Quantitative Structure-activity Relationship Analysis Research Articles

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP-deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.

Comprehensive understanding of fluoroquinolone degradation via MPUV/PAA process: Radical chemistry, matrix effects, degradation pathways, and toxicity.

The wide occurrence of fluoroquinolones (FQs) in aquatic environments has aroused increasing concern about their potential adverse effects on human health. In this study, an emerging advanced oxidation process, i.e., the Medium-Pressure Ultraviolet/Peracetic Acid (MPUV/PAA) process, was used to degrade FQs (e.g., levofloxacin (LEV), norfloxacin, and ciprofloxacin). Compared with the MPUV process alone and the PAA process alone, the MPUV/PAA process significantly promoted degradation of FQs due to the considerable contribution of reactive radicals. Probe experiments revealed that PAA-specific organic radicals (e.g., CH3C(O)O• and CH3C(O)OO•) were the major radicals responsible for FQ elimination. Rapid degradation of FQs via the MPUV/PAA process was achieved within a wide range of pH values (5-9) by selecting LEV as the target compound, and higher pH values were more favorable for the reaction. The slight impacts of Cl- and CO32-/HCO3- on LEV removal were observed. The transformation products and pathways of LEV were identified, and nearly all of the transformation pathways occurred on the piperazine ring. Based on Quantitative Structure-Activity Relationship (QSAR) analysis, most of the products had lower toxicities than LEV. Overall, these findings improve our understanding and application of the MPUV/PAA process for degrading emerging contaminants in (waste)water treatment.

In-silico studies of inhibitory compounds against protease enzymes of SARS-CoV-2.

In December 2019, a COVID-19 outbreak caused by SARS-CoV-2 raised worldwide health concerns. In this case, molecular docking and drug repurposing computational approaches were engaged to check the efficiency of plant-based inhibitory compounds against SARS-CoV-2 main protease enzyme and papain-like protease enzyme. Twenty phytochemical inhibitory compounds were collected. Then these compounds were screened based on Lipinski's rule. As a result of this screening eleven compounds were further selected. Quantitative structure-activity relationships analysis was done before molecular docking to check especially the antiviral activity of inhibitory compounds. Docking validation of these compounds was checked by using online server Database of Useful Decoys: Enhanced. Binding affinity value, and pharmacokinetic properties of Aloin compound indicated that it can be used against main protease enzyme of SARS-CoV-2. So, it makes it a promising compound to follow further in cell and biochemical-based assays to explore its potential use against COVID-19.

QSAR Analysis, Molecular Docking and ADME Studies of Thiobarbituric Acid Derivatives as Thymidine Phosphorylase Inhibitors: A Rational Approach to Anticancer Drug Design by in silico Modelling

Background: Thymidine Phosphorylase (TP) is an imperative target for cancer researchers. In the current research, quantitative structure-activity relationship (QSAR) models were demonstrated to identify new TP inhibitors. Objective: The main objective is to perform a QSAR study on a series of 19 derivatives of thiobarbituric acid and new molecules designed and dock to check potency and efficacy for anticancer activity. Methods: Multiple linear regression analysis (MLR) was used to establish a two-dimensional quantitative structure-activity relationship (2D-QSAR) with regression coefficient values of 0.9781, 0.9513, and 0.9819 for the training set (r2), leave-one-out (LOO) dependent internal regression (q2), and external test set regression (r2 _pred), respectively. Three-dimensional quantitative structure-activity relationship (3DQSAR) model, obtained by using the simulated annealing k nearest neighbour (SA-KNN) method (q2 = 0.7880). Newly designed molecules were subjected to docking studies with 7-deazaxanthine taken as standard. Results: Molecular modelling, structure-based drug design and docking study analysis were performed. The new chemical entities (NCE’s) designed, docked towards targeted receptor and show good results as compared to the standard 7-deazaxanthine. It was found that these molecules bind similar amino acid pocket regions as that of standard. Molecules bind at the active site of TP enzyme involving H bond interactions with shorter distances showed greater affinity. At last, the oral bioavailability and toxic effect were evaluated as absorption, distribution, metabolism, and elimination (ADME) studies by computational means of the Qikprop tool of Schrodinger. Conclusion: One of the most successful and fast-increasing methodologies is molecular modelling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. QSAR and docking study was performed, and most of the molecules have shown good dock scores. Based on these results, NCE’s for anticancer activity were successfully designed and analysed in this research work which will be helpful for effective drug synthesis with less toxicity in the future. Others: 2D QSAR model was generated by three methods, and the best one was selected for further study. NCEs were planned based on descriptors such as topological, electrostatic, steric, and hydrophobic substitutions around the core.

Conducting bioinformatics analysis to predict sulforaphane-triggered adverse outcome pathways in healthy human cells

Sulforaphane (SFN) is a naturally occurring molecule present in plants from Brassica family. It becomes bioactive after hydrolytic reaction mediated by myrosinase or human gastrointestinal microbiota. Sulforaphane gained scientific popularity due to its antioxidant and anti-cancer properties. However, its toxicity profile and potential to cause adverse effects remain largely unidentified. Thus, this study aimed to generate SFN-triggered adverse outcome pathway (AOP) by looking at the relationship between SFN-chemical structure and its toxicity, as well as SFN-gene interactions. Quantitative structure-activity relationship (QSAR) analysis identified 2 toxophores (Derek Nexus software) that have the potential to cause chromosomal damage and skin sensitization in mammals or mutagenicity in bacteria. Data extracted from Comparative Toxicogenomics Database (CTD) linked SFN with previously proposed outcomes via gene interactions. The total of 11 and 146 genes connected SFN with chromosomal damage and skin diseases, respectively. However, network analysis (NetworkAnalyst tool) revealed that these genes function in wider networks containing 490 and 1986 nodes, respectively. The over-representation analysis (ExpressAnalyst tool) pointed out crucial biological pathways regulated by SFN-interfering genes. These pathways are uploaded to AOP-helpFinder tool which found the 2321 connections between 19 enriched pathways and SFN which were further considered as key events. Two major, interconnected AOPs were generated: first starting from disruption of biological pathways involved in cell cycle and cell proliferation leading to increased apoptosis, and the second one connecting activated immune system signaling pathways to inflammation and apoptosis. In both cases, chromosomal damage and/or skin diseases such as dermatitis or psoriasis appear as adverse outcomes.

Quantitatively regulating the ketone structure of triazine-based covalent organic frameworks for efficient visible-light photocatalytic degradation of organic pollutants: Tunable performance and mechanisms.

As promising visible-light-responsive photocatalysts, triazine-based covalent organic frameworks (CTFs) still suffer from broad bandgap and high electron-hole recombination. As such, different contents of electron-rich ketone group were introduced to CTFs (X % keto-CTF), aiming to clarify the mechanism of quantitatively regulating ketone for enhanced visible-light photocatalytic performance of CTFs. As ketone content increased, the bandgap narrowed, electron-hole recombination decreased, charge transfer and quantum yield increased. As a result, keto-CTF outperformed other keto-CTFs in visible-light photocatalytic degradation of tetracycline, and apparent rate constant of TC (kobs) was 3.69 times higher than that of CTF. Importantly, ketone tuning induced varied types and concentrations of reactive species. Integrated with quantitative structure-activity relationships (QSARs) analysis and density functional theory (DFT) calculations, this study unravels how ketone content regulates bandgap structure of CTF, affects the contribution of varied reactive species, and quantitatively enhances the photocatalytic performance of CTFs. It also provides novel insights into the precise design and synthesis of CTFs-based catalyst structures for high-efficient visible-light photocatalytic degradation of organic pollutants.

The role of the direct and indirect mechanism in the advanced oxidation process induced degradation of ciprofloxacin

The fluoroquinolone ciprofloxacin occurs worldwide in the aquatic environment and has therefore been listed on the 3rd European union watchlist for monitoring. To eliminate sewage and wastewater treatment plants as entry pathways, advanced oxidation processes have been intensely researched. Hence, photolysis at different pH ranges was studied for its capacity to eradicate ciprofloxacin. High-performance liquid chromatography coupled to high-resolution mass spectrometry was used to identify the degradation products and to monitor all compounds. Ecotoxicity was assessed using quantitative structure-activity relationship analysis comprising the Ecological Structure Activity Relationships tool. Two degradation mechanisms were found active: the direct mechanism, i.e. degradation through ultraviolet absorption by the substance, and the indirect mechanism caused by hydroxyl radicals from water photolysis. The radical scavenger tert-butanol and pH variations revealed that the indirect mechanism predominated in general. The direct photo-induced degradation proceeded about 10 times slower. Products could be attributed to the mechanisms. Based on their chemical structures, all identified products were predicted less toxic by quantitative structure-activity relationship than ciprofloxacin. Mechanistic insight suggested that photo-induced advanced oxidation processes proved efficient for ciprofloxacin elimination when generating hydroxyl radicals. Compounds and pH range hampering their occurrence diminish the efficacy of elimination.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R2) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

Anti-HIV Potential of Beesioside I Derivatives as Maturation Inhibitors: Synthesis, 3D-QSAR, Molecular Docking and Molecular Dynamics Simulations.

HIV-1 maturation is the final step in the retroviral lifecycle that is regulated by the proteolytic cleavage of the Gag precursor protein. As a first-in-class HIV-1 maturation inhibitor (MI), bevirimat blocks virion maturation by disrupting capsid-spacer peptide 1 (CA-SP1) cleavage, which acts as the target of MIs. Previous alterations of beesioside I (1) produced (20S,24S)-15ꞵ,16ꞵ-diacetoxy-18,24; 20,24-diepoxy-9,19-cyclolanostane-3ꞵ,25-diol 3-O-3',3'-dimethylsuccinate (3, DSC), showing similar anti-HIV potency compared to bevirimat. To ascertain the binding modes of this derivative, further modification of compound 1 was conducted. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis combined with docking simulations and molecular dynamics (MD) were conducted. Five new derivatives were synthesized, among which compound 3b showed significant activity against HIV-1NL4-3 with an EC50 value of 0.28 µM. The developed 3D-QSAR model resulted in great predictive ability with training set (r2 = 0.99, q2 = 0.55). Molecular docking studies were complementary to the 3D-QSAR analysis, showing that DSC was differently bound to CA-SP1 with higher affinity than that of bevirimat. MD studies revealed that the complex of the ligand and the protein was stable, with root mean square deviation (RMSD) values <2.5 Å. The above results provided valuable insights into the potential of DSC as a prototype to develop new antiviral agents.

Papyrus: a large-scale curated dataset aimed at bioactivity predictions

With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure–activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.Graphical

Structure-based in silico investigation of antagonists of human ribonucleotide reductase from Annonamuricata

Human ribonucleotide reductase (HRR) also known as ribonuceotide diphosphate reductase catalyzes the conversion of ribonucleotide to deoxyribonuceotides which is the rate limiting step in the synthesis of DNA, establishing the enzyme as a vital therapeutic target in discovery of cancer drugs. Hence, this study aimed at predicting HRR inhibitor(s) from Annona muricata bioactive compounds. A library of one hundred and twenty-six (126) compounds from Annona muricata was screened against HRR via the glide model. Eight (8) leads were used for the molecular mechanics generalized born surface area (MM-GBSA), e-pharmacophore, quantitative structural activity relationship (QSAR), pharmacokinetics profile and the top scored compound for molecular dynamics (MD) simulation using the Maestro - Schrödinger suite (2017-1). The quantum mechanics calculation of the lead compounds was carried out using Spartan 10. The results of the molecular docking and MM-GBSA calculation revealed eight (8) lead compounds observed as potent antagonists of HRR with binding affinity ranging from −6.454 kcal/mol to −10.190 kcal/mol which are comparable to the reference drug (Gemcitabine) and co-crystalized ligand. These eight hit compounds showed high fitness score and pIC50 value from the e-pharmacophore and QSAR analysis respectively. The result of the density functional theory of the hit compounds revealed that, EHOMO values range from −5.38 eV to −5.83 eV suggesting the hit molecules as electron donor. This study has predicted eight compounds from A. muricata as inhibitors of HRR with the stability of the top-scored compound (myricetin)-protein complex validated via MD simulation. Hence, in vivo and in vitro experiments are proposed for the discovery of potent therapeutic agents in the management of cancer via human ribonucleotide reductase inhibition.

In silico identification of natural compounds from virgin coconut oil as potential ligand peroxisome proliferator-activated receptor-gamma as preventive food leads against colitis: Is it really work?

Ulcerative colitis (UC) is an inflammation of the large intestine characterized by diarrhea with blood. UC has a more extensive manifestation in children. Current therapy has not given satisfactory results. This is the basis for the need for preventive therapy to reduce the morbidity and mortality of UC in children. Virgin coconut oil (VCO) is a viable dietary supplement option due to its ability to act as a peroxisome proliferator-activated receptor (PPAR) ligand, inhibiting the release of pro-inflammatory cytokines. The aim of this study was to determine natural compounds from VCO that have the potential to prevent colitis using a docking-based virtual screening approach. Quantitative structure-activity relationship analysis was used to find out how similar the input compounds and the database were. Docking is done using AutoDockTools 1.5.6. The algorithm used is the Lamarckian Genetic Algorithm (4.2). PPAR-gamma (PPAR-γ) was used as the target protein in a complex with rosiglitazone (ID PDB: 7AWC). PyMol 2.5.1 was used to prepare and visualize three-dimensional data, and the amino acid interactions were visualized using Discovery Studio 2021 Clients. It was found that linoleic acid and oleic acid in VCO have anti-inflammatory effects with predictive values of 0.73 and 0.614, respectively, and that they stop tumor necrosis factor (TNF) expression with predictive values of 0.751 and 0.724. The result of molecular docking showed that the VCO compound was able to interact with the same residue as the control. VCO reduces inflammation by acting as a PPAR-γ and TNF expression inhibitor.

QSAR Studies on Some C-21 Mercapto Derivatives as AntiPulmonary Inflammatory Agents

Glucocorticoids have been widely used for the treatment of inflammatory conditions but are associated with various side effects. C-21 mercapto derivatives, a class of dissociated steroids, have shown potential as anti-inflammatory agents with reduced side effects. In this study, QSAR (Quantitative Structure-Activity Relationship) analysis was performed on a series of 19 C-21 mercapto hydrocortisone derivatives with IL-6 cytokine inhibition activities. Molecular descriptors were calculated using Alva descriptor software, and significant descriptors governing the activity were identified. Multiple linear regression analysis revealed statistically significant models, and the best two-variable model included spectral mean absolute deviation from reciprocal squared geometrical matrix (SpMAD_RG) and H autocorrelation of lag 3 weighted by i state (H3s). Internal and external validation confirmed the reliability of the models. The study suggests that these structural descriptors, particularly SpMAD_RG and H3s, play a crucial role in the activity prediction of C-21 mercapto derivatives. The findings contribute to the design of novel molecules in this class with improved therapeutic potential.

In vivo polymerization of the dopamine-borate melanin precursor: A proof-of-concept regarding boron neutron-capture therapy for melanoma

The 10boron neutron-capture therapy (BNCT) is an emerging antitumoral method that shows increasing biomedical interest. BNCT is based on the selective accumulation of the 10boron isotope within the tumor, which is then irradiated with low-energy thermal neutrons, generating nuclear fission that produces 7lithium, 4helium, and γ rays. Simple catechol-borate esters have been rather overlooked as precursors of melanin biosynthesis, and therefore, a proof-of-concept approach for using dopamine-borate (DABO) as a suitable boron-containing candidate for potential BNCT is presented here. DABO can spontaneously oxidize and autopolymerize in vitro, giving a soluble, eumelanin-like brown-black poly-DABO product. Melanotic melanoma cell cultures treated with 1 mM DABO for 24 and 48 h were viable and showed no signs of damage or cell death. The stability and possible trans-esterification of DABO is shortly discussed. Chemical calculations and quantitative structure-activity relationships (QSAR) analysis of DABO and the BNCT agent BPA indicated that they should be cell permeant and accumulate within lysosomes and melanosomes. Molecular modeling allows visualization of both the DABO precursor and the structure of a borate derivative of the proposed catechol-porphycene model for eumelanin, showing interesting features from molecular orbital calculations. The main difference between DABO and other agents, such as BPA, is that it is not a boronic acid nor a boron cluster. This simple catechol-borate ester (protected from oxidation and blackening) could be administrated to living cells and organisms, in which biosynthesis of boron-melanin in melanoma melanocytes can lead to improved BNCT.

QSAR, structure-based pharmacophore modelling and biological evaluation of novel platelet ADP receptor (P2Y12) antagonist.

P2Y12 has a key role in platelet aggregation and thrombus formation via an ADP-induced platelet activation mechanism. Recently, P2Y12 antagonists have become of great interest in the clinical management of antithrombotic therapy. In light of this, we explored the pharmacophoric space of P2Y12 using structure-based pharmacophore modelling. Subsequently, genetic algorithm and multiple linear regression analyses were conducted to select the best combination of physicochemical descriptors and pharmacophoric models to create useful predictive quantitative structure-activity relationship (QSAR) equation (r 2 = 0.9135, r (adj) 2 = 0.9147, r (PRESS) 2 = 0.9129, LOF = 0.3553). One pharmacophoric model emerged in the QSAR equation and was validated by analysing receiver operating characteristic (ROC) curves. The model was then used to screen 200 000 compounds from the National Cancer Institute (NCI) database. The top-ranked hits were in vitro tested, where their IC50's range between 4.20 to 35.00 μM when measured via the electrode aggregometry assay. Whilst, the VASP phosphorylation assay showed 29.70% platelet reactivity index for NSC618159, which is superior to that of ticagrelor.

QSPR/QSAR analysis of some eccentricity based topological descriptors of antiviral drugs used in COVID-19 treatment via $ \mathscr{D}\varepsilon $- polynomials.

In the field of chemical and medical sciences, topological indices are used to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. The COVID-19 pandemic is largely recognized as the most life-threatening crisis confronting medical advances. Scientists have tested various antiviral drugs and discovered that they help people recover from viral infections like COVID-19. Antiviral medications, such as Arbidol, Chloroquine, Hydroxy-Chloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide and Theaflavin, are often used to treat COVID-19. In this paper, we define Diameter Eccentricity Based vertex degree and employ it to introduce a new polynomial called $ D\varepsilon- $ Polynomial. Using the newly introduced polynomial, we derive new topological indices, namely, diameter eccentricity based and hyper diameter eccentricity based indices. In order to check the efficacy of our indices, we derive the $ D\varepsilon- $ polynomials for the eight COVID-19 drugs mentioned above. Using these polynomials, we compute our proposed topological descriptors for the eight COVID-19 drugs. We perform quantitative structure-property relationship (QSPR) analysis by identifying the best fit curvilinear/multilinear regression models based on our topological descriptors for 8 physico- chemical properties of the COVID-19 drugs. We also perform quantitative structure-activity relationship (QSAR) analysis by identifying the best fit multilinear regression model for predicting the $ IC_{50} $ values for the eight COVID-19 drugs. Our findings and models may be useful in the development of new COVID-19 medication.

Potential Activity of Caryophyllene Derivatives as Xanthine Oxidase Inhibitor: An in silico Quantitative Structure-Activity Relationship Analysis

As the enzyme responsible for the uric acid formation, Xanthine oxidase was considered to be a therapeutic target for hyperuricemic treatment. This study was carried out to assess the potential of caryophyllene, and its derivates usually present in the natural product to inhibit Xanthine oxidase. The molecular docking using Autodock Tool and Biovia Discovery Studio was conducted to visualize the molecular interaction and to reveal the structure-activity relationship of those compounds. The results showed that the derivates of caryophyllene showed a higher affinity to Xanthine Oxide than Allopurinol. Among those all, caryophyllene oxide has the most stable bonding to xanthine oxide. Structure-activity relationship analysis showed that the chemical properties of the compound affected the affinity and molecular interaction to the enzyme as the target site in this study. The number of double bonds, substituents position, conformational structure related to steric hindrance, and the presence of lactone ring were assumed to influence the xanthine oxide inhibitory activity of caryophyllene derivates.

Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer's disease

Alzheimer's disease (AD) is an age-related neurodegenerative disorder, which is the most common cause of dementia in elderly individuals. It is characterized by selective neuronal cell death that affects the brain area related to memory and learning. So far, various computational research targeting AD have been reported, but we are still far from finding a precise treatment strategy for AD. It appeared of interest to us to carry out a two-dimensional quantitative structure-activity relationship (2D-QSAR) analysis against multiple targets of AD using large datasets to determine the essential structural features which are responsible for the inhibition of the enzymes/targets. In the present research, we have implemented 2D-QSAR modeling against twelve major targets (AChE, BuChE, BACE1, β-amyloid, 5-HT6, CDK-5, Gamma-secretase, Glutaminyl Cyclase, GSK-3β, MAO-B, NMDA and Phosphodiester (PDE10A) enzymes) of AD for the identifications of novel multitarget inhibitors. The models were used to check the applicability domain of a pool of ∼19 million compounds obtained from the four chemical drug-like databases (ZINC12, Asinex, NCI, and InterBioscreen databases) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drug. Additionally, we have also developed the quantitative structure activity-activity relationship (QSAAR) and selectivity-based models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have also performed chemical Read-Across predictions using the Read-Across-v3.1 tool (https://dtclab.webs.com/software-tools), the results for the external validation metrics were found to be better than the 2D-QSAR-derived predictions. Furthermore, molecular docking experiments have been performed to understand the molecular interactions between ligands and enzymes at the atomic level, and the observations are compared with the structural features acquired from QSAR models that justified the mechanistic aspect of binding phenomena. The proposed models and read-across hypotheses could be used as potential tools to identify essential molecular features for designing suitable drug(s) for Alzheimer's therapy using rational design of multi-target inhibitors.

Cobalt and nitrogen co-doped monolithic carbon foam for ultrafast degradation of emerging organic pollutants via peroxymonosulfate activation

Cobalt-based catalysts are expected as one of the most promising peroxymonosulfate (PMS) activators for the removal of organic pollutants from industrial wastewater. However, the easy agglomeration, difficult separation, and secondary pollution of cobalt ions limit their practical application. In this study, a novel, highly efficient, reusable cobalt and nitrogen co-doped monolithic carbon foam (Co-N-CMF) was utilized to activate PMS for ultrafast pollutant degradation. Co-N-CMF (0.2 g/L) showed ultrafast catalytic kinetics and higher total organic carbon (TOC) removal efficiency. Bisphenol A, ciprofloxacin, 2,4-dichlorophenoxyacetic acid, and 2,4-dichlorophenol could be completely degraded after 2, 4, 5, and 5 min, and the TOC removal efficiencies were 77.4 %, 68.9 %, 72.8 %, and 79.8 %, respectively, corresponding to the above pollution. The sulfate radical (SO4•-) was the main reactive oxygen species in Co-N-CMF/PMS based on electron paramagnetic resonance. The ecological structure-activity relationship program analysis via the quantitative structure activity relationship analysis and phytotoxicity assessment revealed that the Co-N-CMF/PMS system demonstrates good ecological safety and ecological compatibility. The Co-N-CMF catalyst has good catalytic activity and facile recycling, which provides a fine method with excellent PMS activation capacity for 2,4-dichlorophenol elimination from simulated industrial wastewater. This study provides new insights into the development of monolithic catalysts for ultrafast wastewater treatment via PMS activation.

Peroxymonosulfate activation by iron-carbon composite derived from coal gasification slag for sulfamethoxazole removal: Performance evaluation and mechanism insight

A novel iron-carbon (Fe-C) composite was fabricated using coal gasification slag (CGS) as the feedstocks and applied in sulfamethoxazole (SMX) degradation via peroxymonosulfate (PMS) activation. The optimized Fe-C composite (CGS-1000) had excellent catalytic ability among the reported metal-C catalysts, showing a superior SMX degradation capacity (91.1 %) and kinetic reaction rate (0.0177 min−1) within 90 min under the condition of 0.2 g·L−1 CGS-1000, 2.0 mM PMS at ambient pH (6.3). Characterization results illustrated that the relatively larger surface area (102.50 m2·g−1), pore size (3.93 nm), and higher carbon defectiveness (ID/IG = 1.48) of CGS-1000 promoted its SMX degradation performance. XPS and FTIR spectrum revealed that PMS was mainly activated by Fe species and oxygen-containing functional groups (CO, C-OH) on the carbon matrix for the generation of reactive oxygen species (ROS). Notably, surface-bounded radicals (SO4−, OH), singlet oxygen (1O2), and direct electron transfer were primarily involved in the CGS-1000/PMS system, of which the relative contributions to SMX oxidation were calculated to be 25.8 %, 49.5 %, and 12.5 %, respectively. The excellent degradation efficiency should be attributed to the simultaneous SMX oxidation both in solution and on the surface of CGS-1000 through these three oxidation pathways. Finally, four SMX degradation pathways were proposed based on the twelve intermediates detected by LC-MS. The toxicity of the by-products of SMX was alleviated according to the quantitative structure–activity relationship analysis. Overall, these results suggested that CGS-1000 was a high-efficiency PMS catalyst for SMX degradation and provided new insights into the design of other solid waste-derived catalysts.