC reactions between benzylic Katritzky salts and aryl halides. The method tolerates a variety of functional groups, some of which are particularly challenging for most XEC transformations. Overall, we expect that our new reductants will both replace conventional homogeneous reductants in current reductive transformations due to their stability and relatively facile synthesis and lead to the development of novel synthetic methods due to their tunability.

Lessons in Organic Fluorescent Probe Discovery

Fluorescent probes have gained profound use in biotechnology, drug discovery, medical diagnostics, molecular and cell biology. The development of methods for the translation of fluorophores into fluorescent probes continues to be a robust field for medicinal chemists and chemical biologists, alike. Access to new experimental designs has enabled molecular diversification and led to the identification of new approaches to probe discovery. This review provides a synopsis of the recent lessons in modern fluorescent probe discovery.

Discovery of Inhibitors for Mycobacterium Tuberculosis Peptide Deformylase Based on Virtual Screening in Silico

Tuberculosis has been the serious disease threatening human health and public safety due to the emergence of MDR and XDR-TB. Mycobacterium tuberculosis peptide deformylase (MtPDF) is a valuable target for antituberculotics. In order to discover new potential inhibitor candidates of MtPDF as leads for antituberculotics, Discovery Studio (DS) 2019 was used to perform molecular docking for virtual screening in silico with the bioactive compound library-I (L1700) against MtPDF. Six compounds with high docking scores and favourable ligand-protein interactions by LibDock and CDOCKER were selected for the evaluation of the inhibition potencies against MtPDF and Mycobacterium smegmatis. GST-6×His tagged MtPDF was recombinant expressed and purified firstly by Glutathione Sepharose 4B, and secondly by Ni Sepharose 6 FF after the cleavage of human rhinovirus 3C protease. These compounds showed IC₅₀ values from 0.5 μmol/L to 112 μmol/L against MtPDF, among which CUDC-101 bearing hydroxamic acid exhibited IC₅₀ of 0.5 μmol/L on MtPDF and MIC against Mycobacterium smegmatis of 32 μg/mL, and Ixazomib Citrate with IC₅₀ of 63 μmol/L and MIC of 16 μg/mL. CUDC-101 and Ixazomib Citrate are promising as the potential leads for antituberculotics.

Chemputation and the Standardization of Chemical Informatics

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Nanomole-scaled high-throughput chemistry plus direct bioautography on the same chromatography plate for drug discovery

The powerful fusion of on-surface synthesis and effect-directed analysis was introduced as novel tool for synthetic drug discovery, all on the same high-performance thin-layer chromatography plate. Precise automated sample application allowed both, high-throughput chemistry of 60 reactions at once and reaction miniaturization down to the 15-nmol scale. The antibiotic activity of all on-surface synthesized compounds was evaluated on the same surface via the Gram-positive Bacillus subtilis bioassay. For one product, synthesis (reaction, purification and identification) took 5.3 min and semi-quantitative biological evaluation took 2.8 min. Out of 60 on-surface reactions 10 products (17%) were identified to be more active than a well-known antibiotic reference. The concept was transferred to the Gram-negative Aliivibrio fischeri bioassay. For the first time, a new analytical platform was shown for a streamlined workflow at the most miniaturized scale from synthesis, purification, identification and quantification to semi-quantitative biological activity evaluation (all on the same chromatography plate).

Picomole-Scale Synthesis and Screening of Macrocyclic Compound Libraries by Acoustic Liquid Transfer

Macrocyclic compounds are an attractive class of therapeutic ligands against challenging targets, such as protein-protein interactions. However, the development of macrocycles as drugs is hindered by the lack of large combinatorial macrocyclic libraries, which are cumbersome, expensive, and time consuming to make, screen, and deconvolute. Here, we established a strategy for synthesizing and screening combinatorial libraries on a picomolar scale by using acoustic droplet ejection to combine building blocks at nanoliter volumes, which reduced the reaction volumes, reagent consumption, and synthesis time. As a proof-of-concept, we assembled a 2700-member target-focused macrocyclic library that we could subsequently assay in the same microtiter synthesis plates, saving the need for additional transfers and deconvolution schemes. We screened the library against the MDM2-p53 protein-protein interaction and generated micromolar and sub-micromolar inhibitors. Our approach based on acoustic liquid transfer provides a general strategy for the development of macrocycle ligands.

Towards Data-driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.

Combining High-Throughput Synthesis and High-Throughput Protein Crystallography for Accelerated Hit Identification

Protein crystallography (PX) is widely used to drive advanced stages of drug optimization or to discover medicinal chemistry starting points by fragment soaking. However, recent progress in PX could allow for a more integrated role into early drug discovery. Here, we demonstrate for the first time the interplay of high throughput synthesis and high throughput PX. We describe a practical multicomponent reaction approach to acrylamides and -esters from diverse building blocks suitable for mmol scale synthesis on 96-well format and on a high-throughput nanoscale format in a highly automated fashion. High-throughput PX of our libraries efficiently yielded potent covalent inhibitors of the main protease of the COVID-19 causing agent, SARS-CoV-2. Our results demonstrate, that the marriage of in situ HT synthesis of (covalent) libraires and HT PX has the potential to accelerate hit finding and to provide meaningful strategies for medicinal chemistry projects.

Late-stage C–H functionalization offers new opportunities in drug discovery

Over the past decade, the landscape of molecular synthesis has gained major impetus by the introduction of late-stage functionalization (LSF) methodologies. C-H functionalization approaches, particularly, set the stage for new retrosynthetic disconnections, while leading to improvements in resource economy. A variety of innovative techniques have been successfully applied to the C-H diversification of pharmaceuticals, and these key developments have enabled medicinal chemists to integrate LSF strategies in their drug discovery programmes. This Review highlights the significant advances achieved in the late-stage C-H functionalization of drugs and drug-like compounds, and showcases how the implementation of these modern strategies allows increased efficiency in the drug discovery process. Representative examples are examined and classified by mechanistic patterns involving directed or innate C-H functionalization, as well as emerging reaction manifolds, such as electrosynthesis and biocatalysis, among others. Structurally complex bioactive entities beyond small molecules are also covered, including diversification in the new modalities sphere. The challenges and limitations of current LSF methods are critically assessed, and avenues for future improvements of this rapidly expanding field are discussed. We, hereby, aim to provide a toolbox for chemists in academia as well as industrial practitioners, and introduce guiding principles for the application of LSF strategies to access new molecules of interest.