Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

Rational Design for Antioxidant Diphenylamine Derivatives Using Quantitative Structure-Activity Relationships and Quantum Mechanics Calculations

Diphenylamine (DPA) derivatives, used as antioxidants in rubber-based products, inhibit autoxidation by donating hydrogen atoms to peroxyl radicals. Octanol-water partition coefficient (LogKow), an antioxidant index, helps predict their distribution in hydrophobic polymer matrices. Therefore, this study aimed to investigate the relationship between the structure of DPA derivatives and their antioxidant activities, using machine learning with quantitative structure-activity relationships (QSAR) and quantum mechanics (QM). The structure of DPA derivatives was optimized using Density Functional Theory and analyzed for molecular properties. The QSAR models were trained using important descriptors identified through permutation importance. Among the models developed, the Gradient Boosting Regressor (GBR) showed the best performance, with R2 of 0.983 and root mean square error (RMSE) of 0.642 for the test set. SHAP analysis revealed that molecular weight and electronic properties significantly influenced LogKow predictions. For instance, a higher molecular weight was associated with increased LogKow, and a higher positive charge of C2 correlated with higher LogKow predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogKow values of 9.789 and 7.102 for D1 and D2, respectively, which are higher than the training compounds in the model. To gain molecular insight, the quantum chemical calculations with M062X/6-311++G(d,p)//M062X/6-31G(d,p) were performed to investigate the bond dissociation enthalpy (BDE). The results showed that D1 (79.50 kcal/mol) and D2 (72.43 kcal/mol) exhibited lower BDEs than the reference compounds, suggesting that the designed compounds have the potential for enhanced antioxidant activity. In addition, the antioxidant reaction mechanism was studied by using M062X/6-311++G(d,p)//M062X/6-31G(d,p) which found that the hydrogen atom transfer is the key step, where D1 and D2 showed activation energy barriers of 10.38 and 6.29 kcal/mol, respectively, compared to reference compounds of R3 (10.39 kcal/mol), R1 (10.40 kcal/mol), and R2 (18.26 kcal/mol). Therefore, our findings demonstrate that integrating QSAR with quantum chemical calculations can effectively guide the design of DPA derivatives with improved antioxidant properties.

High-Throughput Microarray Approaches for Predicting the Stability of Drug-Polymer Solid Dispersions

Amorphous solid dispersions (ASDs) offer a well-recognized strategy to improve the effective solubility and, hence, bioavailability of poorly soluble drugs. In this study, we developed an extensive library of a significant number of solid dispersion formulations using a library of chemically diverse drugs combined with a water-soluble polymer (polyvinylpyrrolidone vinyl acetate, PVPVA) at different loadings. These formulations were printed as microarrays of solid dispersion formulations, utilizing minimal material amounts (nanograms). They were subjected to a six-month stability study under accelerated conditions (40 °C and 75% relative humidity). Physical stability outcomes varied significantly among the different drug-polymer combinations, with stability ranging from immediate drug crystallization to several days of stability. The comprehensive data set obtained from this high-throughput screening was used to construct multiple linear regression models to correlate the stability of ASDs with the physicochemical properties of the used Active Pharmaceutical Ingredients (APIs). Our findings reveal that increased stability of ASDs is associated with a lower number of hydrogen bond acceptors alongside a higher overall count of heteroatoms and oxygen atoms in the drug molecules. This suggests that, while heteroatoms and oxygen are abundant, their role as hydrogen bond acceptors is limited due to their specific chemical environments, contributing to overall stability. Additionally, drugs with lower melting points formed more stable ASDs within the polymer matrix. This study, hence, highlights the importance of minimizing repulsive drug-polymer interactions to yield a physically stable ASD. The developed models, validated through Leave-One-Out Cross-Validation, demonstrated good predictability of stability trends. Hence, the high-throughput 2D inkjet printing technique that was used to manufacture the microarrays proved valuable for assessing drug-polymer crystallization onset risks and predicting stability outcomes. In conclusion, this study demonstrates a novel approach to solid dispersion formulation physical stability screening, enhancing efficiency, minimizing material requirements, and expanding the range of samples evaluated. Our findings provide insights into the critical physicochemical properties influencing ASD stability, offering a significant advancement in developing stable ASDs.

Correlation of physicochemical properties with antioxidant activity in phenol and thiophenol analogues

Oxidative stress, associated with excessive production of reactive oxygen and nitrogen species (ROS, RNS), contributes to the development and progression of many ailments, such as aging, cardiovascular diseases, Alzheimer's disease, Parkinson's disease, diabetes, cancer, preeclampsia or multiple sclerosis. While phenols and polyphenols are the most studied antioxidants structurally similar compounds such as anilines or thiophenols are sporadically analyzed despite their radical scavenging potential. This work assesses the impact of structural features of phenols and thiophenols on their antioxidant activity. Seventeen pairs of phenol/thiophenol analogues, possessing both electron-donating and withdrawing groups were selected for this study. Several physicochemical properties of the compounds were determined by density functional theory (DFT) calculations at the (U)B3LYP/6-311++G(d,p) level of theory for gas phase calculations and at the (U)B3LYP/6-311++G(d,p) scrf = (smd, solvent = water) level for the solvated ones. Correlations between calculated properties and experimental radical scavenging activities were investigated to identify the pivotal physical characteristics contributing to antioxidant efficiency. These include S-H and O-H bond distances and bond dissociation enthalpies (BDE), dipole moments, logP values, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) orbital energies, and the HOMO-LUMO gap energies that were calculated at the M06-2X/6-311++G(d,p) level of theory, and Fukui functions. The experimental activity was evaluated using the 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) and 2,2-diphenyl-1picrylhydrazyl (DPPH) radical scavenging assays. Several compounds exhibited superior scavenging abilities, surpassing that of the reference antioxidant Trolox. The extensive DFT calculations revealed that in the gas phase, lower BDE values, compared to IP and PA, suggested that the HAT mechanism predominates in case of these compound groups. In contrast, in water, significant reductions in PA due to solvent effects suggested that the SPLET mechanism is dominant under aqueous conditions.

Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy

Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.

Repurposing FDA-approved drugs for COVID-19: targeting the main protease through multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has created an urgent need for effective therapeutic agents. The SARS-CoV-2 Main Protease (Mpro) plays a crucial role in viral replication and immune evasion, making it a key target for drug development. While several studies have explored Mpro inhibition, identifying FDA-approved drugs with potential efficacy remains a critical research focus.

PURPOSE

This study aims to identify FDA-approved drugs that could inhibit SARS-CoV-2 Mpro. Using computational screening, we seek compounds that share structural similarities with a known co-crystallized ligand (PRD_002214) and exhibit strong binding affinity to the enzyme, providing viable candidates for COVID-19 treatment.

RESEARCH DESIGN

A systematic in silico approach was used, screening 3009 FDA-approved drugs. The initial screening focused on structural similarity to PRD_002214 (PDB ID: 6LU7), followed by molecular docking studies to predict binding affinity. Promising compounds were further analyzed through molecular dynamics (MD) simulations to evaluate their stability and interactions with Mpro over 100 ns.

STUDY SAMPLE

Of the 3009 FDA-approved drugs screened, 74 were selected for initial evaluation. After refinement, 28 compounds underwent docking analysis, with eight showing strong binding potential to Mpro.

ANALYSIS

Molecular docking assessed the binding affinity and interaction of the selected compounds with Mpro. MD simulations were conducted on the top compound, Atazanavir, to study its dynamic interactions. MM-GBSA, PLIP, and PCAT analyses were used to validate binding affinity and interactions.

RESULTS

Eight compounds, including Carfilzomib, Atazanavir, Darunavir, and others, exhibited promising binding affinities. Among them, Atazanavir showed the highest binding strength and was selected for further MD simulation studies. These simulations revealed that Atazanavir forms stable interactions with Mpro, demonstrating favorable binding and dynamic stability. The binding affinity was further confirmed through MM-GBSA, PLIP, and PCAT analyses, supporting Atazanavir's potential as an effective Mpro inhibitor.

CONCLUSIONS

In silico results suggest that Atazanavir is a promising candidate for targeting SARS-CoV-2 Mpro, with strong binding affinity and dynamic stability. These findings support its potential as a lead compound for further preclinical and clinical testing, though in vitro and in vivo validation are needed to confirm its therapeutic efficacy against COVID-19.

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape

Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔGTOTAL = - 30.78 kcal/mol) and 74977521 (ΔGTOTAL = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.

New binary mixtures of fungicides against Macrophomina phaseolina: Machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation

Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q2LMO of 0.81, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts.

Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (ΔG1, ΔG2, and ΔG3) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased ΔG1 and δ by 2.2 and 15.1%, respectively, while ΔG2 and ΔG3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru

BACKGROUND

Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru.

METHODS

A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model.

RESULTS

An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness.

CONCLUSIONS

Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction

Quantitative structure-activity relationship (QSAR) analysis, an in silico methodology, offers enhanced efficiency and cost effectiveness in investigating anti-inflammatory activity. In this study, a comprehensive comparative analysis employing four machine learning algorithms (random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and artificial neural networks (ANNs)) was conducted to elucidate the activities of naturally derived compounds from durian extraction. The analysis was grounded in the exploration of structural attributes encompassing steric and electrostatic properties. Notably, the nonlinear SVR model, utilizing five key features, exhibited superior performance compared to the other models. It demonstrated exceptional predictive accuracy for both the training and external test datasets, yielding R2 values of 0.907 and 0.812, respectively; in addition, their RMSE resulted in 0.123 and 0.097, respectively. The study outcomes underscore the significance of specific structural factors (denoted as shadow ratio, dipole z, methyl, ellipsoidal volume, and methoxy) in determining anti-inflammatory efficacy. Thus, the findings highlight the potential of molecular simulations and machine learning as alternative avenues for the rational design of novel anti-inflammatory agents.

Prediction of Hydrogen Abstraction Rate Constants at the Allylic Site between Alkenes and OH with Multiple Machine Learning Models

Hydrogen abstraction reactions between hydrocarbons and hydroxyl radicals are important propagation steps in radical chain reactions, playing a crucial role in atmospheric and combustion chemistry. This study focuses on predicting the rate constants of the prototype of the reaction class of hydrogen abstractions, i.e., the primary allylic hydrogen abstraction from alkenes by the OH radical, via utilizing machine learning (ML) methods. Specifically, three distinct models, namely, feedforward neural network (FNN), support vector regression (SVR), and Gaussian process regression (GPR), have been employed to construct robust ML models for prediction. We proposed a novel strategy that seamlessly integrates descriptor preprocessing, a pairwise linear correlation analysis, and a model-specific Wrapper method to enhance the effectiveness of the feature selection procedure. The selected feature subset was then evaluated using two cross-validation techniques, i.e., leave-one-group-out (LOGO) and K-fold cross-validations, for each of the three ML models (FNN, SVR, and GPR) to assess their predictive and stability performance. The results demonstrate that the FNN model, trained with seven representative descriptors, achieves superior performance compared to the other two methods. For the FNN model, the average percentage deviation is 39.06% on the test set by performing LOGO cross-validation, while the repeated 10-fold cross-validation achieves a percentage prediction deviation of 19.1%. Two larger alkenes with 10 carbons were selected to test the prediction performance of the trained FNN model on primary allylic hydrogen abstraction. Results show that the kinetic predictions follow well the modified three-parameter Arrhenius equation, indicating the reliable performance of FNN in predicting hydrogen abstraction rate constants, especially for the primary allylic site. Hopefully, this work can shed useful light on the application of ML in generating chemical kinetic parameters of hydrocarbon combustion chemistry.

Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2.

OBJECTIVES

The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach.

METHODS

The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds.

RESULTS

Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside.

CONCLUSION

The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.

Chemoinformatic modelling of the antioxidant activity of phenolic compounds

BACKGROUND

Antioxidants are chemicals used to protect foods from deterioration by neutralizing free radicals and inhibiting the oxidative process. One approach to investigate the antioxidant activity is to develop quantitative structure-activity relationships (QSARs).

RESULTS

A curated database of 165 structurally heterogeneous phenolic compounds with the Trolox equivalent antioxidant capacity (TEAC) was developed. Molecular geometries were optimized by means of the GFN2-xTB semiempirical method and diverse molecular descriptors were obtained afterwards. For model development, V-WSP unsupervised variable reduction was used before performing the genetic algorithms-variable subset selection (GAs-VSS) to construct the best five-descriptor multiple linear regression model. The coefficient of determination and the root mean square error were used to measure the performance in calibration (R²  = 0.789 and RMSEC = 0.381), and test set prediction (Q²  = 0.748 and RMSEP = 0.416), along several cross-validation criteria. To thoroughly understand the TEAC prediction, a fully explained mechanism of action of the descriptors is provided. In addition, the applicability domain of the model defined a theoretical chemical space for reliable predictions of new phenolic compounds.

CONCLUSION

This in silico model conforms to the five principles stated by the Organisation for Economic Co-operation and Development. The model might be useful for virtual screening of the antioxidant chemical space and for identifying the most potent molecules related to an experimental measurement of TEAC activity. In addition, the model could assist chemists working on computer-aided drug design for the synthesis of new targets with improved activity and potential uses in food science. © 2023 Society of Chemical Industry.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal “leave some out” test. The developed model could be utilized in future preclinical experiments with novel drugs.

Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models

Quantitative Structure-Activity Relationship (QSAR) models are commonly used for risk assessment of emerging contaminants. The objective of this study was to use a toxicity rank order (TRO) as an integrating parameter to improve the toxicity prediction by QSAR models. TRO for each contaminant was calculated from collected toxicity data including acute toxicity concentration and no observed effect concentration. TRO values associated with toxicity mechanisms were used to classify pollutants into three modes of action consisting of narcosis, transition and reactivity. The selection principle of parameters for QSAR models was established and verified. It showed a reasonable prediction of toxicities caused by organophosphates and benzene derivatives, especially. Compared with traditional procedures, incorporating TRO showed an improved correlation coefficient of QSAR models by approximately 10%. Our study indicated that the proposed procedure can be used for screening modeling parameter data and improve the toxicity prediction by QSAR models, and this could facilitate prediction and evaluation of environmental contaminant toxicity.

Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'-o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs

As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (2'OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2'OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2'OMTase-Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2'OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of -43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2'OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.