Desired Molecules Research Articles

Twinned Metal-Organic Framework Nanoplates for Hydrocarbon Separation Membranes.

Filler morphology control is critical for enhancing the gas separation performance of mixed matrix membranes (MMMs). A vertical transport channel using the crystal twinning phenomenon is designed on the zeolitic imidazolate framework (ZIF) nanoplate. Twinned ZIF-8 (TZIF-8) nanoplate is prepared by controlling the shape of ZIF-L from nanosheet to twinned flake, followed by conversion into the ZIF-8 phase. With the addition of TZIF-8 in 6FDA-DAM polymer, propylene/propane selectivity is dramatically enhanced, showing propylene permeability of 40 Barrer and propylene/propane selectivity of 82. The separation performance surpasses the performance of MMMs reported so far, and the selectivity is comparable to that of polycrystalline ZIF-8 membranes. A transport mechanism study using mathematical models implies that the percolated twin fillers create rapid and selective gas channels for desired molecules and substantial tortuous pathways for undesired molecules.

Correlating Chemical Structure and Charge Carrier Dynamics in Phenanthrocarbazole‐Based Hole Transporting Materials for Efficient Perovskite Solar Cells

ABSTRACTPolymeric hole transport materials (HTMs) have emerged because of their potential to produce dopant‐free, efficient, and stable perovskite solar cells (PSCs). Therefore, we engineered 10 novel donor materials (SMH1–SMH10) containing phenanthrocarbazole‐based polymeric structures for organic and PSCs. These molecules underwent bridging‐core modifications using different spacers, such as furan (N1), pyrrole (N2), benzene (N3), pyrazine (N4), dioxane (N5), isoxazole (N6), isoindole (N7), indolizine (N8), double bond (N9), and pyrimidine (N10), in comparison to reference molecule R. The study examined the structure–property relationship and the impact of these modifications on the optical, photovoltaic, photophysical, and optoelectronic characteristics of the newly designed SMH1–SMH10 series. Density functional theory (DFT) and time‐dependent density functional theory (TD‐DFT) calculations were conducted to analyze frontier molecular orbitals, density of states, reorganization energies, open‐circuit voltage, transition density matrix, and charge transfer processes. Results show that the newly designed molecules (SMH1–SMH10) exhibited superior optoelectronics characteristics compared to the R molecule. Among these, SMH4 is the most promising candidate, with a small band gap (2.79 eV), low electron and hole mobility (λe 0.0028 eV, λh 0.0020 eV), lower binding energy (Eb 0.58 eV), high λmax values (656.42 nm in gas, 573.34 nm in chlorobenzene), and a high Voc of 1.30 V. Therefore, this study demonstrated that bridging‐core modifications offer a simple and effective strategy for designing desirable characteristics molecules for photovoltaic applications.

Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Advances and challenges in the fractionation of edible oils and fats through supercritical fluid processing.

Petrochemical solvents are widely used for the extraction and fractionation of biomolecules from edible oils and fats at an industrial scale. However, owing to its safety concerns, toxicity, price fluctuations, and sustainability, alternative solvents and technologies have been actively explored in recent years. Technologies, such as ultrasound and microwave-assisted extraction, supercritical carbon dioxide extraction, supercritical fluid fractionation, and sub-critical water extraction, and solvents, like ionic liquids and deep eutectic solvents, are reported for extraction and fractionation of biomolecules. Among them, supercritical carbon dioxide extraction and fractionation are some of the most promising green technologies with the potential to replace petrochemical-based conventional techniques. The addition of cosolvents, such as water, ethanol, and acetone, improves the extraction of amphiphilic and polar compounds from edible oils and fats. Supercritical fluid processing has diverse applications, including concentration of solutes, selective separation of desired molecules, and separation of undesirable compounds from the feed material. Temperature, pressure, particle size, porosity, flow rate, solvent-to-feed ratio, density, viscosity, diffusivity, solubility, partition coefficient, and separation factor are the fundamental factors governing the extraction and fractionation of desired biomolecules from lipids. Supercritical fluids stand alone compared to conventional fluids, because of their tunable solvent properties. Overall, it is to be noted that supercritical fluid-based methods have lots of scope to replace conventional solvent-based methods and progress toward the creation of sustainable food-processing techniques. This review critically evaluates the parameters responsible for the extraction and fractionation of biomolecules from edible oils and fats under supercritical conditions.

Practical and regioselective halonitrooxylation of olefins to access β-halonitrates

Organic nitrates, as effective donors of the signaling molecule nitric oxide, are widely applied in the pharmaceutical industry. However, practical and efficient methods for accessing organic nitrates are still scarce, and achieving high regiocontrol in unactivated alkene difunctionalization remains challenging. Here we present a simple and practical method for highly regioselective halonitrooxylation of unactivated alkenes. The approach utilizes TMSX (X: Cl, Br, or I) and oxybis(aryl-λ3-iodanediyl) dinitrates (OAIDN) as sources of halogen and nitrooxy groups, with 0.5 mol % FeCl3 as the catalyst. Remarkably, high regioselectivity in the halonitrooxylation of aromatic alkenes can be achieved even without any catalyst. This protocol features easy scalability and excellent functional group compatibility, providing a range of β-halonitrates (127 examples, up to 99% yield, up to >20:1 rr). Notably, 2-iodoethyl nitrate, a potent synthon derived from ethylene, reacts smoothly with a variety of functional units to incorporate the nitrooxy group into the desired molecules.

Quantum-Chemistry Enabled AI for Materials Discovery for Energy Storage

A priori and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘Let’s Start from Computing’ is an optimal approach to initialize modern day R&D processes. In energy storage, beyond lithium-ion (BLI) research has the potential to revolutionize consumer electronics including portable and stationary power, transportation, and grid energy storage sectors. Multi-valent (Mg, Ca, Zn) energy storage or economically viable monovalent (Na, K) batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. All these R&D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization. Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide accurate insights to accelerate discovery of optimalelectrolytes, electrodes, and membranes for BLI systems to reduce the cost. Thus, coupled with AI and multi-scale simulations techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, and reactivity) to down-select optimal materials or material combinations. In this presentation, I will describe some of our recent efforts in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for flow battery technology. This concept can be utilized for design of experiments using autonomous experimentation. Additionally, I will describe some of our quantum chemistry-informed molecular property predictions redoxmers and liquid organic hydrogen carriers. In addition to molecules, I will present. A data driven approach to study longer time scale diffusion of ions for multivalent battery concepts. Figure 1

Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.

Since the rise of generative AI models, many goal-directed molecule generators have been proposed as tools for discovering novel drug candidates. However, molecule generators often produce highly similar molecules and tend to overemphasize conformity to an imperfect scoring function rather than capturing the true underlying properties sought. We rectify these two shortcomings by offering diversity-based evaluations using the #Circles metric and considering constraints on scoring function calls or computation time. Our findings highlight the superior performance of SMILES-based autoregressive models in generating diverse sets of desired molecules compared to graph-based models or genetic algorithms.

Random Forests Regression and Rescoring Strategy for Identifying Inhibitors of Ubiquitin Specific Protease-7

Ubiquitin-specific protease-7 (USP7) is a potential target for cancer therapy. In this work, the evaluation and design of USP7 inhibitors are conducted employing a comprehensive computational approach. Initially, a large database of compounds undergoes preliminary screening via similarity and pharmacophore analyses. Subsequently, a secondary selection process is executed based on both quantitative structure–activity relationship modeling and molecular docking. The final selection is refined through rescoring treatments via consensus scores and MM/GBSA binding free energy. A total of 138 experimental USP7 inhibitors were collected for the construction of random forest regression models using four different feature selection methods. 17 top experimentally active inhibitors were used as models in similarity searching, and eight representative USP7-ligand binding models were used in pharmacophore screening, performed over a database of 14 million compounds. The rescoring approach applied in this study offers an alternative and beneficial method for the ranking and selection of desired molecules. Based on the molecular scaffolds of the highly active inhibitors, some new derivatives have been designed and received high predicted scores for inhibition.

A Combined Experimental, DFT and Molecular Dynamics Simulation Study of Binary Mixture of Ethylbenzene and Aniline

AbstractDensity and viscosity values of the binary mixture of ethylbenzene and aniline have been experimentally measured in different mole fractions at temperatures of 293.15 to 313.15 K and at an absolute pressure of 86.7 kPa. Based on the obtained experimental data, the excess molar volume and viscosity deviations have been reported, which can interpret the interactions in this mixture. The obtained data were fitted with the Redlich‐Kister equation. In addition, the viscosity results are discussed using the equation proposed by Gruenberg‐Nissan. After experimental studies, in order to investigate the interactions of these two components and their mixture, quantum mechanical calculations and molecular dynamics simulation have been used. In this way, the structures of the desired molecules have been optimized. Then Atoms in Molecules (AIM) and frontier molecular orbital (FMO) techniques have been used for a more detailed investigation. Finally, by plotting radial distribution functions (RDFs) obtained from molecular dynamics, these interactions have been investigated in a more comprehensive way. In addition, the simulated densities at 298.15 K have been reported. At the end, clear pictures of the interactions of molecules in mixtures were obtained by comparing the achievements of experimental measurements, quantum chemical and calculations MD simulation.

Increased Selectivity in Photolytic Activation of Nanoassemblies Compared to Thermal Activation in On-Surface Ullmann Coupling.

On-surface synthesis is a powerful method that has emerged recently to fabricate a large variety of atomically precise nanomaterials on surfaces based on polymerization. It is very successful for thermally activated reactions within the framework of heterogeneous catalysis. As a result, it often lacks selectivity. We propose to use selective activation of specific bonds as a crucial ingredient to synthesize desired molecules with high selectivity. In this approach, thermally nonaccessible products are expected to arise in photolytically activated on-surface reactions with high selectivity. We demonstrate for assembled 2,2'-dibromo biphenyl clusters on Cu(111) that the thermal and photolytic activations yield distinctly different products, combining submolecular resolution of individual product molecules in real-space imaging by scanning tunneling microscopy with chemical identification in X-ray photoelectron spectroscopy and supported by ab initio calculations. The photolytically activated Ullmann coupling of 2,2'-dibromo biphenyl is highly selective, with only one identified product. It starkly contrasts the thermal reaction, which yields various products because alternate pathways are activated at the reaction temperature. Our study extends on-surface synthesis to a directed formation of thermally inaccessible products by direct bond activation. It promises tailored reactions of nanomaterials within the framework of on-surface synthesis based on the photolytic activation of specific bonds.

DNA-functionalized hyaluronic acid bioink in cartilage engineering: a perspective

Degenerative osteoarthritis, a common sequela of articular cartilage defect, significantly impacts the quality of life of millions of individuals worldwide. Three-dimensional (3D) bioprinting has emerged as an advanced tissue engineering strategy, offering precise spatial arrangements of cells, hydrogels, and bioactive cues. Hyaluronic acid (HA) is a crucial component of bioink designed for fabricating cartilage tissue. However, creating a bioink that closely mimics the cartilaginous extracellular matrix (ECM) still remains a challenge. HA hydrogels have limitations in recapitulating tunable mechanical properties, stimuli responsiveness, and flexibility in ligands’ adhesion akin to those of native tissues. In recent years, DNA has emerged as a smart biomaterial that endows hydrogels with tunable properties and allows for precise structural customization of the hydrogels due to its unique programmability. Integrating reversible DNA linkages, reconfigurable DNA architectures, DNA plasmid, and targeted DNA aptamers into HA hydrogels allows them to respond to the extracellular environment and express desired molecules, making them ideal artificial ECMs for 3D bioprinting of cartilage tissue. This review targets this challenge by highlighting the characteristics of DNA moieties designed as reversible crosslinkers, responsive units, and adhesion ligands to functionalize HA hydrogels. Furthermore, we offer perspectives on how DNA-functionalized HA hydrogels can be harnessed to create dynamic and biomimetic bioink capable of recapitulating the more complex functions required for cartilage tissue engineering.

Goal-directed molecule generation with fine-tuning by policy gradient

Graph-structured drug molecule representations often struggle to generate molecules by particular intentions, which results in generated molecules without pharmacological properties. To address the problem, we propose a de novo molecular generation method that utilizes the policy gradient algorithm of reinforcement learning to fine-tune the molecular graph generation model. The training process of the method is divided into the pre-training stage and the fine-tuning stage. During the pre-training stage, it uses graph neural networks and multilayer perceptrons to train a molecule graph generation model. During the fine-tuning stage, scoring functions are devised for multiple goal-directed generation tasks, and subsequently, the policy loss function is formulated based on the reward shaping mechanism. A value network is designed to calculate the value of taking an action based on the current graph state during agent sampling to guide policy updates. To mitigate the issue of molecular uniqueness decline during the learning process, we dynamically adjust the weights of the two learning processes in the policy loss function, aiming to generate desirable molecules with high probability and reduce the descent of uniqueness. The experiments showed that after fine-tuning, the generative model exhibits a higher probability of generating molecules with desired properties compared to other models. Furthermore, our method effectively mitigates the issue of molecular uniqueness declining during the learning process when compared to alternative fine-tuning methods.

Correlations in Charged Multipore Systems: Implications for Enhancing Selectivity and Permeability in Nanoporous Membranes.

Nanoporous membranes have emerged as powerful tools for diverse applications, including gas separation and water desalination. Achieving high permeability for desired molecules alongside exceptional rejection of other species presents a significant design challenge. One potential strategy involves optimizing the chemistry and geometry of isolated nanopores to enhance permeability and selectivity while maximizing their density within a membrane. However, the impact of the pore proximity on membrane performance remains an open question. Through path sampling simulations of model graphitic membranes with multiple subnanometer pores, we reveal that nanoscale proximity between pores detrimentally affects water permeability and salt rejection. Specifically, counterion transport is decelerated, while co-ion transport is accelerated, due to direct interactions among water molecules, salt ions, and the dipoles within neighboring pores. Notably, the observed ionic transport time scales significantly deviate from established theories such as the access resistance model but are well explained using the simple phenomenological model that we develop in this work. We use this model to prescreen and optimize pore arrangements that elicit minimal correlations at a target pore density. These findings deepen our understanding of multipore systems, informing the rational design of nanoporous membranes for enhanced separation processes such as water desalination. They also shed light on the physiology of biological cells that employ ion channel proteins to modulate ion transport and reversal potentials.

FDA-approved drugs containing dimethylamine pharmacophore: a review of the last 50 years.

Dimethylamine (DMA) derivatives represent a promising class of compounds with significant potential in the field of medicinal chemistry. DMA derivatives exhibit a diverse range of pharmacological activities, including antimicrobial, antihistaminic, anticancer, and analgesic properties. Their unique chemical structure allows for the modulation of various biological targets, making them valuable candidates for the treatment of numerous diseases. Synthetic strategies for the preparation of DMA derivatives vary depending on the desired biological activity and target molecule. Common synthetic routes involve the modification of the DMA scaffold through functional group manipulation, scaffold hopping, or combinatorial chemistry approaches. Therapeutically, DMA derivatives have shown promise in the treatment of infectious diseases, especially bacterial infections. Additionally, by focusing on particular biochemical pathways involved in tumor growth and metastasis, DMA-based drugs have shown anticancer activity. In addition to their direct pharmacological effects, DMA derivatives can serve as valuable tools in drug delivery systems, prodrug design, and molecular imaging techniques, enhancing their utility in medicinal chemistry research. Overall, DMA derivatives represent a versatile class of compounds with immense potential in medicinal chemistry. Further research and development efforts are warranted to explore their full therapeutic capabilities and optimize their clinical utility in the treatment of various diseases. This article outlines the pharmacological properties, synthetic strategies, and therapeutic applications of DMA derivatives of FDA approved drugs, highlighting their importance in drug discovery and development.

N4-Allylcytidine: a new nucleoside analogue for RNA labelling and chemical sequencing.

RNA labelling has become indispensable in studying RNA biology. Nucleoside analogues with a chemical sequencing power represent desirable RNA labelling molecules because precise labelling information at base resolution can be obtained. Here, we report a new nucleoside analogue, N4-allylcytidine (a4C), which is able to tag RNA through both in vitro and in vivo pathways and further specifically reacts with iodine to form 3, N4-cyclized cytidine (cyc-C) in a catalyst-free, fast and complete manner. Full spectroscopic characterization concluded that cyc-C consisted of paired diastereoisomers with opposite chiral carbon centers in the fused 3, N4-five-membered ring. During RNA reverse transcription into complementary DNA, cyc-C induces base misincorporation due to the disruption of canonical hydrogen bonding by the cyclized structure and thus can be accurately identified by sequencing at single base resolution. With the chemical sequencing rationale of a4C, successful applications have been performed including pinpointing N4-methylcytidine methyltransferases' substrate modification sites, metabolically labelling mammalian cellular RNAs, and mapping active cellular RNA polymerase locations with the chromatin run-on RNA sequencing technique. Collectively, our work demonstrates that a4C is a promising molecule for RNA labelling and chemical sequencing and expands the toolkit for studying sophisticated RNA biology.

Click synthesis of dendronized malonates for the preparation of amphiphilic dendro[60]fullerenes.

Fullerene C60 and its malonate derivatives, produced via the Bingel-Hirsch reaction, have displayed promising properties against various diseases. These molecules have great therapeutic potential, but their broad use has been limited due to poor aqueous solubility and toxicity caused by accumulation. In this study, we synthesized new malonates and malonamides attached to first- and second-generation polyester dendrons using click chemistry (CuAAC). These dendrons were then linked at C60 through the Bingel-Hirsch reaction, resulting in an amphiphilic system that retains the hydrophobic nature of C60. The dendronized malonate derivatives showed good reaction yields for the Bingel-Hirsch mono-adducts and were easier to work with than the corresponding malonamides. However, the malonamide derivatives, which were obtained through a multistep reaction sequence, showed moderate yields in the Bingel-Hirsch reaction. Surprisingly, removing acetonide protecting groups from dendritic architectures was more challenging than anticipated, likely due to product decomposition. Only the corresponding free malonate derivatives 25 and 26 were obtained, but in a low yield due to decomposition under the reaction conditions. Meanwhile, it was not possible to obtain the corresponding malonamide derivatives 27 and 28. Currently, efforts are being made to improve the production of the desired molecules and to design new synthesis routes that allow direct access to the desired poly-hydroxylated derivatives. These derivatives will be evaluated as multitarget ligands against Alzheimer's disease, through their use as inhibitors of amyloid β-peptide aggregation, acetylcholinesterase modulators, and antioxidants.

NRC-VABS: Normalized Reparameterized Conditional Variational Autoencoder with applied beam search in latent space for drug molecule design

Designing an optimal and desired drug molecule structure is a challenging problem. Most of the existing solutions/representations reported in the literature for this problem are complex and time consuming. This is due to larger datasets with longer training periods and long learning dependencies. Deep learning’s generative model can be used to enable chemical modelling to generate molecules without explicit complex molecular rules. However, Deep Learning models (LSTM based VAE) suffer from posterior collapse, larger vocabulary of datasets and sub-optimal latent space searching mechanisms. Motivated by this, we propose a recently researched idea of Normalized Reparameterized conditional Variational Autoencoder with applied beam search in latent space (NRC-VABS). The resulting model with normalized vocabulary, conditionally augmented dataset and revised/reparameterized loss function addresses posterior collapse and constructs continuous and consistent latent space for exploitation by beam search during generation stages. The conditions/properties of desirable molecules are specified through a condition vector and is used while training as well as during generation of drug molecules. Beam search is coined on improved normalized SMILES representation. The idea entails by creating samples with beam search and filtering them depending on their condition and identifying the optimal molecules with desired properties. Normalization also improves the information and reduces complexity in latent space. To address the diversity of the generated molecules, a tunable parameter (D) is also used. Various performance evaluation metrics, such as validity, uniqueness, novelty, accuracy, and Frechet ChemNet Distance are used to evaluate the NRC-VABS on benchmark data sets such as GDB13, MOSES and subset of 250k ZINC molecules. The performance of the NRC-VABS is compared with state-of-the-art peer techniques. NRC-VABS generates molecules at validity range from 92% to 84%, Accuracy 89% to 97% at varied level of diversities (D = 1, D = 2 and D = 3). An application of the proposal in terms interpolation and controlling other (2 of 3) properties by varying one (1 of 3) property at a time. Generating only target molecules with desired properties and maintaining diversity improves novel molecules while greatly reducing time complexity as only novel and desired molecules can be generated.

Evolution of surface functional groups and aromatic ring degradation upon treatment of polystyrene with hydroxyl radicals

The surface properties of hydrocarbon polymers are inadequate for numerous applications. Hence, they require alteration via functionalisation with desired functional groups. Hydroxyl groups are often preferred, since they enable appropriate polarity for the irreversible grafting of desired molecules. In this study, the surface kinetics resulting from the treatment of polystyrene with hydroxyl (OH) radicals from the gas phase was fundamentally investigated through a precisely-designed experiment. Polystyrene samples were exposed to various known fluences of OH radicals, and the evolution of surface functional groups versus the OH fluence was monitored using high-resolution X-ray photoelectron spectroscopy (XPS). The fluences of OH radicals varied between 1 × 1018 and 4 × 1023 m−2 in the process of finding a threshold fluence for the formation of specific groups. The surface concentration of carbonyl (C=O) groups could be measured using XPS at a fluence of approximately 5 × 1020 m−2. The C=O groups became measurable at a fluence of approximately 1.5 × 1021 m−2, and carboxyl (COOH)/ester groups at approximately 4 × 1021 m−2. As deduced from the XPS, a concentration of C=O groups at approximately 5 % occurred before the degradation of the aromatic ring. The formation of other oxygen-functional groups required opening of the aromatic ring. The results have been explained using a two-step process, considering available theories vis-a-vis initial stages in the functionalisation of PS with polar functional groups.

P-TSA-Promoted Efficient Synthesis of Some New Thiophene Hybridized Thiadiazolyl Schiff Bases as Antibacterial Agents

A new series of thiophene hybridized thiadiazolyl Schiff bases was designed and synthesized employing FeCl3-mediated cyclization of thiosemicarbazoneinto thiadiazoles and their subsequent Schiff bases formation using p-TSA in benzene. To understand the interaction of the proposed compounds with β-lactamase (Protein Data Bank [PDB] ID: 3UDI), a molecular docking was performed. All the compounds demonstrated an optimal binding affinity with β-lactamase (−8.17 to −9.75 kcal/mol) and showed crucial hydrogen bonds and π–π interaction with the leading amino acids Arg298, Ala300, and Val391 located at the active site of β-lactamase. The in vitro antibacterial activity of the desired molecules was conducted against few gram-positive and Gram-negative bacterial strains using amoxicillinas reference drug. The compound having p-hydroxyphenyl substituent (3c) was found to be potentially effective to inhibit P. aeruginosa and E. coli with MIC value 7.5 μg/mL and 9.0 μg/mL, respectively, whereas other compounds exhibited moderate to good activity. Altogether, the primary in-vitro screening of newly synthesized thiophene hybridized thiadiazole Schiff bases opens a new venture towards the development of promising alternatives of β-lactamase inhibitors as anti-bacterial agents.

Electrochemical Sulphenylation/Cyclization of Quinones: A Rapid, Green, and Efficient Access to Cytotoxic Compounds

AbstractUndivided electrochemical cells enable economical preparation of sulphur‐containing naphthoquinones through electrochemical sulphenylation of quinoidal compounds. The environmentally friendly and efficient protocol eliminates the use of chemical oxidants and facilitates the synthesis of the desired molecules. This approach offers an efficient and versatile method to synthesize quinones that exhibit cytotoxicity against cancer cell lines.