Quantitative Structure-activity Relationship Analysis Research Articles

QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency.

O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.

MolSHAP: Interpreting Quantitative Structure-Activity Relationships Using Shapley Values of R-Groups.

Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired objectives directly while ignoring the explanations for predictions. Although several techniques can provide interpretations for machine learning-based methods such as feature attribution, there are still gaps between these interpretations and the principles commonly adopted by medicinal chemists when designing and optimizing molecules. Here, we propose an interpretation framework, named MolSHAP, for quantitative structure-activity relationship analysis by estimating the contributions of R-groups. Instead of attributing the activities to individual input features, MolSHAP regards the R-group fragments as the basic units of interpretation, which is in accordance with the fragment-based modifications in molecule optimization. MolSHAP is a model-agnostic method that can interpret activity regression models with arbitrary input formats and model architectures. Based on the evaluations of numerous representative activity regression models on a specially designed R-group ranking task, MolSHAP achieved significantly better interpretation power compared with other methods. In addition, we developed a compound optimization algorithm based on MolSHAP and illustrated the reliability of the optimized compounds using an independent case study. These results demonstrated that MolSHAP can provide a useful tool for accurately interpreting the quantitative structure-activity relationships and rationally optimizing the compound activities in drug discovery.

Sanggenol B, a plant bioactive, as a safer alternative to tackle cancer by antagonising human FGFR

Fibroblast Growth Receptor Factor (FGFR) are a family of proteins which are, in addition to their biological role, are involved in various pathological functions, such as cancer cellular proliferation, and metastasis. Deregulation of FGFRs at various points could result in malignancy. A conformational transition of the DFG (Asp–Phe–Gly) motif can switch the enzyme from a catalytically active (DFG-in) to an inactive (DFG-out) state. There are a few FDFR inhibitors which have received approval from the FDA, but these have adverse side effects. Hence, there is a demand for safer alternatives. With this aim, Ligand and Structure based virtual screening was carried to identify suitable lead molecule. In this process, Four Featured atom-based 3D Pharmacophore with quantitative structure–activity relationship analysis (3D-QSAR) was developed. The External validation of the hypothesis was carried invoking criteria such as Area under the ROC curve. Natural plant compound databases such as the Traditional Chinese medicine, NPACT and the ZINC Natural databases were chosen for pharmacophore filtering, which was followed by virtual screening against FGFR isoforms. The compound Sanggenol B was identified as the most suitable lead molecule. Structural stability of the protein-ligand complex and interactions of the ligand (Sanggenol B & the reference compound Ponatinib) with FGFR were analysed for 1000 ns (triplicate) by means of molecular simulation and the binding free energy was calculated using MMGBSA. Sanggenol B (PubChem CID: 15233694) binds effectively at the active site with favourable energies and is proposed as a safe alternative from a natural source. Communicated by Ramaswamy H. Sarma

In-silico Pharmacology for Evidence-Based and Precision Medicine

Precision medicine, driven by genetic and physical characteristics, has emerged as a transformative approach in healthcare, aiming to provide personalised treatments with optimised efficacy and minimised side effects. This approach contrasts evidence-based medicine, which emphasises population-level data and trends. Technological advancements in pharmacometrics and quantitative systems pharmacology have revolutionised pharmaceutical research, enabling the identification of new drug targets and the development of innovative drug delivery systems. Computational methods, such as quantitative structure-activity relationship (QSAR) analysis and in silico pharmacology tools, have played a pivotal role in identifying potential drugs and repurposing existing ones. These computational approaches leverage diverse data sets and predictive models, leading to significant advancements in optimising drug safety and effectiveness. This transformative era, driven by precision medicine and computational pharmacology, holds immense potential for improving patient outcomes and advancing the field of medicine towards personalised and targeted therapeutic interventions.

High-performance biochar-loaded MgAl-layered double oxide adsorbents derived from sewage sludge towards nanoplastics removal: Mechanism elucidation and QSAR modeling

Utilization of sewage sludge for the fabrication of environmental functional materials is highly desirable to achieve pollution mitigation and resource recovery. In the present work, we introduced a novel MgAl-layered double oxide (LDO)@biochar composite adsorbent in-situ fabricated from Al-rich sewage sludge, and its excellent application in nanoplastics adsorption. Initially, fifteen model contaminants with varied conjugate structures, hydrogen bonding and ionic properties were selected for an investigation of adsorption behavior and adsorption selectivity on LDO@biochar. Structural variation of LDO@biochar suggested reconstruction of the layered double hydroxide (LDH) during the adsorption process due to the “memory effect”. Under the synergy of LDH and biochar, the contaminants were adsorbed via multiple adsorbent-adsorbate interactions, including anion exchange, electrostatic interaction, hydrogen bonding and π-π conjugation. Then, a quantitative structure-activity relationship (QSAR) model was constructed by integrating the number of hydrogen bond acceptors, polarity surface area, number of aromatic rings, and Fukui index f(−)x together to reflect the affinity of each contaminant to the adsorbent. Guided by the QSAR model, the negatively charged polystyrene nanoplastics with continuously conjugated aromatic rings were predicted to be effectively adsorbed on LDO@biochar. Experimental tests confirmed a great capacity of LDO@biochar towards the polystyrene nanoplastics, given the equilibrium adsorption capacity as high as 360 mg g−1 at 30–50 °C. This work not only opened up a new avenue for sustainable utilization of sewage sludge towards high-performance environmental functional materials, but also demonstrated the potential of the QSAR analysis as a rapid and accurate approach for guiding the application of an adsorbent to new emerging containments.

Tetracyclic triterpenoids as inhibitors of cytochrome P450 3A4 and their quantitative structure activity relationship analysis

The interaction between herbs and clinical drugs is one of the risk factors for adverse drug reactions. In the present study, the inhibitory effects of 47 tetracyclic triterpenoids toward the main drug-metabolizing CYPs in humans were investigated. Most of the evaluated tetracyclic triterpenoids (TT) exhibited strong inhibitory effects toward CYP3A4 compared with other CYP subtypes. The inhibition kinetics of compounds 9 (23-acetyl alisol C), 44 (hemslecin A), and 47 (cucurbitacin E) against CYP3A4 were studied, and inhibition constant (Ki) was determined as 2.12, 0.196, and 0.162 µM, respectively. Some representative TT exhibited significant inhibitory effects toward the metabolism of gefitinib, atorvastatin, and quetiapine, indicating a potential interaction between TT derivatives and clinical drugs. Additionally, a quantitative structure–activity relationship (QSAR) study of a series of TT as inhibitors of CYP3A4 was performed. The present study provided key information to guide the rational use of herbs rich in TT.

The performance and mechanism of tetracycline and ammonium removal by Pseudomonas sp. DX-21

To remove ammonium and tetracycline (TC) from wastewater, a new strain, DX-21, was isolated and exhibited simultaneous removal ability. The performance of DX-21 in TC removal, its removal mechanism, and the potential toxicities of the degradation products were investigated with genomics, mass spectrometry, density functional theory calculations, quantitative structure–activity relationship analyses, and Escherichia coli exposure experiments. DX-21 exhibited removal of ammonium (9.64 mg·L-1·h−1) via assimilation, and TC removal (0.85 mg·L-1·h−1) primarily occurred through cell surface bio-adsorption and biodegradation. Among the 12 identified degradation products, the majority exhibited lower toxicities than TC. Moreover, potential degradation pathways were proposed, including hydroxylation and deamination. Furthermore, DX-21 possessed TC resistance genes, various oxygenases and peroxidases that could potentially contribute to TC degradation. DX-21 colonized activated sludge and significantly enhanced the biodegradation of TC. Therefore, DX-21 showed potential for treating wastewater containing both ammonium and TC.

Heterocycle-Substituted α-Methylene-γ-Butyrolactones Derivatives Synthesis, Antifungal Activity, and 3D-QSAR.

Developing fungicides from active botanical skeletons is one of the effective methods to tackle the resistance of plant pathogens. Based on our previous discoveries, a series of novel α-methylene-γ-butyrolactone (MBL) derivatives containing heterocycles and phenyl rings were designed according to the antifungal molecule carabrone first discovered in plant Carpesium macrocephalum. The target compounds were synthesized, and the inhibitory activity against pathogenic fungi as well as the mechanism of action were then systematically investigated. Several compounds showed promising inhibitory activities against a variety of fungi. The most potent compound 38 exhibited the EC50 value of 0.50 mg/L against Valsa mali (V. mali), which was more effective than that of commercial fungicide famoxadone. The protective effect of compound 38 against V. mali on apple twigs was superior to that of famoxadone, with an inhibition rate of 47.9% at 50 mg/L. The physiological and biochemical results showed that compound 38 inhibits V. mali by causing cell deformation and contraction, reducing the number of intracellular mitochondria, thickening the cell wall, as well as increasing the permeability of the cell membrane. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, it was shown that the introduction of the bulky and negatively charged groups favored the antifungal activity of the novel MBL derivatives. These findings suggest that compound 38 can be a potential candidate for novel fungicides worthy of further investigation further.

Molecular Descriptors and QSAR Models for Sedative Activity of Sesquiterpenes Administered to Mice via Inhalation.

Essential oils are often utilized for therapeutic purposes and are composed of complex structural molecules, including sesquiterpenes, with high molecular weight and potential for stereochemistry. A detailed study on the properties of selected sesquiterpenes was conducted as part of a broader investigation on the effects of sesquiterpenes on the central nervous system. A set of 18 sesquiterpenes, rigorously selected from an original list of 114, was divided into 2 groups i.e., the training and test sets, with each containing 9 compounds. The training set was evaluated for the sedative activity in mice through inhalation, and all compounds were sedatives at any dose in the range of 4 × 10-4-4 × 10-2 mg/cage, except for curzerene. Molecular determinants of the sedative activities of sesquiterpenes were evaluated using quantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) analyses. An additional test set of six compounds obtained from the literature was utilized for validating the QSAR model. The parental carbonyl cation and an oxygen-containing groups are possible determinants of sedative activity. The QSAR study using multiple regression models could reasonably predict the sedative activity of sesquiterpenes with statistical parameters such as the correlation coefficient r2 = 0.82 > 0.6 and q2 LOO = 0.71 > 0.5 obtained using the leave-one-out cross-validation technique. Molar refractivity and the number of hydrogen bond acceptors were statistically important in predicting the activities. The present study could help predict the sedative activity of additional sesquiterpenes, thus accelerating the process of drug development.

R software for QSAR analysis in phytopharmacological studies.

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable. The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity. The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model. QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for developing new drugs, and quantitative structure-activity relationship (QSAR) analysis has generally been used to perform this task. QSAR models with good predictive power improve the cost and time efficiencies invested in compound development. Generating these good models depends on how well differences between "active" and "inactive" compound groups can be conveyed to the model to be learned. Efforts to solve this difference issue have been made, including generating a "molecular descriptor" that compressively expresses the structural characteristics of compounds. From the same perspective, we succeeded in developing the Activity Differences-Quantitative Structure-Activity Relationship (ADis-QSAR) model by generating molecular descriptors that more explicitly convey features of the group through a pair system that performs direct connections between active and inactive groups. We used popular machine learning algorithms, such as Support Vector Machine, Random Forest, XGBoost and Multi-Layer Perceptron for model learning and evaluated the model using scores such as accuracy, area under curve, precision and specificity. The results showed that the Support Vector Machine performed better than the others. Notably, the ADis-QSAR model showed significant improvements in meaningful scores such as precision and specificity compared to the baseline model, even in datasets with dissimilar chemical spaces. This model reduces the risk of selecting false positive compounds, improving the efficiency of drug development.

Amino Acid Transporter as a Potential Carrier Protein for the Root-to-Shoot Translocation of Polybrominated Diphenyl Ethers in Rice.

As typical persistent organic pollutants, polybrominated diphenyl ethers (PBDEs) tend to accumulate in edible parts of rice, posing great ecological and health risks. The translocation of PBDEs from underground to aboveground parts of rice is a crucial procedure to determine the final bioaccumulation level. Herein, this study aimed to identify the transporter proteins for PBDEs in rice plants in order to strengthen our understanding of the bioaccumulation mechanism and the potential prevention strategy of the PBDE risk. Similar time-dependent patterns were observed among the root-to-shoot translocation factors (TFs) of PBDEs, the expression of lysine histidine transporter (LHT) protein, and the relative levels of LHT substrates (phenylalanine or tyrosine), implying the potential co-transport of PBDEs, phenylalanine, and tyrosine by the carrier LHT. Fluorescence spectra and circular dichroism showed that PBDE congeners interfered with LHT via static fluorescence quenching and changes in the protein's secondary structure. The in vitro sorption fraction of LHT to PBDEs, as revealed by sorption equilibrium analysis, was comparable to the in vivo TF values. Knockout of OsLHT1 in rice using CRISPR/Cas9 technology caused a 48.2-78.4% decrease in PBDE translocation. Molecular docking simulation suggested that PBDEs, phenylalanine, and tyrosine were inserted into the same ligand-binding cavity of LHT, substantiating the potential carrier role of LHT for PBDEs from a conformational perspective. Quantitative structure activity relationship analysis demonstrated that the ether-bond oxygen and the carbons at the site 4 and 4' of PBDE molecules are significant determinants of the binding affinity with the LHT protein and in vivo translocation of PBDEs. In summary, this study discovered that LHT acts as the cellular carrier for PBDEs and offered a comprehensive molecular explanation for the bioaccumulation and translocation of PBDEs in rice plants, covering both biological and chemical perspectives. These findings fill in a knowledge gap on the endogenous transporter proteins for exogenous organic pollutants.

Virtual screening, identification, and potential antioxidant mechanism of novel bioactive peptides during aging by a short-chain peptidomics, quantitative structure–activity relationship analysis, and molecular docking

Antioxidant peptides have received a great deal of attention. However, only a few studies have been conducted on the antioxidant peptides originating from Baijiu. A total of 1490 features deemed potential short-chain peptides (the amino acid number between 2 and 4, SCPs) were screened and analyzed by a customized short-chain peptidomics approach in Feng-flavor Baijiu (FFB) during 14 years of aging, with an obvious discrepancy between FFB aged for 3 years and 6 years being observed. Thirty-nine characteristic SCPs in total were identified and accurately quantified by high-throughput parallel reaction monitoring-based synthetic standards, with the contents ranging from 0.16 to 279.33 μg L−1. Combined with the absorption, distribution, metabolism, excretion, and toxicity analysis model, PGRW, WK, SC, and PAW, four novel antioxidant peptides with high ABTS radical scavenging capacity, were obtained using a customized quantitative structure–activity relationship (QSAR) model based on a two terminal position numbering method, with satisfied coefficients of determination (R2), internal cross-validated R2 (Q2), and external R2 (R2pre) of 0.925, 0.808, and 0.665, respectively. Furthermore, these 4 antioxidant peptides could block the Keap-Nrf2 interaction and promote the accumulation of Nrf2 by molecular docking analysis, and the interaction energy between peptide PGRW and Keap1 was higher than that between epigallocatechin gallate and Keap1 based on CHARMm forced field. Overall, this study facilitated the discovery of functional peptides in Baijiu and the understanding of aging mechanisms.

New-Generation Drug Discovery using Machine Learning

Abstract: Finding innovative molecules with specific chemical properties to treat diseases is one of the goals of drug discovery. Recent years have seen the production of a sizable volume of biological data from many sources. These statistics and molecular analyses have been used to determine the most effective medications. Medical research often frustrates people and is far more expensive. The work at hand is made easier by having the ability to predict whether a medicine will be active or not. The information about the drug can also be used to develop other drugs. One application that makes use of machine learning to enhance decision-making in pharmaceutical data across numerous applications is quantitative structure activity relationship (QSAR) analysis. Machine learning-based predictive models have recently gained a lot of attention in areas outside of preclinical research. Costs and research times associated with finding new drugs are considerably decreased at this stage. Drug research is growing and more commonly utilising machine learning, algorithms for pattern recognition, knowledge of mathematical correlations, and knowledge of the chemical and biological characteristics of molecules. The necessity for a sizable volume of data, the incapacity to interpret the data, and other issues are further restrictions. Without the need for computational resources, massive amounts of data can be analysed using both physical models and machine learning approaches.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods.

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.

Photolysis mechanism of eleven insecticides under simulated sunlight irradiation: Kinetics, pathway and QSAR

Insecticides are widely used in crop protection against insects and frequently detected in aquatic environment. Photolysis kinetics are directly related with exposure assessment and risk assessment. However, the photolysis mechanism of neonicotinoid insecticides with different structures has not been studied and compared systematically in the literature. In this paper, the photolysis rate constants in water were determined for eleven insecticides under irradiation of simulated sunlight. At the same time, the photolysis mechanism and effect of dissolved organic matter (DOM) on their photolysis were studied. The results showed that photolysis rates of eleven insecticides vary in a large range. The photolysis rates of nitro-substituted neonicotinoids and butenolide insecticide are much faster than that of cyanoimino-substituted neonicotinoids and sulfoximine insecticide. The ROS scavenging activity assays reveal that direct photolysis dominates the degradation of seven insecticides and, on the other hand, self-sensitized photolysis dominates four insecticides. The shading-effect from DOM can reduce the direct photolysis rates, on the other hand, ROSs generated by triplet-state DOM (3DOM*) can also accelerate photolysis of insecticides. According to the photolytic products identified from HPLC-MS, these eleven insecticides have different photolysis pathways. Six insecticides are degraded from the removal of nitro group from their parent compounds and four insecticides are degraded through ·OH reaction or singlet oxygen (1O2) reaction. QSAR (quantitative structure-activity relationship) analysis showed that photolysis rate was directly related to the energy gap between the highest occupied molecular orbital to the lowest unfilled molecular orbital (Egap = ELUMO−EHOMO) and dipole moment (δ). These two descriptors reflect the chemical stability and reactivity of insecticides. The pathways developed from identified products and the molecular descriptors of QSAR models can well verify the photolysis mechanisms of eleven insecticides.

Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning.

Downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target for quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single active site, the risk of developing resistance to these fungicides is deemed high. Consequently, using a combination of fungicides is considered an effective way to reduce the development of QoI resistance. Currently, there is little information available to help in the selection of appropriate fungicides. This study used a combination of in silico simulations and quantitative structure-activity relationship (QSAR) machine learning algorithms to screen the most potent QoI-based fungicide combinations for wild-type (WT) and the G143A mutation of fungal cytochrome b. Based on in silico studies, mandestrobin emerged as the top binder for both WT Plasmopara viticola and WT Botrytis cinerea cytochrome b. Famoxadone appeared to be a versatile binder for G143A-mutated cytochrome b of both Plasmopara viticola and Botrytis cinerea. Thiram emerged as a reasonable, low-risk non-QoI fungicide that works on WT and G143A-mutated versions of both fungi. QSAR analysis revealed fenpropidin, fenoxanil, and ethaboxam non-QoIs to have a high affinity for G143A-mutated cytochrome b of Plasmopara viticola and Botrytis cinerea. Above-QoI and non-QoI fungicides can be considered for field studies in a fungicide management program against Plasmopara viticola- and Botrytis cinerea-based fungal infections.

Molecular Docking and QSAR Studies of Indole Derivatives as Antifungal Agents

Introduction: Fungal infection and resistance to existing antibiotics is a major problem across the globe, which is associated with the inappropriate use of antibiotics in humans, animals farming, and agriculture. Unfortunately, addressing these difficulties is becoming extremely critical as they arise. In this paper, we used Hansch and Fujita's linear free energy relationship (LFER) model to perform 2D QSAR (quantitative structure activity relationships) on 28 known Indole derivatives. Objective: Molecular docking and 2D QSAR studies of Indole derivatives as antifungal agents. Methods: 2D QSAR was performed on the reported antifungal activity data of 28 selected compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of Indole derivatives to antifungal activity. To represent the structural properties of compounds, a collection of molecular descriptors such as geometric, topological, hydrophobic and electronic characters were determined. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a predictor variable in the QSAR analysis. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. Result: All docked compounds had better binding scores with reference to fluconazole. Conclusion: Compounds with high values of electronic energy and dipole moment will be effective against fungi, and Indole derivatives having lowered κ2 values will be effective antifungal agents.

Unveiling the Direct Electron Transfer Regime of Peracetic Acid Activation: Quantitative Structure–Activity Relationship Analysis of Carbon Nanotube Catalysis

The demands for high-efficient and green activation of peracetic acid (PAA) have triggered research in exploring carbon catalysis. Nevertheless, the efforts in designing reaction-oriented and high-performance carbon catalysts are largely impeded by an ambiguous understanding of the fundamental carbon structure–PAA activation performance relationship. Herein, we investigated the quantitative structure–activity relationship (QSAR) of carbon nanotubes (CNTs) for PAA activation and micropollutant (MP) removal, by tuning the physiochemical properties of CNT via thermal annealing. The CNT/PAA system was dominated by the nonradical direct electron transfer (DET) oxidation pathway, showing high MP removal rates under complex water matrices. By conducting QSAR analysis, improved catalytic efficacy of the surface-regulated CNTs was attributed to the reinforced DET via the elevated oxidative potential of the CNT–PAA complex and the enhanced electrical conductivity of CNT. Furthermore, the larger specific surface area and lower oxygen content of CNT gave rise to the elevated oxidative potential of the CNT–PAA complex, while the electrical conductivity of CNT was positively correlated with the graphitization degree of CNT. Overall, this work sheds light on the influence cascade of the physicochemical properties of CNT for MP removal and PAA activation, providing guidelines for the fit-for-purpose design of the DET-mediated carbon catalysts for PAA oxidation.