Toward practical quantum embedding simulation of realistic chemical systems on near-term quantum computers

Quantum computing has recently exhibited great potential in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such as LiH and hydrogen chains of up to 12 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from the limitations of the size and the fidelity of near-term quantum hardware, the accurate simulation of large realistic molecules remains a challenge. Here, integrating an adaptive energy sorting strategy and a classical computational method—the density matrix embedding theory, which respectively reduces the circuit depth and the problem size, we present a means to circumvent the limitations and demonstrate the potential of near-term quantum computers toward solving real chemical problems. We numerically test the method for the hydrogenation reaction of C₆H₈ and the equilibrium geometry of the C₁₈ molecule, using basis sets up to cc-pVDZ (at most 144 qubits). The simulation results show accuracies comparable to those of advanced quantum chemistry methods such as coupled-cluster or even full configuration interaction, while the number of qubits required is reduced by an order of magnitude (from 144 qubits to 16 qubits for the C₁₈ molecule) compared to conventional VQE. Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.

Quantum embedding simulation greatly enhanced the capability of near-term quantum computers on realistic chemical systems and reach accuracy comparable to advanced quantum chemistry methods.     

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application

Most contemporary drug discovery projects start with a ‘hit discovery’ phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, ’virtual docking calculations’ are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel ‘ensemble docking’ calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be ’selected’ by chemicals is known as ’conformational selection’ process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.     

Ziziphus nummularia: A Comprehensive Review of Its Phytochemical Constituents and Pharmacological Properties

Ziziphus nummularia, a small bush of the Rhamnaceae family, has been widely used in traditional folk medicine, is rich in bioactive molecules, and has many reported pharmacological and therapeutic properties. Objective: To gather the current knowledge related to the medicinal characteristics of Z. nummularia. Specifically, its phytochemical contents and pharmacological activities in the treatment of various diseases such as cancer, diabetes, and cardiovascular diseases, are discussed. Methods: Major scientific literature databases, including PubMed, Scopus, ScienceDirect, SciFinder, Chemical Abstracts, Medicinal and Aromatic Plants Abstracts, Henriette’s Herbal Homepage, Dr. Duke’s Phytochemical and Ethnobotanical Databases, were searched to retrieve articles related to the review subject. General web searches using Google and Google scholar were also utilized. The search period covered articles published between 1980 and the end of October 2021.The search used the keywords ‘Ziziphus nummularia’, AND (‘phytochemical content’, ‘pharmacological properties, or activities, or effects, or roles’, ‘anti-inflammatory’, ‘anti-drought’, ‘anti-thermal’, ‘anthelmintic’, ‘antidiabetic’,’ anticancer’, ‘anticholinesterase’, ‘antimicrobial’, ‘sedative’, ‘antipyretic’, ‘analgesic’, or ‘gastrointestinal’). Results: This plant is rich in characteristic alkaloids, especially cyclopeptide alkaloids such as nummularine-M. Other phytochemicals, including flavonoids, saponins, glycosides, tannins, and phenolic compounds, are also present. These phytochemicals are responsible for the reported pharmacological properties of Z. nummularia, including anti-inflammatory, antioxidant, antimicrobial, anthelmintic, antidiabetic, anticancer, analgesic, and gastrointestinal activities. In addition, Z. nummularia has anti-drought and anti-thermal characteristics. Conclusion: Research into the phytochemical and pharmacological properties of Z. nummularia has demonstrated that this plant is a rich source of novel bioactive compounds. So far, Z. nummularia has shown a varied pharmacological profile (antioxidant, anticancer, anti-inflammatory, and cardioprotective), warranting further research to uncover the therapeutic potential of the bioactives of this plant. Taken together, Z. nummularia may represent a new potential target for the discovery of new drug leads.     

Steric effects and quantum interference in the inelastic scattering of NO(X) + Ar

Rotationally inelastic collisions of NO(X) with Ar are investigated in unprecedented detail using state-to-state, crossed molecular beam experiments. The NO(X) molecules are selected in the Ω = 0.5, j = 0.5, f state and then oriented such that either the ‘N’ or ‘O’ end of the molecule is directed towards the incoming Ar atom. Velocity map ion imaging is then used to probe the scattered NO molecules in well-defined quantum states. We show that the fully quantum state-resolved differential steric asymmetry, which quantifies how the relative efficiency for scattering off the ‘O’ and the ‘N’ ends of the molecule varies with scattering angle, is strongly affected by quantum interference. Significant changes in both integral and differential cross sections are found depending on whether collisions occur with the N or O ends of the molecule. The results are well accounted for by rigorous quantum mechanical calculations, in contrast to both classical trajectory calculations and more simplistic models that provide, at best, an incomplete picture of the dynamics.     

Designing molecules by optimizing potentials

The astronomical number of accessible discrete chemical structures makes rational molecular design extremely challenging. We formulate the design of molecules with specific tailored properties as performing a continuous optimization in the space of electron-nuclear attraction potentials. The optimization is facilitated by using a linear combination of atomic potentials (LCAP), a general framework that creates a continuous property landscape from an otherwise unlinked set of discrete molecular-property values. A demonstration of this approach is given for the optimization of molecular electronic polarizability and hyperpolarizability. We show that the optimal structures can be determined without enumerating and separately evaluating the characteristics of the combinatorial number of possible structures, a process that would be much slower. The LCAP approach may be used with quantum or classical Hamiltonians, suggesting possible applications to drug design and new materials discovery.     

High Throughput Screening of the NatureBank ‘Marine Collection’ in a Haemonchus Bioassay Identifies Anthelmintic Activity in Extracts from a Range of Sponges from Australian Waters

Widespread resistance in parasitic nematodes to most classes of anthelmintic drugs demands the discovery and development of novel compounds with distinct mechanisms of action to complement strategic or integrated parasite control programs. Products from nature—which assume a diverse ‘chemical space’—have significant potential as a source of anthelmintic compounds. In the present study, we screened a collection of extracts (n = 7616) derived from marine invertebrates sampled from Australian waters in a high throughput bioassay for in vitro anti-parasitic activity against the barber’s pole worm (Haemonchus contortus)—an economically important parasitic nematode of livestock animals. In this high throughput screen (HTS), we identified 58 active extracts that reduced larval motility by ≥70% (at 90 h), equating to an overall ‘hit rate’ of ~0.8%. Of these 58 extracts, 16 also inhibited larval development by ≥80% (at 168 h) and/or induced ‘non-wild-type’ (abnormal) larval phenotypes with reference to ‘wild-type’ (normal) larvae not exposed to extract (negative controls). Most active extracts (54 of 58) originated from sponges, three from chordates (tunicates) and one from a coral; these extracts represented 37 distinct species/taxa of 23 families. An analysis of samples by ¹H NMR fingerprinting was utilised to dereplicate hits and to prioritise a set of 29 sponge samples for future chemical investigation. Overall, these results indicate that a range of sponge species from Australian waters represents a rich source of natural compounds with nematocidal or nematostatic properties. Our plan now is to focus on in-depth chemical investigations of the sample set prioritised herein.     

A Comparison between Different Agro-wastes and Carbon Nanotubes for Removal of Sarafloxacin from Wastewater: Kinetics and Equilibrium Studies

In the current study, eco-structured and efficient removal of the veterinary fluoroquinolone antibiotic sarafloxacin (SARA) from wastewater has been explored. The adsorptive power of four agro-wastes (AWs) derived from pistachio nutshells (PNS) and Aloe vera leaves (AV) as well as the multi-walled carbon nanotubes (MWCNTs) has been assessed. Adsorbent derived from raw pistachio nutshells (RPNS) was the most efficient among the four tested AWs (%removal ‘%R’ = 82.39%), while MWCNTs showed the best adsorptive power amongst the five adsorbents (%R = 96.20%). Plackett-Burman design (PBD) was used to optimize the adsorption process. Two responses (‘%R’ and adsorption capacity ‘q_e’) were optimized as a function of four variables (pH, adsorbent dose ‘AD’ (dose of RPNS and MWCNTs), adsorbate concentration [SARA] and contact time ‘CT’). The effect of pH was similar for both RPNS and MWCNTs. Morphological and textural characterization of the tested adsorbents was carried out using FT-IR spectroscopy, SEM and BET analyses. Conversion of waste-derived materials into carbonaceous material was investigated by Raman spectroscopy. Equilibrium studies showed that Freundlich isotherm is the most suitable isotherm to describe the adsorption of SARA onto RPNS. Kinetics’ investigation shows that the adsorption of SARA onto RPNS follows a pseudo-second order (PSO) model.     

Chemical Compositions and Antioxidant Activities of Essential Oils,and Their Combinations,Obtained from Flavedo By-Product of Seven Cultivars of Sicilian Citrus aurantium L.

In this work, seven Citrus aurantium essential oils (EOs) derived from flavedo of cultivars ‘Canaliculata’, ‘Consolei’, ‘Crispifolia’, ‘Fasciata’, ‘Foetifera’, ‘Listata’, and ‘Bizzaria’ were investigated. EOs were also combined in 1:1 (v/v) ratio to identify possible synergism or antagonism of actions. GC-MS analysis was done to investigate Eos’ phytochemical profiles. The antioxidant activity was studied by using a multi-target approach based on FRAP, DPPH, ABTS, and β-carotene bleaching tests. A great difference was observed in EOs’ phytochemical profiles. d-limonene (33.35–89.17%) was the main monoterpene hydrocarbon, and α-Pinene, β-myrcene, and β-linalool were identified in almost all samples. Among EOs, only C3 showed high quantitative and qualitative variability in its chemical composition. The chemical diversity of EOs was also demonstrated by PCA and HCA statistical analysis. Samples C2, C4, C5, C6, and C7 were statistically similar to each other, while C1 and C3 were characterized as having a different amount of other compounds and oxygenated monoterpenes, respectively, with respect to the other EOs mentioned. The global antioxidant score (GAS) revealed that among the tested EOs, C. aurantium ‘Fasciata’ EO had the highest antioxidant potential, with a GAS value of −0.47, whereas among combinations, the EO obtained by mixing ‘Canaliculata’ + ‘Bizzaria’ was the most active. Comparison by theoretical and real data on inhibitory concentration (IC₅₀) and FRAP values did not reveal any significant effect of synergism or antagonism of actions to be valid in all biological applied tests. These findings, considered together, represent an important starting point to understand which compounds are responsible for the activities and their future possible industrial application.     

Determination of the Masking Effect of the ‘Zapateria’ Defect in Flavoured Stuffed Olives Using E-Nose

Spanish-style table olives are one of the most common processed foods in the Mediterranean countries. Lack of control during fermentation can lead to one of the main defects of the olive, called ‘Zapateria’, caused by the combination of volatile fatty acids reminiscent of rotten leather. In this study, table olives altered with ‘Zapateria’ defect were stuffed with a hydrocolloid flavoured with the aroma ‘Mojo picón’ to improve consumer acceptance. Sensory analysis, determination of volatile compounds and electronic nose (E-nose) were used to evaluate the quality of the olives. The control samples had a high concentration of the defect ‘Zapateria’ and were classified in the second commercial category, while higher ‘Mojo picón’ flavour concentrations resulted in these olives being classified as ‘extra category’ (a masking effect). The main volatile compounds in olives with ‘Zapateria’ defect were cyclohexanecarboxylic acid and pentanoic acid. E-nose allowed discrimination between stuffed olives without added flavouring and olives with ‘Mojo picón’ flavouring at different concentrations. Finally, PLS regression allowed a predictive linear model to be established between E-nose and sensory analysis values. The RP2 values were 0.74 for perceived defect and 0.86 for perceived aroma. The E-nose was successfully applied for the first time to classify Spanish-style table olives with ‘Zapateria’ defect intensity and with the addition of the ‘Mojo picón’ aroma masking the defect.     

The synthesis of a chemically reactive and polymeric luminescent gel

In the past, chemically reactive polymeric interfaces have been considered to be of potential interest for developing functional materials for a wide range of practical applications. Furthermore, the rational incorporation of luminescence properties into such chemically reactive interfaces could provide a basis for extending the horizon of their prospective utility. In this report, a simple catalyst-free chemical approach is introduced to develop a chemically reactive and optically active polymeric gel. Branched-polyethyleneimine (BPEI)-derived, inherently luminescent carbon dots (BPEI-CDs) were covalently crosslinked with pentaacrylate (5Acl) through a 1,4-conjugate addition reaction under ambient conditions. The synthesized polymeric gel was milky white under visible light; however, it displayed fluorescence under UV light. Additionally, the residual acrylate groups in the synthesized fluorescent gel allowed its chemical functionality to be tailored through facile, robust 1,4-conjugate addition reactions with primary-amine-containing small molecules under ambient conditions. The chemical reactivity of the luminescent gel was further employed for a proof-of-concept demonstration of portable and parallel ‘ON’/‘OFF’ toxic chemical sensing (namely, the sensing of nitrite ions as a model analyte). First, the chemically reactive luminescent gel derived from BPEI-CDs was covalently post-modified with aniline for the selective synthesis of a diazo compound in the presence of nitrite ions. During this process, the color of the gel under visible light changed from white to yellow and, thus, the colorimetric mode of the sensor was turned ‘ON’. In parallel, the luminescence of the gel under UV light was quenched, which was denoted as the ‘OFF’ mode of the sensor. This parallel and unambiguous ‘ON’/‘OFF’ sensing of a toxic chemical (nitrite ions, with a detection limit of 3 μM) was also achieved even in presence of other relevant interfering ions and at concentrations well below the permissible limit (65 μM) set by the World Health Organization (WHO). Furthermore, this chemically reactive luminescent gel could be of potential interest in a wide range of basic and applied contexts.

An unprecedented chemically reactive and polymeric luminescent gel is developed, and this material is further employed to develop a portable and rapid sensor for a practically relevant analyte (nitrite ions) with a sensitivity of 3 μM.     

Peptide nano ‘bead-grafting’ for SDT-facilitated immune checkpoints blocking

Combination therapies based on immune checkpoint blockade (ICB) are currently the mainstay of cancer treatment, in which the synergetic delivery of multiple drugs is the essential step. Although nanoparticle drugs (NPDs) show satisfactory anticancer effects, the promotion of active co-delivery of NPDs is premature, since the processes are usually difficult to predict and control. Targeting peptide self-assemblies have been widely used as carriers for small-molecular drugs, but remain elusive for NPDs. We describe here peptide-based nano ‘bead-grafting’ for the active delivery of quantum-dot NPDs through a co-assembly method. Based on a ‘de novo’ design, we used a ‘one-bead-one-compound (OBOC)’ combinatorial chemical screening method to select a peptide RT with high affinity for the immune checkpoint CD47, which could also form biocompatible nanofibers and efficiently trap Ag₂S quantum dots along the self-assembly path. This system can combine ICB therapy and sonodynamic therapy (SDT) to effectively inhibit tumor growth. Moreover, the tumor antigen produced by SDT can activate the adaptive immune system, which enhances the anti-tumor immune response of the ICB and shows efficient inhibition of both primary and distant tumors. This study provides a new strategy for the active control and delivery of NPDs and a new option for ICB therapy with immune checkpoints that are highly susceptible to systemic side effects.

Peptide-fibril-captured QDs form nano ‘bead-grafting’, which inhibit both the primary and distant tumors through the combination with immune checkpoint blockade (ICB) and sonodynamic therapy (SDT).     

Reuse of Food Waste: The Chemical Composition and Health Properties of Pomelo (Citrus maxima) Cultivar Essential Oils

The aim of the present study is to investigate the chemical profile, antioxidant activity, carbohydrate-hydrolysing enzyme inhibition, and hypolipidemic effect of essential oils (EOs) extracted from Sicilian Citrus maxima (pomelo) flavedo. Using gas-chromatography-mass spectrometry analysis (GC-MS) we analysed the Eos of five cultivars of C. maxima, namely, ‘Chadock’, ‘Maxima’, ‘Pyriformis’, ‘Terracciani’, and ‘Todarii’, and their blends. The antioxidant activity was performed by using a multi-target approach using 2,2′-Azino-Bis-3-Ethylbenzothiazoline-6-Sulfonic acid (ABTS), 2,2-Diphenyl-1-picrylhydrazyl (DPPH), ferric reducing ability power (FRAP), and β-carotene bleaching tests. The α-amylase, α-glucosidase, and lipase-inhibitory activities were also assessed. GC-MS analyses revealed D-limonene as the main monoterpene hydrocarbon in all cultivars, albeit with different percentages in the range of 21.72–71.13%. A good content of oxygenated monoterpenes was detected for all cultivars, especially for ‘Todarii’. The analysis of the principal components (PCA), and related clusters (HCA), was performed to find chemo-diversity among the analysed samples. EOs from ‘Chadock’ and ‘Maxima’ were statistically similar to each other, and they differed from P3 in the smaller amount of sesquiterpene hydrocarbons, while the oils from ‘Terracciani’ and ‘Todarii’ were found to be chemically and statistically different. ‘Chadock’ EO was the most active to scavenge radicals (IC₅₀ values of 22.24 and 27.23 µg/mL in ABTS and DPPH tests, respectively). ‘Terracciani’ EO was the most active against both lipase and α-amylase, whereas the blends obtained by the combination (1:1 v/v) of C. maxima ‘Maxima’ + ‘Todarii’ were the most active against α-glucosidase. Generally, the blends did not exert a unique behaviour in potentiating or reducing the bioactivity of the pomelo EOs.     

Alkaloids Used as Medicines: Structural Phytochemistry Meets Biodiversity—An Update and Forward Look

Selecting candidates for drug developments using computational design and empirical rules has resulted in a broad discussion about their success. In a previous study, we had shown that a species’ abundance [as expressed by the GBIF (Global Biodiversity Information Facility)] dataset is a core determinant for the development of a natural product into a medicine. Our overarching aim is to understand the unique requirements for natural product-based drug development. Web of Science was queried for research on alkaloids in combination with plant systematics/taxonomy. All alkaloids containing species demonstrated an average increase of 8.66 in GBIF occurrences between 2014 and 2020. Medicinal Species with alkaloids show higher abundance compared to non-medicinal alkaloids, often linked also to cultivation. Alkaloids with high biodiversity are often simple alkaloids found in multiple species with the presence of ’driver species‘ and are more likely to be included in early-stage drug development compared to ‘rare’ alkaloids. Similarly, the success of an alkaloid containing species as a food supplement (‘botanical’) is linked to its abundance. GBIF is a useful tool for assessing the druggability of a compound from a certain source species. The success of any development programme from natural sources must take sustainable sourcing into account right from the start.     

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds.     

Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery

Fragment-based drug discovery (FBDD) is a powerful strategy for the identification of new bioactive molecules. FBDD relies on fragment libraries, generally of modest size, but of high chemical diversity. Although good chemical diversity in FBDD libraries has been achieved in many respects, achieving shape diversity – particularly fragments with three-dimensional (3D) structures – has remained challenging. A recent analysis revealed that >75% of all conventional, organic fragments are predominantly 1D or 2D in shape. However, 3D fragments are desired because molecular shape is one of the most important factors in molecular recognition by a biomolecule. To address this challenge, the use of inert metal complexes, so-called ‘metallofragments’ (mFs), to construct a 3D fragment library is introduced. A modest library of 71 compounds has been prepared with rich shape diversity as gauged by normalized principle moment of inertia (PMI) analysis. PMI analysis shows that these metallofragments occupy an area of fragment space that is unique and highly underrepresented when compared to conventional organic fragment libraries that are comprised of orders of magnitude more molecules. The potential value of this metallofragment library is demonstrated by screening against several different types of proteins, including an antiviral, an antibacterial, and an anticancer target. The suitability of the metallofragments for future hit-to-lead development was validated through the determination of IC₅₀ and thermal shift values for select fragments against several proteins. These findings demonstrate the utility of metallofragment libraries as a means of accessing underutilized 3D fragment space for FBDD against a variety of protein targets.

Fragment-based drug discovery (FBDD) using 3-dimensional metallofragments is a new strategy for the identification of bioactive molecules.     

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.     

Ethnobotanical Uses,Phytochemistry, Toxicology,and Pharmacological Properties of Euphorbia neriifolia Linn. against Infectious Diseases: A Comprehensive Review

Medicinal plants have considerable potential as antimicrobial agents due to the presence of secondary metabolites. This comprehensive overview aims to summarize the classification, morphology, and ethnobotanical uses of Euphorbia neriifolia L. and its derived phytochemicals with the recent updates on the pharmacological properties against emerging infectious diseases, mainly focusing on bacterial, viral, fungal, and parasitic infections. The data were collected from electronic databases, including Google Scholar, PubMed, Semantic Scholar, ScienceDirect, and SpringerLink by utilizing several keywords like ‘Euphorbia neriifolia’, ‘phytoconstituents’, ‘traditional uses’, ‘ethnopharmacological uses’, ‘infectious diseases’, ‘molecular mechanisms’, ‘COVID-19’, ‘bacterial infection’, ‘viral infection’, etc. The results related to the antimicrobial actions of these plant extracts and their derived phytochemicals were carefully reviewed and summarized. Euphol, monohydroxy triterpene, nerifoliol, taraxerol, β-amyrin, glut-5-(10)-en-1-one, neriifolione, and cycloartenol are the leading secondary metabolites reported in phytochemical investigations. These chemicals have been shown to possess a wide spectrum of biological functions. Different extracts of E. neriifolia exerted antimicrobial activities against various pathogens to different extents. Moreover, major phytoconstituents present in this plant, such as quercetin, rutin, friedelin, taraxerol, epitaraxerol, taraxeryl acetate, 3β-friedelanol, 3β-acetoxy friedelane, 3β-simiarenol, afzelin, 24-methylene cycloarenol, ingenol triacetate, and β-amyrin, showed significant antimicrobial activities against various pathogens that are responsible for emerging infectious diseases. This plant and the phytoconstituents, such as flavonoids, monoterpenoids, diterpenoids, triterpenoids, and alkaloids, have been found to have significant antimicrobial properties. The current evidence suggests that they might be used as leads in the development of more effective drugs to treat emerging infectious diseases, including the 2019 coronavirus disease (COVID-19).     

Chemical Diversity and Potential Target Network of Woody Peony Flower Essential Oil from Eleven Representative Cultivars (Paeonia × suffruticosa Andr.)

Woody peony (Paeonia × suffruticosa Andr.) has many cultivars with genetic variances. The flower essential oil is valued in cosmetics and fragrances. This study was to investigate the chemical diversity of essential oils of eleven representative cultivars and their potential target network. Hydro-distillation afforded yields of 0.11–0.25%. Essential oils were analyzed by GC-MS and GC-FID which identified 105 compounds. Three clusters emerged from multivariate analysis, representative of phloroglucinol trimethyl ether (‘Caihui’), citronellol (‘Jingyu’, ‘Zhaofen’ and ‘Baiyuan Zhenghui’) and mixed (the rest of the cultivars) chemotypes. ‘Zhaofen’ and ‘Jingyu’ also exhibited low levels of other rose-related compounds. The main components were subjected to a target network approach. Drug-likeness screening gave 20 compounds with predictive blood–brain barrier permeation. Compound target network identified six key compounds, namely nerol, citronellol, geraniol, geranic acid, cis-3-hexen-1-ol and 1-hexanol. Top enriched terms in GO, KEGG and DisGeNET were mostly related to the central nervous system (CNS). Protein—protein interactions revealed a core network of 14 targets, 11 of which were CNS-related (targets for antidepressants, analgesics, antipsychotics, anti-Alzheimer’s and anti-Parkinson’s agents). This work provides useful information on the production of woody peony essential oils with specific chemotypes and reveals their potential importance in aromatherapy for alternative treatment of CNS disorders.     

Controlled synthesis of Bi- and tri-nuclear Cu-oxo nanoclusters on metal–organic frameworks and the structure–reactivity correlations

Precisely tuning the nuclearity of supported metal nanoclusters is pivotal for designing more superior catalytic systems, but it remains practically challenging. By utilising the chemical and molecular specificity of UiO-66-NH₂ (a Zr-based metal–organic framework), we report the controlled synthesis of supported bi- and trinuclear Cu-oxo nanoclusters on the Zr₆O₄ nodal centres of UiO-66-NH₂. We revealed the interplay between the surface structures of the active sites, adsorption configurations, catalytic reactivities and associated reaction energetics of structurally related Cu-based ‘single atoms’ and bi- and trinuclear species over our model photocatalytic formic acid reforming reaction. This work will offer practical insight that fills the critical knowledge gap in the design and engineering of new-generation atomic and nanocluster catalysts. The precise control of the structure and surface sensitivities is important as it can effectively lead to more reactive and selective catalytic systems. The supported bi- and trinuclear Cu-oxo nanoclusters exhibit notably different catalytic properties compared with the mononuclear ‘Cu₁’ analogue, which provides critical insight for the engineering of more superior catalytic systems.

The controlled synthesis of novel bi- and trinuclear Cu-oxo nanoclusters supported on UiO-66-NH₂ that show notably different catalytic properties in the photocatalytic formic acid decomposition reaction is reported.