The selective recognition of hydrophilic carbohydratesin waterremains a longstanding challenge in supramolecular chemistry due tosolvent competition and the lack of a strong driving force comparableto the hydrophobic effect. Herein, we report the design, synthesis,and characterization of a water-soluble tetralactam macrocycle, MPNT2+·2Cl–, as a highly effective syntheticlectin for glucuronate. MPNT2+·2Cl– features two dimethylnaphthalene panels, pyridinium spacers, andmorpholine side chains, forming a rigid, preorganized cavity withconvergent hydrogen bond donors, polarized C–H donors, andcomplementary electrostatic interactions. The receptor achieves abinding affinity of 103,000 M–1 for glucuronatein water, over 19-fold higher than previous synthetic systems, alongwith excellent selectivity over structurally similar carbohydrates.Single-crystal X-ray analysis, DFT calculation, and IGMH analysisreveal a dense network of [N–H···O], [C–H···O],and [C–H···π] interactions, highlightingthe role of stereoelectronic complementarity in complex formation.Moreover, MPNT2+·2Cl– acts as achiroptical sensor, producing binding-induced circular dichroism signalsthat enable sensitive detection of glucuronic acid at physiologicallyrelevant concentrations. This work presents a generalizable strategyfor designing synthetic lectins that recognize carbohydrates in aqueoussolutions and opens up new possibilities for developing molecularsensors and diagnostic tools for biologically important anionic sugars.

The human microbiome contains at least as many bacterial cells as human cells. Some bacteria offer benefits, like improving gut barrier function, suppressing pathobiont growth, and modulating immunity. These benefits have popularized probiotics, but probiotic retention is often hindered by low colonization efficiency in the mucosal layer that lines all epithelial cells. Mucins, the primary components of mucus, are essential for the organization and regulation of microbial populations. The molecular mechanisms of mucin-probiotic interactions remain understudied due, in part, to the inability to incisively manipulate native mucin sequences or their glycans. Here, we used synthetic mucins with defined glycan presentations to interrogate glycan-dependent interactions between mucus and three probiotic lactobacilli species. The nutrient conditions under which bacteria were cultured influenced glycan binding preferences, suggesting mucin-probiotic interactions change with nutrient availability. The addition of synthetic mucins to native mucin increased Limosilactobacillus fermentum adherence. Additionally, an increase in glycosidase activity indicated that native and synthetic mucins function as prebiotics, as probiotic bacteria can cleave the displayed O-glycans. Thus, synthetic mucins can cultivate target probiotic bacteria and increase adhesion as binding sites, highlighting their value as tools for elucidating native mucin functions and as promising agents for promoting human health.

Fully automated preparation of diverse small organicmoleculesremains a formidable challenge due to the inherent constraints ofconventional synthetic philosophies. The existing automation approachesrequire access to either almost unlimited kinds of chemical reagentsor custom-made building blocks (BBs). Herein we propose atom-by-atomiterative synthesis (AIS) as a new synthetic logic to tackle thischallenge. By viewing complex organic molecules as assemblies of single-carbon-or heteroatom-based units, AIS aims to construct molecular skeletonsthrough iterative coupling of simple atomic-scale BBs by a unifiedtype of reactionboron homologations. Compared with conventionalapproaches, the AIS strategy uses only a few types of chemical reactionsand a small set of BBs, making it more suitable for automation andartificial intelligence-assisted synthetic route design. To date,enormous progresses have been made on the synthetic chemistry thatserves for the purpose of AIS, such as introducing heteroatoms andsp2-carbons, forming ring structures, developing thermostablecarbenoid reagents, and achieving stereochemical controls. On theother hand, substantial challenges and limitations remain to be overcomefor realizing fully automated construction of diverse molecules. ThisOutlook article describes the AIS concept, recent progress, currentlimitations, and future opportunities in this field.

Microplastics in dementia-affected brains resisted identification until she lit them up.

Rillig uncovers how microplastics are transforming soil.

The late-stage functionalization of complex molecules is a pivotal strategy in drug discovery, enabling the rapid optimization of lead compounds. However, the use of gaseous alkanes as alkylating agents in these processes remains underexplored due to their inertness and handling challenges. Here we present a photocatalytic platform that facilitates the alkylation of heteroarenes using abundant gaseous C1-C4 hydrocarbons under continuous-flow conditions. Through hydrogen atom transfer (HAT) catalysis, we achieve the efficient alkylation of pharmaceutically relevant compounds without the need for prefunctionalized reagents. Our method is not only scalable and sustainable but also extends to the functionalization of marketed drugs and natural products.

Sirtuin 3 (SIRT3), a pivotal mitochondrial deacetylase, plays a critical role in restoring mitochondrial function, particularly through the activation of autophagy. Despite its promise as a cardioprotective target, developing SIRT3 activators and their therapeutic applications remains challenging. Here, we report the identification of SKLB-11A, a SIRT3 activator with submicromolar affinity and high efficacy. Structural and mutagenesis analyses revealed a unique allosteric site for SKLB-11A in SIRT3, where a conformational change in Leu298 drives its potent activation. Subsequent studies demonstrated that SKLB-11A drives autophagy/mitophagy signaling pathways, effectively preventing mitochondrial dysfunction, and improving cardiac dysfunction in both doxorubicin (Dox)-induced cardiotoxicity and myocardial ischemia/reperfusion (I/R) models. Collectively, our data highlight the potential of pharmacological SIRT3 activation as an effective therapeutic strategy for cardioprotection. SKLB-11A, as a first-in-class SIRT3 allosteric activator with a distinct binding mode, not only offers a valuable tool for exploring the physiological and pathological roles of SIRT3 deacetylation but also holds promise for the development of targeted cardioprotective therapies.

Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.

Artificial intelligence (AI) using large language models (LLMs) such as GPTs has revolutionized various fields. Recently, LLMs have also made inroads in chemical research even for users without expertise in coding. However, applying LLMs directly may lead to "hallucinations", where the model generates unreliable or inaccurate information and is further exacerbated by limited data set and inherent complexity of chemical reports. To counteract this, researchers have suggested prompt engineering, which can convey human ideas formatively and unambiguously to LLMs and simultaneously improve LLMs' reasoning capability. So far, prompt engineering remains underutilized in chemistry, with many chemists barely acquainted with its principle and techniques. In this Outlook, we delve into various prompt engineering techniques and illustrate relevant examples for extensive research from metal-organic frameworks and fast-charging batteries to autonomous experiments. We also elucidate the current limitations of prompt engineering with LLMs such as incomplete or biased outcomes and constraints imposed by closed-source limitations. Although LLM-assisted chemical research is still in its early stages, the application of prompt engineering will significantly enhance accuracy and reliability, thereby accelerating chemical research.