Quantitative Structure-activity Relationship Analysis Research Articles

Synergistic removal mechanism of tetracycline by ethylenediamine modified magnetic chitosan based Fenton-like catalyst.

Modified magnetic chitosan nanoparticles (EMMCS-G), used as a Fenton-like catalyst, were successfully prepared and modified with glutaraldehyde and ethylenediamine. EMMCS-G has strong magnetization, good reusability, stability, environmental friendliness, and high efficiency. In the Fenton-like system, the synergistic effect of adsorption and advanced oxidation significantly enhances the removal effect of tetracycline (TC). The optimal concentration of persulfate was found to be 20 mmol L-1, and at a pH of 3, the removal efficiency of TC reached 95.6% after 6 hours. The oxidation system demonstrated excellent pH adaptability, achieving a TC removal rate of 94% within 6 hours across a pH range of 3 to 8. Hydroxyl (˙OH) and sulfate (SO4 -˙ ) radicals were present in the reaction system, with ˙OH playing an important role in the oxidation process of TC. The attack sites of tetracycline were identified using density functional theory (DFT), and five degradation pathways for TC were proposed based on LS-MS experiments. Finally, quantitative structure-activity relationship (QSAR) analysis was employed to assess the toxicity of the intermediates. Overall, toxicity gradually decreased, indicating that the Fenton reaction system effectively reduced the toxicity and mutagenicity of TC. This study suggests EMMCS-G as a potential catalyst for enhanced Fenton-like degradation with excellent efficiency observed for the degradation of tetracycline for environmental remediation.

Machine Learning-Based Approach to Identify Inhibitors of Sterol-14-Alpha Demethylase: A Study on Chagas Disease.

Chagas Disease, caused by the parasite Trypanosoma cruzi, remains a significant public health concern, particularly in Latin America. The current standard treatment for Chagas Disease, benznidazole, is associated with various side effects, necessitating the search for alternative therapeutic options. In this study, we aimed to identify potential therapeutics for Chagas Disease through a comprehensive computational analysis. A library of compounds derived from Cananga odorata was screened using a combination of pharmacophore modeling, structure-based screening, and quantitative structure-activity relationship (QSAR) analysis. The pharmacophore model facilitated the efficient screening of the compound library, while the structure-based screening identified hit compounds with promising inhibitory potential against the target enzyme, sterol-14-alpha demethylase. The QSAR model predicted the bioactivity of the hit compounds, revealing one compound to exhibit superior activity compared to benznidazole. Evaluation of the physicochemical, pharmacokinetic, toxicity, and medicinal chemistry properties of the hit compounds indicated their drug-like characteristics, oral bioavailability, ease of synthesis, and reduced toxicity profiles. Overall, our findings present a promising avenue for the discovery of novel therapeutics for Chagas Disease. The identified hit compounds possess favorable drug-like properties and demonstrate potent inhibitory effects against the target enzyme. Further in vitro and in vivo studies are warranted to validate their efficacy and safety profiles.

Antiviral Flavonoids: A Natural Scaffold with Prospects as Phytomedicines against SARS-CoV2.

Flavonoids are vital candidates to fight against a wide range of pathogenic microbial infections. Due to their therapeutic potential, many flavonoids from the herbs of traditional medicine systems are now being evaluated as lead compounds to develop potential antimicrobial hits. The emergence of SARS-CoV-2 caused one of the deadliest pandemics that has ever been known to mankind. To date, more than 600 million confirmed cases of SARS-CoV2 infection have been reported worldwide. Situations are worse due to the unavailability of therapeutics to combat the viral disease. Thus, there is an urgent need to develop drugs against SARS-CoV2 and its emerging variants. Here, we have carried out a detailed mechanistic analysis of the antiviral efficacy of flavonoids in terms of their potential targets and structural feature required for exerting their antiviral activity. A catalog of various promising flavonoid compounds has been shown to elicit inhibitory effects against SARS-CoV and MERS-CoV proteases. However, they act in the high-micromolar regime. Thus a proper leadoptimization against the various proteases of SARS-CoV2 can lead to high-affinity SARS-CoV2 protease inhibitors. To enable lead optimization, a quantitative structure-activity relationship (QSAR) analysis has been developed for the flavonoids that have shown antiviral activity against viral proteases of SARS-CoV and MERS-CoV. High sequence similarities between coronavirus proteases enable the applicability of the developed QSAR to SARS-CoV2 proteases inhibitor screening. The detailed mechanistic analysis of the antiviral flavonoids and the developed QSAR models is a step forward toward the development of flavonoid-based therapeutics or supplements to fight against COVID-19.

Development and application of an LC-MS/MS method for the detection of N-nitrosochlordiazepoxide as a potential genotoxic impurity

In this study, we synthesized and characterized N-nitrosochlordiazepoxide, a potential genotoxic impurity originating from chlordiazepoxide hydrochloride, assessing its toxicity via quantitative structure-activity relationship (QSAR) analysis. Characterization techniques including UV spectroscopy and HPLC demonstrated purity at 97.0%, complemented by Fourier transform infrared spectroscopy analysis indicating characteristic peaks at 1,500 cm-1 (nitroso group) and 945 cm-1 (N-O bond), as well as mass spectrometry (m/z 329.2 [M+H]+) and NMR. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, utilizing an HPLC octyldecylsilane column, revealed ion transitions from (Q1) m/z 329.20 to (Q3) m/z 281.90, 299.25, and 240.80. Method validation confirmed linearity (0.18–3 ppm) following The International Conference of Harmonization Q2 (R1) guidelines, with LOD and LOQ determined at 0.18 ppm and 0.375 ppm, respectively. Robustness studies showed <3% RSD with minor adjustments, and method precision exhibited <2.3% RSD for the main fragment (Q3) m/z 281.90, with intermediate precision below 1.8% RSD. Method accuracy was verified by recovery rates between 92.01% and 104.55%, and solution stability was confirmed for 24 hours at room temperature. Notably, analysis of three batches of chlordiazepoxide hydrochloride samples detected no traces of N-nitrosochlordiazepoxide, underscoring the enhanced sensitivity and accuracy of this LC-MS/MS method in pharmaceutical analytical procedures, crucial for ensuring product safety and compliance.

Improving antibiotic removal and anaerobic digestion performance of discarded cefradine pellets through thermo-alkaline pretreatment

Discarded cefradine pellets (DCP) as the hazardous wastes contain lots of bioavailable sucrose. Anaerobic digestion (AD) may be a promising technology for treating DCP, achieving dual goals of waste treatment and resource recovery. However, high concentration of cefradine will inhibit the AD process. This study applied thermo-alkaline pretreatment (TAP) to remove cefradine and improve the AD performance of DCP. Around 95% cefradine could be degraded to different intermediate degradation products (TPs) in TAP at optimal condition, and hydrolysis and hydrogenation were the main degradation pathways. Quantitative structure-activity relationship analysis indicated that the main TPs exhibited lower toxicity than cefradine, and DCP residues after TAP were almost not toxic to E. coli K12 and B. subtilis growth by antibacterial activity analysis. Therefore, TAP promoted the biomethane yield in AD of DCP residues (274.74 mL/g COD), which was 1.91 times that of control group. Besides, compared to control group, final cefradine concentrations in liquids and sludge were significantly decreased in AD system with TAP, lowering environmental risk and indicating stronger prospect for process application. Microbiological analysis revealed that acidogens (Macellibacteroides, Bacteroides), syntrophs (Syntrophobacter, Syntrophorhabdus), and acetoclastic Methanosaeta were enriched in AD system with TAP, which contributed to improving AD performance of DCP.

Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery?

Self-supervised pretrained models are gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pretrained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations has not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship analysis, we propose a method named Representation-Property Relationship Analysis (RePRA) to evaluate the quality of the representations extracted by the pretrained model and visualize the relationship between the representations and properties. The concepts of ACs and SH are generalized from the structure-activity context to the representation-property context, and the underlying principles of RePRA are analyzed theoretically. Two scores are designed to measure the generalized ACs and SH detected by RePRA, and therefore, the quality of representations can be evaluated. In experiments, representations of molecules from 10 target tasks generated by 7 pretrained models are analyzed. The results indicate that the state-of-the-art pretrained models can overcome some shortcomings of canonical Extended-Connectivity FingerPrints, while the correlation between the basis of the representation space and specific molecular substructures are not explicit. Thus, some representations could be even worse than the canonical fingerprints. Our method enables researchers to evaluate the quality of molecular representations generated by their proposed self-supervised pretrained models. And our findings can guide the community to develop better pretraining techniques to regularize the occurrence of ACs and SH.

Exploring the Transmembrane Behaviors of Dietary Flavonoids under Intestinal Digestive Products of Different Lipids: Insights into the Structure-Activity Relationship In Vitro.

This study aimed to investigate the transmembrane transport behavior and structure-activity relationships of various dietary flavonoids in the presence of dietary lipids derived from different sources in vitro. Results revealed that the digestion products of soybean oil (SOED) and lard (LOED) augmented the apparent permeability coefficients of most dietary flavonoids, and SOED exhibited higher transport compared with LOED. The structural properties of flavonoids and the potential interactions between fatty acids in these digestion products and flavonoids may influence the outcomes. 3D quantitative structure-activity relationship analyses revealed that incorporating small-volume groups at position 8 of the A-ring augmented the transmembrane transfer of flavonoids in the LOED system compared with the control group. By contrast, the integration of hydrophobic groups at position 5 of the A-ring and hydrogen bonding acceptor groups at position 6 of the A-ring enhanced the transmembrane transportation of flavonoids in the SOED system. Molecular dynamics simulations revealed that the SOED system may facilitate the interactions with flavonoids to form more stable and compact fatty acid-flavonoid complexes compared to the LOED system. These findings may provide valuable insights into flavonoid absorption to facilitate the development and utilization of functional foods or dietary supplements based on dietary flavonoids.

Synthesis, Herbicidal Activity against Barnyard Grass, and Photolytic Behavior of Aryl 2,6-Dipyrimidinoxybenzoates.

In this study, we investigated the characteristics and herbicidal potential of bispyribac phenolic esters, which belong to the 2-(pyrimidin-2-yloxy)benzoic acid (PYB) class of acetohydroxyacid synthase (AHAS-)-inhibiting herbicides. These herbicides are primarily used for controlling Poaceae and broadleaf weeds. Among them, bispyribac-sodium stands out as a representative in this class. Surprisingly, other bispyribac esters, including alkanol and phenol esters exhibit considerably reduced herbicidal activity compared to bispyribac-sodium. In contrast, oxime esters (e.g., pyribenzoxim) demonstrate high activity. To further understand and develop novel PYB herbicides, we synthesized and screened a series of bispyribac phenolic esters while investigating their photochemical behaviors. Several compounds displayed excellent herbicidal activity, with compounds Ia-19 and Ic showing impressive 90% effective dosages for fresh weight inhibition of barnyard grass, measuring 0.55 and 0.60 g a.i./hm2, respectively. These values were approximately half of bispyribac-sodium or pyribenzoxim. The results indicate that the herbicidal activity of phenolic esters is influenced by both their binding ability to the AHAS enzyme and their decomposition into bispyribac acid. For instance, bispyribac phenol ester exhibited considerably reduced receptor affinity compared to bispyribac-sodium, and faced challenges in transforming into bispyribac acid, explaining its diminished herbicidal activity. However, introducing a photosensitive nitro group led to a complete transformation. This modification improved its affinity with AHAS and accelerated its decomposition into bispyribac acid, further accelerated by photocatalysis. Consequently, nitro-containing compounds displayed heightened herbicidal activity. The findings from this study open possibilities for structural optimization of phenolic esters through quantitative structure-activity relationship analysis, potentially regulating their activity-releasing period. Furthermore, the high activity of aromatic heterocyclic esters offers new insights into developing novel PYB herbicides.

Design and Evaluation of Synthesized Pyrrole Derivatives as Dual COX-1 and COX-2 Inhibitors Using FB-QSAR Approach.

This study delves into the intricate dynamics of the inflammatory response, unraveling the pivotal role played by cyclooxygenase (COX) enzymes, particularly COX-1 and COX-2 subtypes. Motivated by the pursuit of advancing scientific knowledge, our contribution to this field is marked by the design and synthesis of novel pyrrole derivatives. Crafted as potential inhibitors of COX-1 and COX-2 enzymes, our goal was to unearth molecules with heightened efficacy in modulating enzyme activity. A meticulous exploration of a synthesis library, housing around 3000 compounds, expedited the identification of potent candidates. Employing advanced docking studies and field-based Quantitative Structure-Activity Relationship (FB-QSAR) analyses enriched our understanding of the complex interactions between synthesized compounds and COX enzymes. Guided by FB-QSAR insights, our synthesis path led to the identification of compounds 4g, 4h, 4l, and 4k as potent COX-2 inhibitors, surpassing COX-1 efficacy. Conversely, compounds 5b and 5e exhibited heightened inhibitory activity against COX-1 relative to COX-2. The utilization of pyrrole derivatives as COX enzyme inhibitors holds promise for groundbreaking advancements in the domain of anti-inflammatory therapeutics, presenting avenues for innovative pharmaceutical exploration.

QSAR ANALYSIS USING SEMI-EMPIRICAL AM1 METHOD, MOLECULAR DOCKING, AND ADMET STUDIES OF CHALCONE DERIVATIVES AS ANTIMALARIAL COMPOUNDS

Malaria is a serious caused by protozoan parasites such as Plasmodium groups and has fatal consequences for human health. The increase in the resistance of the Plasmodium parasites toward existing antimalarial drugs prompts the exploration of novel compounds. In this study, quantitative structure-activity relationship (QSAR) analysis using the semi-empirical AM1 method was conducted to identify the optimal model that relates physicochemical properties and biological activity of chalcone derivatives. In addition, ADMET prediction and molecular docking were also carried out. Multilinear regression calculations for statistical parameters of QSAR models revealed that Model 4, with 11 independent variables, provided the best predictions and exhibited a robust correlation with antimalarial activity represented by inhibitory concentration (IC50). ADMET predictions indicated favorable absorption, distribution, metabolism, excretion, and toxicity properties, particularly for B2D, showing promising antimalarial attributes. Molecular docking studies targeting 5 mutated PfDHODH proteins revealed B2D’s potential to reach therapeutic targets efficiently. It has low docking scores for mutations I (-10.5 kcal/mol), II (-8.6 kcal/mol), and V (-10.5 kcal/mol) with RMSD < 2Å, in carrying out its role for antimalarial activity. This research successfully identifies B2D as an efficient inhibitor of PfDHODH receptors. Thus, it is a highly promising novel antimalarial drug.

Selective electrophilic attack towards organic micropollutants with superior Fenton-like activity by biochar-supported cobalt single-atom catalyst

The global shortage of freshwater and inadequate supply of clean water have necessitated the implementation of robust technologies for wastewater purification, and Fenton-like chemistry is a highly-promising approach. However, realizing the rapid Fenton-like chemistry for high-efficiency degradation of organic micropollutants (OMs) remains challenging. Herein, one novel system was constructed by a Co single-atom catalyst activating peroxymonosulfate (PMS), and the optimal system (SA-Co-NBC-0.2/PMS) achieved unprecedented catalytic performance towards a model OM [Iohexol (IOH)], i.e., almost 100% decay ratio in only 10 min (the observed rate constant: 0.444 min−1) with high electrophilic species 1O2 (singlet oxygen) generation. Theoretical calculations unveiled that Co-N4 sites preferred to adsorb the terminal-O of PMS (more negative adsorption energy than other O sites: –32.67 kcal/mol), promoting the oxidation of PMS to generate 1O2. Iodine (I)23 (0.1097), I24 (0.1154) and I25 (0.0898) on IOH with higher f– electrophilic values were thus identified as the main attack sites. Furthermore, 16S ribosomal RNA high-throughput sequencing and quantitative structure–activity relationship analysis illustrated the environmentally-benign property of the SA-Co-NBC-0.2 and the tapering ecological risk during IOH degradation process. Significantly, this work comprehensively checked the competence of the SA-Co-NBC-0.2/PMS system for organics abatement in practical wastewater.

Identification of potent small-molecule PCSK9 inhibitors based on quantitative structure-activity relationship, pharmacophore modeling, and molecular docking procedure

Abstract Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) attaches to the domain of LDL receptor (LDLR), diminishing LDL-C influx and LDLR cell surface presentation in hepatocytes, resulting in higher circulating LDL-C levels. PCSK9 dysfunction has been linked to lower levels of plasma LDLC and a decreased CHD risk. Purpose To identify a potent small-molecule PCSK9 inhibitor in compounds that are currently being studied in clinical trials. Methods We first performed chemical absorption, distribution, metabolism, excretion, and toxicity filtering of 9800 clinical trial compounds obtained from the ZINC 15 database using Lipinski's rule of five and achieved 3853 compounds. Two-dimensional (2D) Quantitative Structure-Activity Relationship (QSAR) was initiated by computing molecular descriptors and selecting important descriptors of 23 PCSK9 inhibitors. Multivariate calibration was performed with the partial least square regression (PLS) method with 18 compounds for training to design the QSAR model and five compounds for the test set to assess the model. The best latent variables (LV) (LV=6) with the lowest value of Root-Mean-Square Error of Cross-Validation (RMSECV) of 0.48 and leave-one-out cross-validation correlation coefficient (R2CV)=0.83 were obtained for the QSAR model. The low RMSEC (0.21) with high R²cal (0.966) indicates the probability of fit between the experimental data and the calibration model. Results Using QSAR analysis of 3853 compounds, 2635 had a pIC50<1 and were considered for pharmacophore screening. The PHASE module designed the pharmacophore hypothesis through multiple ligands. The top 14 compounds (pIC50>1) were defined as active, whereas nine (pIC50<1) were considered as an inactive set. Three five-point pharmacophore hypotheses achieved the highest score: DHHRR1, DHHRR2, and DHRRR1. The highest and best model with survival scores (5.365) was DHHRR1, comprising one hydrogen donor (D), two hydrophobic groups (H), and two rings of aromatic (R) features. We selected the molecules with a higher 1.5 fitness score (257 compounds) in pharmacophore screening (DHHRR1) for molecular docking screening.Molecular docking indicates that ZINC000051951669, with a binding affinity: of -13.2 kcal/mol and two H-bonds, has the highest binding to the PCSK9 protein. ZINC000011726230 with energy binding: -11.4 kcal/mol and three H-bonds, ZINC000068248147 with binding affinity: -10.7 kcal/mol and one H-bond, ZINC000029134440 with a binding affinity: -10.6 kcal/mol and four H-bonds were ranked next, respectively. Conclusion The archived molecules identified as inhibitory PCSK9 candidates, and especially ZINC000051951669 may therefore significantly inhibit PCSK9 and should be considered in the newly designed trials.Figure 1.The six-top docking compound against PCSK9 with lowest binding affinity. A) ZINC000051951669, B) ZINC000011726230, C) ZINC000068248147, D) ZINC000029134440, E) ZINC000000602086, F) ZINC000034630885.

Marbofloxacin mitigation by simultaneous process of adsorption and advanced oxidative process: An approach to the degradation mechanism and evaluation of the eco-toxicological impact using the QSAR tool

The present study investigated the adsorption and oxidation of the antibiotic Marbofloxacin (MBX) individually and simultaneously. The concentrations of MBX were monitored over time to assess the efficiency of the simultaneous process besides High-Performance Liquid Chromatography (HPLC) was employed to quantify the MBX degradation. An experimental design of three Box-Behnken Factors was performed to study the influence between the variables. To adsorption as an individual method, the best conditions were achieved at a pH of 7.8, 0.85 g of activated carbon, and a contact time of 42 min. To Fenton, the best conditions were achieved at a ratio of 6.25 (r = [H2O2]/[Fe2+]) and a time of 30 min. The final concentration reached by adsorption and Fenton as individual processes were 0.49 and 0.12 mg L−1, respectively, while the concentration of MBX in the simultaneous process reached a final concentration of 0.078 mg L−1. The results indicated that the simultaneous process exhibited greater efficiency in decreasing MBX concentration when compared to individual processes. The formation of degradation products also decreased after treatment, suggesting the potential environmental safety of the combined treatment. Finally, to further comprehend the environmental impact of MBX in water, a toxicity Quantitative Structure-Activity Relationship (QSAR) analysis was conducted to predict its potential toxicity, enabling a more thorough assessment of its ecological risks. It was concluded, that the method offers promising implications for environmental safety.

Experimental determination and QSAR analysis of the rate constants for [formula omitted] reactions with aromatic micropollutants in water

S(IV)-based systems used for advanced oxidation processes (AOPs) have been constructed for the degradation of organic contaminants via oxysulfur radicals, including SO3•−, SO4•−, and SO5•−. Although SO5•− is proposed as an active species in AOPs processes, research on the reactivity of SO5•− has remained unclear. In this work, 53 target aromatic micropollutants (AMPs), including 13 phenols, 27 amines, and 13 PPCPs were selected to determine the second-order reaction rate constants for SO5•− using the competitive kinetics method, in which the kSO5•− values, observed at pH 4 ranged from (2.44 ± 0.00) × 105 M−1 s−1 to (4.41 ± 0.28) × 107 M−1 s−1. Quantitative structure-activity relationship (QSAR) models for the oxidation of AMPs by SO5•− were developed based on 40 kSO5•− values of amines and phenols, and their molecular descriptors, using the stepwise multiple linear regression method. This comprehensive model exhibited the excellent goodness-of-fit (Radj2 = 0.802), robustness (QLOO2 = 0.749), and predictability (Qext2 = 0.656), and the one-electron oxidation potential (Eox), energy of the highest occupied molecular orbital energy (EHOMO), and most positive net atomic charge on the carbon atoms (qC+) were considered the most influential descriptors for the comprehensive model, indicating that SO5•− oxidizes pollutants via single electron transfer reaction and exhibits a strong oxidation capacity, especially for pollutants containing electron-donating groups. Moreover, the kSO5•− values of 13 PPCPs were predicted using this comprehensive model, which suggested the practical application significance of the QSAR model. This study emphasizes the direct oxidation capacity of SO5•−, which is important to evaluate and simulate AOPs based on S(IV).

Calotropis gigantea fiber confined ZIF-67 derived Co, N in-situ assembled hollow carbon fiber to activate peroxymonosulfate for degradation of perfluorooctanoic acid

Perfluorinated compounds have serious impacts on ecosystems and threats to human health, and exploring technologies to remove them is urgently needed. Using Calotropis gigantea fiber (CGF) as the biological template, the precursor of ZIF-67 was firstly in-situ anchored in the inner/outer walls of the fiber and then pyrolyzed to yield the composite catalyst labelled as Co/C@C. In combination with peroxymonosulfate (PMS), the advanced oxidation system, i.e. Co/C@C/PMS, was constructed for the catalytic removal of perfluorooctanoic acid (PFOA) from water. The experimental results showed that under the optimized conditions of PMS dosage of 1.0 g/L, catalyst dosage of 0.5 g/L and pH 3.0, PFOA achieved the degradation efficiency of 72.2% within 4 h at ambient temperature. Combined with free radical quenching, electron paramagnetic resonance, electrochemistry and nitroblue tetrazolium dichloride-based UV-Vis analysis, the reactive oxygen species and diverse pathways were identified, including SO4•−, O2•−, O21 and electron transfer process in the Co/C@C/PMS system. The coexisting anions (HCO3−, Cl−, H2PO4− and NO3−) and real wastewaters showed no significant effects on PFOA degradation, demonstrating that the Co/C@C/PMS system has good resistance to external ions and different media. After four cycles, the degradation efficiency of PFOA was still 62.8%, indicating that Co/C@C has high stability and good reusability. The degradation products of PFOA were mainly short-chain perfluorinated carboxylic acids (C4-C7) and hydrogen-substituted perfluorinated compounds. Based on quantitative structure-activity relationship analysis, their ecotoxicity was finally evaluated via ECOSAR method. In short, natural CGF derived Co/C@C has great potential for the reduction of emerging PFOA to achieve a sustainable water environment for humans.

‘3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity

Iron overload is a primary cause of vital organ failure in patients with blood transfusion-dependent beta-thalassemia, and the hypoxia-inducible factor-1 α (HIF-1α) plays an important role in iron homeostasis pathway. HIF-1α modulation as a potential therapeutic target approach for iron chelation in hepatocyte cells. In this study, we used a 3D quantitative structure-activity relationship (QSAR) analysis to predict the inhibitory activity of HIF-1α inhibitors for iron chelation in liver cells. These feature descriptors were used to build a 3D-QSAR model, which was validated using Cost analysis and Fischer's randomization test. The model was used to virtually search the chemical compound libraries for potential inhibitor candidates with least inhibitory activity. The High-throughput Docking (Libdock) approach was used to dock large repositories of chemical molecules. Following Libdock score screening, the protein-ligand poses were docked using docking optimization (Cdocker) method. Binding energy were calculated for the protein-ligand poses of lowest –Cdocker Energy and –Cdocker Interaction. Further, side chain hopping method was used to generate lead novel ligand from best hit pose of ligand. Molecular dynamics simulation study to evaluate the lead novel ligand. Our study demonstrates the utility of 3D-QSAR pharmacophore screening in predicting the inhibitory activity for target. Inhibition strategy for iron chelation provides an alternative routes for reducing the iron content.

Peroxydisulphate activated FTO-WO3 nanorods based photoelectrocatalytic degradation of tetracycline: Intermediate products, degradation pathway and ecotoxicity studies

This work reports sulphate radical assisted photoelectrocatalytic (SR-PEC) degradation of tetracycline using a visible light active fluorine-doped tin oxide – tungsten trioxide nanorods (FTO-WO3 NRs) photoanode. The WO3 NRs were synthesised via the hydrothermal method and then conducted on the FTO glass to form a photoanode. When the photoanode was applied without sulphate radicals for PEC degradation, 10 % of the tetracycline was degraded. Conversely, when 3 mM persulphate was added, the extent of tetracycline degraded was 88 % using the UV–vis spectrophotometer and 99 % using the ultra-performance liquid chromatography mass spectrometer (UPLC-MS) within 90 min at 1.5 V. The mechanism of tetracycline degradation was proposed based on the intermediate products identified using UPLC-MS and the extent of toxicity was evaluated using quantitative structure activity relationship (QSAR) analysis. Trapping experiment revealed that the photogenerated holes, sulphate radicals, and hydroxyl radicals were the oxidants that significantly took part in the degradation of tetracycline. Overall, the electrode was stable and reusable, therefore suggesting the suitability of FTO-WO3 NRs photoanode in the presence of sulphate radicals towards the decontamination of water laden with pharmaceutical pollutants.

QSAR analysis of five generations of cephalosporins to establish the structural basis of activity against methicillin-resistant and methicillin-sensitive Staphylococcus aureus.

Solving the worldwide problem of growing bacterial drug resistance will require a short-run and medium-term strategy. Structure-activity relationship (SAR) and quantitative SAR (QSAR) analyses have recently been utilized to reveal the molecular basis of the antibacterial activity and antibacterial spectrum of penicillins, the use of which is no longer solely empirical. Likewise, a more rational drug design can be achieved with cephalosporins, the largest group of β-lactam antibiotics. The current contribution aimed to establish the molecular and physicochemical basis of the antibacterial activity of five generations of cephalosporins on methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA). With SAR and QSAR analyses, the molecular portions that provide essential and additional antibacterial activity were identified. The substitutions with greater volume and polarity on the R2 side chain of the cephem nucleus increase potency on MSSA. The best effect is produced by substitutions with polar nitrogen atoms at the alpha-carbon (Cα). Substitutions with greater volume and polarity on the R1 side chain further enhance antibacterial activity. In contrast, the effect against MRSA seems to be independent of any substitution on R2 or at the Cα, while depending on the accessory portions with greater volume and polarity on R1.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

Exploration of Structure-Activity Relationship Using Integrated Structure and Ligand Based Approach: Hydroxamic Acid-Based HDAC Inhibitors and Cytotoxic Agents.

The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for histone deacetylase (HDAC) inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anticancer activity. We have selected a dataset from earlier research findings. The target and ligand molecules were procured from recognized databases and incorporated into pivotal findings such as molecular docking (XP glide), e-pharmacophore study and 3D QSAR model designing study (phase). Docking revealed molecule 39 with better docking score and well binding contact with the protein. 3D QSAR analysis, which was performed for partial least squares factor 5 reported good 0.9877 and 0.7142 as R2 and Q2 values and low standard of deviation: 0.1049 for hypothesis AADRR.139. Based on the computational outcome, it has been concluded that molecule 39 is an effective and relevant candidate for inhibition of HDAC activity. Moreover, these computational approaches motivate to discover novel drug candidates in pharmacological and healthcare sectors.