Molecular Complexity Research Articles

Complexity-Building Exhaustive Dearomatization of Benzenoid Aromatics within an ESIPT-Initiated Three-Step Photochemical Cascade.

Dearomative cycloadditions offer rapid access to complex 3D molecular architectures, commonly via a sp2-to-sp3 rehybridization of two atoms of an aromatic ring. Here we report that the 6e π-system of a benzenoid aromatic pendant could be exhaustively depleted within a single photochemical cascade. An implementation of this approach involves the initial dearomative [4+2] cycloaddition of the Exited State Intramolecular Proton Transfer (ESIPT)-generated azaxylylene, followed by two consecutive [2+2] cycloadditions of auxiliary π moieties strategically positioned in the photoprecursor. Such photochemical cascade fully dearomatizes the benzenoid aromatic ring, saturating all six sp2 atoms to yield a complex sp3-rich scaffold with high control of its 3D molecular shape, rendering it a robust platform for rapid systematic mapping of underexplored chemical space. Significant growth of molecular complexity - starting with a modular synthesis of photoprecursors from readily available building blocks - is quantified by Böttcher score calculations.

Molecular complexity as a driving force for the advancement of organic synthesis.

The generation of molecular complexity is a primary goal in the field of synthetic chemistry. In the context of retrosynthetic analysis, the concept of molecular complexity is central to identifying productive disconnections and the development of efficient total syntheses. However, this field-defining concept is frequently invoked on an intuitive basis without precise definition or appreciation of its subtleties. Methods for quantifying molecular complexity could prove useful for characterizing the state of synthesis in a more rigorous, reliable and reproducible fashion. As a first step to evaluating the importance of these methods to the state of the field, here we present our perspective on the development of molecular complexity quantification and its implications for chemical synthesis. The extension and application of these methods beyond computer-aided synthesis planning and medicinal chemistry to the traditional practice of 'complex molecule' synthesis could have the potential to unearth new opportunities and more efficient approaches for synthesis.

Photocatalytic Multisite Functionalization of Unactivated Terminal Alkenes by Merging Polar Cycloaddition and Radical Ring-Opening Process.

Although highly appealing for rapid access of molecular complexity, multi-functionalization of alkenes that allows incorporation of more than two functional groups remains a prominent challenge. Herein, we report a novel strategy that merges dipolar cycloaddition with photoredox promoted radical ring-opening remote C(sp3)-H functionalization, thus enabling a smooth 1,2,5-trifunctionalization of unactivated alkenes. A highly regioselective [3+2] cycloaddition anchors a reaction trigger onto alkene substrates. The subsequent halogen atom transfer (XAT) selectively initiates ring-opening process, which is followed by a series of 1,5-hydrogen atom transfer (1,5-HAT) and intermolecular fluorine atom transfer (FAT) events. With this method, site-selective introduction of three different functional groups is accomplished and a broad spectrum of valuable β-hydroxyl-ϵ-fluoro-nitrile products are synthesized from readily available terminal alkenes.

Photocatalytic Multisite Functionalization of Unactivated Terminal Alkenes by Merging Polar Cycloaddition and Radical Ring‐Opening Process

AbstractAlthough highly appealing for rapid access of molecular complexity, multi‐functionalization of alkenes that allows incorporation of more than two functional groups remains a prominent challenge. Herein, we report a novel strategy that merges dipolar cycloaddition with photoredox promoted radical ring‐opening remote C(sp3)−H functionalization, thus enabling a smooth 1,2,5‐trifunctionalization of unactivated alkenes. A highly regioselective [3+2] cycloaddition anchors a reaction trigger onto alkene substrates. The subsequent halogen atom transfer (XAT) selectively initiates ring‐opening process, which is followed by a series of 1,5‐hydrogen atom transfer (1,5‐HAT) and intermolecular fluorine atom transfer (FAT) events. With this method, site‐selective introduction of three different functional groups is accomplished and a broad spectrum of valuable β‐hydroxyl‐ϵ‐fluoro‐nitrile products are synthesized from readily available terminal alkenes.

Molecular insights into the hedgehog signaling pathway correlated non-coding RNAs in acute lymphoblastic leukemia, a bioinformatics study.

Acute lymphoblastic leukemia (ALL) is a common hematologic cancer with unique incidence and prognosis patterns in people of all ages. Recent molecular biology advances have illuminated ALL's complex molecular pathways, notably the Hedgehog (Hh) signaling system and non-coding RNAs (ncRNAs). This work aimed to unravel the molecular complexities of the link between Hh signaling and ALL by concentrating on long non-coding RNAs (lncRNAs) and their interactions with significant Hh pathway genes. To analyze differentially expressed lncRNAs and genes in ALL, microarray data from the Gene Expression Omnibus (GEO) was reanalyzed using a systems biology approach. Hh signaling pathway-related genes were identified and their relationship with differentially expressed long non-coding RNAs (DElncRNAs) was analyzed using Pearson's correlation analysis. A regulatory network was built by identifying miRNAs that target Hh signaling pathway-related mRNAs. 193 DEGs and 226 DElncRNAs were found between ALL and normal bone marrow samples. Notably, DEGs associated with the Hh signaling pathway were correlated to 26 DElncRNAs. Later studies showed interesting links between DElncRNAs and biological processes and pathways, including drug resistance, immune system control, and carcinogenic characteristics. DEGs associated with the Hh signaling pathway have miRNAs in common with miRNAs already known to be involved in ALL, including miR-155-5p, and miR-211, highlighting the complexity of the regulatory landscape in this disease. The complex connections between Hh signaling, lncRNAs, and miRNAs in ALL have been unveiled in this study, indicating that DElncRNAs linked to Hh signaling pathway genes could potentially serve as therapeutic targets and diagnostic biomarkers for ALL.

Electrochemical Trioxygenation of Allylarenes

AbstractTrioxygenation is a highly effective method for rapidly increasing molecular complexity by incorporating three C–O bonds from simple, readily available raw materials. In this study, we present an electrochemical trioxygenation protocol for allylarenes, which enables the synthesis of a diverse array of triacetoxylation products without external chemical oxidants. These products, which are difficult to obtain through conventional methods, highlight the potential of electrochemistry in promoting sustainable synthesis.

Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution

Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and can be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.

Cigarette Smoke-Induced Epithelial-to-Mesenchymal Transition: Insights into Cellular Mechanisms and Signaling Pathways.

This review delves into the molecular complexities underpinning the epithelial-to-mesenchymal transition (EMT) induced by cigarette smoke (CS) in human bronchial epithelial cells (HBECs). The complex interplay of pathways, including those related to WNT//β-catenin, TGF-β/SMAD, hypoxia, oxidative stress, PI3K/Akt, and NF-κB, plays a central role in mediating this transition. While these findings significantly broaden our understanding of CS-induced EMT, the research reviewed herein leans heavily on 2D cell cultures, highlighting a research gap. Furthermore, the review identifies a stark omission of genetic and epigenetic factors in recent studies. Despite these shortcomings, the findings furnish a consolidated foundation not only for the academic community but also for the broader scientific and industrial sectors, including large tobacco companies and manufacturers of related products, both highlighting areas of current understanding and identifying areas for deeper exploration. The synthesis herein aims to propel further research, hoping to unravel the complexities of the EMT in the context of CS exposure. This review not only expands our understanding of CS-induced EMT but also reveals critical limitations in current methodologies, primarily the reliance on 2D cell cultures, which may not adequately simulate more complex biological interactions. Additionally, it highlights a significant gap in the literature concerning the genetic and epigenetic factors involved in CS-induced EMT, suggesting an urgent need for comprehensive studies that incorporate these types of experiments.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level.

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Discovery, synthesis and SAR of 2-acyl-1-biarylmethyl pyrazolidines, dual orexin receptor antagonists designed as fast and short-acting sleeping drugs

Dual orexin receptor antagonists (DORAs) are approved for the treatment of sleep onset and/or sleep maintenance insomnia. In the present disclosure, we report the discovery of a new class of DORAs designed to treat sleep disorders requiring a fast onset and a short duration of action (<4 h). We used early human pharmacokinetic-pharmacodynamic (PK-PD) predictions and in vivo experiments to identify DORAs eliciting this specific hypnotic profile. A high-throughput screening campaign revealed hits based on a rarely precedented tricyclic pyrazolidine scaffold. After unsuccessful structure–activity-relationship (SAR) studies on this hit series, a scaffold hopping exercise, aimed at reducing the molecular complexity of the tricyclic scaffold, resulted in the discovery of the 2-acyl-1-biarylmethylpyrazolidine series. SAR studies on this achiral series gave rise to the lead compound DORA 42. In vitro and in vivo parameters of DORA 42, and its PK-PD simulation for human use are detailed.

Research Progress on Saccharide Molecule Detection Based on Nanopores.

Saccharides, being one of the fundamental molecules of life, play essential roles in the physiological and pathological functions of cells. However, their intricate structures pose challenges for detection. Nanopore technology, with its high sensitivity and capability for single-molecule-level analysis, has revolutionized the identification and structural analysis of saccharide molecules. This review focuses on recent advancements in nanopore technology for carbohydrate detection, presenting an array of methods that leverage the molecular complexity of saccharides. Biological nanopore techniques utilize specific protein binding or pore modifications to trigger typical resistive pulses, enabling the high-sensitivity detection of monosaccharides and oligosaccharides. In solid-state nanopore sensing, boronic acid modification and pH gating mechanisms are employed for the specific recognition and quantitative analysis of polysaccharides. The integration of artificial intelligence algorithms can further enhance the accuracy and reliability of analyses. Serving as a crucial tool in carbohydrate detection, we foresee significant potential in the application of nanopore technology for the detection of carbohydrate molecules in disease diagnosis, drug screening, and biosensing, fostering innovative progress in related research domains.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

Tandem Synthesis of Benzylidenemalononitrile Derivatives in/on Water under Visible Light

AbstractTandem processes are valuable tools that allow to build molecular complexity while reducing waste production and the number of steps of synthetic routes. In this work, the in situ photooxidation of benzyl alcohols to the corresponding benzaldehydes is coupled with a Knoevenagel condensation for the preparation of benzylidenemalononitrile derivatives. In this rapid one‐pot tandem process, sodium anthraquinone‐1,5‐disulfonate (SAS) and β‐alanine are employed as safe and inexpensive catalysts, while air is used as a terminal oxidant. Moreover, using water as reaction medium results in most cases in the precipitation of the target products, thus facilitating their isolation.

Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.

Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type. Each cell sample was represented by 29,266 gene features. These features were first analyzed by six feature-ranking algorithms, resulting in several feature lists. Then, these lists were fed into incremental feature selection, containing two classification algorithms, to extract essential gene features and classification rules and build efficient classifiers. The identified essential genes can be potential CHD markers in different cardiac cell types. For instance, the LASSO identified key genes specific to various heart cell types in CHD subtypes. FOXO3 was found to be up-regulated in cardiac fibroblasts for both Dilated and hypertrophic cardiomyopathy. In cardiomyocytes, distinct genes such as TMTC1, ART3, ARHGAP24, SHROOM3, and XIST were linked to dilated cardiomyopathy, Neo-Hypoplastic Left Heart Syndrome, hypertrophic cardiomyopathy, HF-Hypoplastic Left Heart Syndrome, and Tetralogy of Fallot, respectively. Endothelial cell analysis further revealed COL25A1, NFIB, and KLF7 as significant genes for dilated cardiomyopathy, hypertrophic cardiomyopathy, and Tetralogy of Fallot. LightGBM, Catboost, MCFS, RF, and XGBoost further delineated key genes for specific CHD subtypes, demonstrating the efficacy of machine learning in identifying CHD-specific genes. Additionally, this study developed quantitative rules for representing the gene expression patterns related to CHDs. This research underscores the potential of machine learning in unraveling the molecular complexities of CHD and establishes a foundation for future mechanism-based studies.

Deciphering the molecular complexity: Employing density functional theory to probe 2-aminopyridinium 4-methyl benzene sulfonate elucidates synthesis methodologies, spectroscopic trends, reactivity patterns, molecular dynamics, and antibacterial properties

Harnessing computational modeling with density functional theory (DFT) and the B3LYP functional, delved into molecular geometry, harmonic spectra, and vibrational spectra for 2-aminopyridinium 4-methylbenzene sulfonate (2AP4MBS). Leveraging VEDA software to conduct a detailed investigation into oscillation spectra, particularly focusing on both quantitative and qualitative facets of infrared (IR) and Raman spectra. Our work also explored natural bonding orbitals, Fukui analysis, RDG analysis, and net Mulliken charge calculations, hinting at potential charge transfer within the molecule. Experimental validation of UV–Vis spectroscopic analyses was conducted. Electron density homology surface mapping via electrostatic potential interface (ESP) revealed insights into size, shape, charge density distribution, and reactivity sites. Topological features were elucidated through ELF and LOL maps.Docking simulations shed light on binding energy and interactions with peptide molecules, offering potential pharmaceutical applications following Lipinski criteria and ADMET framework assessments.

Comparing total chemical synthesis and total biosynthesis routes to fungal specialized metabolites.

Covering the period 1965-2024Total synthesis has been defined as the art and science of making the molecules of living Nature in the laboratory, and by extension, their analogues. At the extremes, specialised metabolites can be created by total chemical synthesis or by total biosynthesis. In this review we explore the advantages and disadvantages of these two approaches using quantitative methodology that combines measures of molecular complexity, molecular weight and fraction of sp3 centres for bioactive fungal metabolites. Total biosynthesis usually involves fewer chemical steps and those steps move more directly to the target than comparable total chemical synthesis. However, total biosynthesis currently lacks the flexibility of chemical synthesis and the ability to easily diversify synthetic routes.

A Note on Wiener and Hyper-Wiener Indices of Abid-Waheed Graph

A Hosoya polynomial is a polynomial connected to a molecular graph, which is a graph representation of a chemical compound with atoms as vertices and chemical bonds as edges. A graph invariant is the Hosoya polynomial; it is a graph attribute that does not change under graph isomorphism. It provides information about the number of unique non-empty subgraphs in a given graph. A molecular graph's size and branching complexity are determined by a topological metric known as the Wiener index. The Wiener index of each pair of vertices in a molecular network is the sum of those distances. The topological index, one of the various classes of graph invariants, is a real number related to a connected graph's structure .The goal of this article is to compute the Hosoya polynomial of some class of Abid-Waheed graph. Further, this research focused on a C++ algorithm to calculate the wiener index of and . The Wiener index (W∗I) and Hyper-Wiener index (H∗W∗I) are calculated using Hosoya polynomial (H∗-polynomial) of some family of Abid-Waheed graphs and . Illustrations and applications are given to enhance the research work.

Visible-Light Mediated Nickel-Catalyzed Asymmetric Difunctionalizations of Alkenes.

Difunctionalizations of alkenes represent one of the most straightforward protocols to build molecular complexity due to the simultaneous construction of two vicinal bonds cross π-bond of alkenes. It is extremely attractive yet challenging to control the stereochemistry outcome of this event. Over the past years, visible-light and Ni-catalyzed asymmetric difunctionalizations of alkenes provide an environmental benign and promising solution for the construction of saturated carbon centers with the control of regio- and enantioselectivity. In this Concept, the initiative and progress of regio- and enantioselective difunctionalizations of alkenes enabled by visible-light and nickel catalysis has been summarized. Moreover, further efforts and directions for the development of visible-light mediated Ni-catalyzed asymmetric difunctionalizations of alkenes has been discussed.

Enantioselective SN1-type reaction via electrochemically generated chiral α-Imino carbocation intermediate

Electrochemical reactions via carbocation intermediates remain fundamental transformations that build up molecular functionality and complexity in a sustainable manner. Enantioselective control of such processes is a great challenge in a highly ionic electrolyte solution. Here, we report an anodic generation of chiral α-imino carbocation intermediates by enamine catalysis. The chiral carbocation intermediates can be intercepted by a variety of nucleophiles such as alcohols, water and thiols with high stereoselectivity. The key SN1 step proceeds via a tertiary amine-mediated proton shuttle that facilitates facial selection in reacting with carbocation.

Cell-surface Milieu Remodeling in Human Dendritic Cell Activation.

Dendritic cells (DCs) are specialized sentinel and APCs coordinating innate and adaptive immunity. Through proteins on their cell surface, DCs sense changes in the environment, internalize pathogens, present processed Ags, and communicate with other immune cells. By combining chemical labeling and quantitative mass spectrometry, we systematically profiled and compared the cell-surface proteomes of human primary conventional DCs (cDCs) in their resting and activated states. TLR activation by a lipopeptide globally reshaped the cell-surface proteome of cDCs, with >100 proteins upregulated or downregulated. By simultaneously elevating positive regulators and reducing inhibitory signals across multiple protein families, the remodeling creates a cell-surface milieu promoting immune responses. Still, cDCs maintain the stimulatory-to-inhibitory balance by leveraging a distinct set of inhibitory molecules. This analysis thus uncovers the molecular complexity and plasticity of the cDC cell surface and provides a roadmap for understanding cDC activation and signaling.