QSAR Approach Research Articles

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

Density Functional Theory (DFT) Study on the Nontoxic Alternative of Bisphenol A (BPA) Derivatives: A Comprehensive Review

AbstractBisphenol A is an oil‐derived, large market volume chemical with a wide spectrum of applications in plastics, adhesives, and thermal papers. However, bisphenol A and its derivative are not considered safe due to its endocrine disrupting properties and reproductive toxicity. A nontoxic alternative of bisphenol analogus has been proposed in this study using plant biomass. A study of different DFT‐based QSAR approaches has been done to show the significance of the conceptual DFT‐based selected descriptors with different QSAR models in the prediction of toxicity and to establish meaningful correlations between the molecular structure of the proposed compounds and their toxicological properties. Multiple regression analysis and ANN model are also suggested to use to relate the biological activity with the global and local reactivity descriptors.

Workflow for predictive risk assessments of UVCBs: cheminformatics library design, QSAR, and read-across approaches applied to complex mixtures of metal naphthenates.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50mg/kg/day. These values are in good agreement with findings from studies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment.

In-silico screening of Staphylococcus aureus antibacterial agents based on Isatin scaffold: QSAR, molecular docking, molecular dynamics simulation and DFT analysis

The design of novel antimicrobial drugs is necessary due to the increasing resistance to many existing antibiotics. Considering that the drug discovery process requires a lot of time and resources, computational chemistry and molecular docking as the basic components in the drug discovery process, can help the acceleration of this prolonged process and minimise the research costs. In this study, 64 molecules containing the Isatin scaffold were subjected to quantitative structure–activity relationship analysis to identify the necessary features for designing effective antimicrobial agents. Three chemometrics methods were employed to establish a connection between structural parameters and antibacterial effects. MLR was identified as the best model, demonstrating high accuracy compared to the other models. Based on the obtained model, ten novel lead compounds were designed, and their antibacterial potency of these compounds was assessed. The outputs of the molecular docking study revealed that compound S4 fits well in the ATP-binding site and blocks ATPase activity. Additionally, it was observed that compound S4 exhibited adequate binding affinity against the DNA gyrase target. An appropriate relationship was observed between QSAR, DFT and molecular modelling approaches. Therefore, compound S4 can be considered as a promising candidate for further development as an antibacterial agent.

Identification of Novel Plant-derived Inhibitors of the EGFR Kinase Domain Using vHTS, QSAR and Molecular Docking Approaches

Identification of Novel Plant-derived Inhibitors of the EGFR Kinase Domain Using vHTS, QSAR and Molecular Docking Approaches

Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management

ABSTRACT Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

2,4,6-Trimethoxy chalcone derivatives: an integrated study for redesigning novel chemical entities as anticancer agents through QSAR, molecular docking, ADMET prediction, and computational simulation

QSAR, an efficient and successful approach for optimizing lead compounds in drug design, was employed to study a reported series of compounds derived from 2,4,6-trimethoxy chalcone derivatives. The ability of these compounds to inhibit CDK1 was examined, with the help of QSARINS software for model development. The generated QSAR model revealed three significant descriptors, exhibiting strong correlations with impressive statistical values: cross-validation leave-one-out correlation coefficient (Q 2LOO) = 0.6663, coefficient of determination (R 2) = 0.7863, external validation coefficient (R 2 ext) = 0.7854, cross-validation leave-many-out correlation coefficient (Q 2LMO) = 0.6256, Concordance Correlation Coefficient for cross-validation (CCCcv) = 0.8150, CCCtr = 0.8804, and CCCext = 0.8750. From the key structural findings and the insights gained from the descriptors, ETA_dPsi_A, WTPT-5, and GATS7s, new lead molecules were designed. The designed molecules were then evaluated for their CDK1 inhibitory activity using the three-descriptor model developed in this study. To evaluate their drug likeliness, in-silico ADMET predictions were made using Schrodinger’s Software. Molecular docking was carried out to determine the interactions of designed compounds with the target protein. The designed compounds having excellent binding pocket molecular stability and anticancer effectiveness was substantiated by the findings of the molecular dynamics simulation. The results of this work point out important properties and crucial interactions necessary for efficient protein inhibition, suggesting lead candidates for further development as novel anticancer agents. Communicated by Ramaswamy H. Sarma

Prediction of selected properties of aflatoxin molecules by the QSAR method

Abstract Aflatoxins are naturally occurring compounds produced by fungi, mainly of Aspergillus species. All aflatoxins are proved to cause acute toxicity to human health, some even causing chronic diseases such as cancer. These molecules clearly proved that even natural molecules can be directly related to cancer and the formation of tumours. In general, aflatoxins can be characterised as organic compounds; B1, G1, B2, and G2 are produced directly by fungi and these four are subsequently metabolised in biological systems of microbes, animals or humans into other forms, such as M1, EB1, AFL etc. This manuscript provides a brief overview of 14 aflatoxins, their molecular structure and its possible relationship to aflatoxins biological activity. This information, in combination with additional calculations, offers the possibility to investigate the mentioned compounds and their properties using the QSAR approach.

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

Enhancing cancer drug development with xanthone derivatives: A QSAR approach and comparative molecular docking investigations

Xanthone compounds are known to have diverse pharmacological activities, including anti-inflammatory, anticancer properties. However, they face challenges such as poor water solubility and a tendency to convert into phase 2 conjugates. Therefore, new strategies are needed to develop improved derivatives. Thus, this study aims to establish a mathematical 2D-QSAR model to predict the pGI50 values of potential xanthone derivatives against breast cancer. For this study, the anti-breast cancer activity (pGI50) values of 27 xanthone derivatives tested against MCF-7 cell lines were retrieved from the National Cancer Institute (NCI) data library. Regression analysis was performed using various physiochemical and Density Functional Theory (DFT)–based descriptors to model a 2D-QSAR equation. The model was internally and externally validated, and the most reactive xanthone derivatives were identified. Based on the QSAR equation, 10 new molecules were computationally designed, and their pGI50 values were determined. The applicability domain (AD) was defined, and the Y-randomization test confirmed the validity and reliability of the QSAR model. DFT calculations were carried out for these molecules, of which Mol 1, Mol 2, Mol 8, and Mol 10 exhibited high reactivity. However, it was found that only Mol 8 fell within the AD, making it suitable for further investigation through molecular docking studies involving cancer proteins. During the molecular docking analyses, the binding energy and interactions between Mol 8 and the target proteins were evaluated. The results showed that Mol 8 exhibited superior binding energy and interactions compared to the native ligands. Additionally, Mol 8 demonstrated more favorable ADMET properties than the native ligands. These findings suggest that Mol 8 holds promise for designing novel and more potent compounds due to its advantageous characteristics and interactions with the target proteins.

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2 LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph’s topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer. Communicated by Ramaswamy H. Sarma

Synthesis and in vitro Activity of Eugenyl Benzoate Derivatives as BCL-2 Inhibitor in Colorectal Cancer with QSAR and Molecular Docking Approach.

This study is aimed to acquiring new compounds of Eugenyl benzoate (2-methoxy-4-(prop-2-en-1-yl)phenyl benzoate) derivatives that can inhibit HT29 colorectal cancer cells. In this research, we used several chemical reactions to synthesize novel compounds, such as Esterification, Demethylation, Halohydrin, and Sharpless reaction. Cytotoxicity assays were performed to test the inhibitory activity of compounds against HT29 colon cancer cells. QSAR analysis were carried out to analyse the relationship of chemical structure of the novel compounds with their cytotoxic activity. Ten novel compounds were successfully synthesized and tested in vitro against the HT29 cell. The IC50 of the novel compounds were between 26.56 µmol/ml - 286.81 µmol/ml which compound 4-[(2S)-2,3-dihydroxypropyl]-2-methoxyphenyl 2-hydroxybenzoate (9) showed as best active compound as BCL-2 inhibitors better than other synthesized compounds and Eugenol as well. QSAR analysis of the in vitro results gave a Log equation: 1/IC50 = -0.865-0.210 (LogP)2 + 1,264 (logP)-0.994 CMR (n = 10; r = 0.706; SE: 0.21; F = 0.497, sig = 7.86). The equation shows the log variable P and CMR affect IC50. The properties of hydrophobicity (log P) are more instrumental than the ones of steric (CMR). The active compound (9) given best activities as BCL-2 inhibitors than other eugenol derivatives. QSAR indicates the logP and CMR have effect on its colorectal cytotoxic activity which the hydrophobicity parameter (logP) plays more role than the steric parameter (CMR).

Bioactive Xanthone C-glycoside Derivatives – QSAR Approach

Introduction. Xanthone glycosides have unique structures and properties. Many efforts focus on the search for C-glycoside derivatives of mangiferin with higher bioavailability. The application of the QSAR approach allows for the optimization of the search for novel xanthone derivatives with the desired characteristics.Aim. Using available descriptors of chemical structure, physical-chemical properties, and biological activity, analyze a sample set of known homologs and analogs of mangiferin to QSAR prognosis bioactivity of new xanthone C-glycosides.Materials and methods. 26 molecules of natural homologs and modified derivatives of mangiferin formed the analyzed sample set. Topological graphs of compounds were constructed using ChemicPen software. ChemicDescript software was used for the calculation of molecular descriptors, including the Balaban index. Physicochemical characteristics of molecules as well as Lipinski's rule criteria were calculated in Molinspiration. The spectrum of the most probable (Pa > 0.7) biological activity of the described compounds were predicted using Pass Online. The software Origin (OriginLab, USA) was used for the graphical representation of the results.Results and discussion. Mangiferin and its natural homologs are the most hydrophilic compounds. The hydrolysis of the C-glycosidic bond, alkylation, acylation, and the introduction of an amino substituent radical into the mangiferin structure led to the increase of its lipophilic properties. The spectrum of the most probable biological activities of the described molecules: antitumor, antioxidant, and cardioprotective effects. The results of ADMET modeling based on the substance-drug similarity criteria showed that only 4 compounds correspond to the rule of five. We proposed the validation model to predict bioactivity from lipophilicity and molecule structure described with Balaban index. The error of prediction obtained in a result of cross-validation turned out to be about less than 3 %.Conclusion. A correlation between the structure and properties of the molecules discussed has been demonstrated. The obtained results can be used for further prediction of the properties of natural and synthetic xanthone C-glycosides and directed synthesis of new active compounds.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach

BackgroundAbuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data. MethodsIn this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods. ResultsThe best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R2 value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%. ConclusionsOur study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.

The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.

In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.

Deciphering the toxicity mechanism of haloquinolines on Chlorella pyrenoidosa using QSAR and metabolomics approaches

The hazardous potential of haloquinolines (HQLs) is becoming an issue of great concern due to its wide and long-term usage in many personal care products. We examined the growth inhibition, structure-activity relationship, and toxicity mechanism of 33 HQLs on Chlorella pyrenoidosa using the 72-h algal growth inhibition assay, three-dimensional quantitative structure–activity relationship (3D-QSAR), and metabolomics. We found that the IC50 (half maximal inhibitory concentration) values for 33 compounds ranged from 4.52 to > 150 mg·L−1, most tested compounds were toxic (1 mg·L−1 < IC50 < 10 mg·L−1) or harmful (10 mg·L−1 < IC50 < 100 mg·L−1) for the aquatic ecosystem. Hydrophobic properties of HQLs dominate their toxicity. Halogen atoms with large volume appear at the 2, 3, 4, 5, 6, and 7-positions of the quinoline ring to significantly increase the toxicity. In algal cells, HQLs can block diverse carbohydrates, lipids, and amino acid metabolism pathways, thereby resulting in energy usage, osmotic pressure regulation, membrane integrity, oxidative stress disorder, thus fatally damaging algal cells. Therefore, our results provide insight into the toxicity mechanism and ecological risk of HQLs.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Ligand-based pharmacophore modeling and QSAR approach to identify potential dengue protease inhibitors.

The viral disease dengue is transmitted by the Aedes mosquito and is commonly seen to occur in the tropical and subtropical regions of the world. It is a growing public health concern. To date, other than supportive treatments, there are no specific antiviral treatments to combat the infection. Therefore, finding potential compounds that have antiviral activity against the dengue virus is essential. The NS2B-NS3 dengue protease plays a vital role in the replication and viral assembly. If the functioning of this protease were to be obstructed then viral replication would be halted. As a result, this NS2B-NS3 proves to be a promising target in the process of anti-viral drug design. Through this study, we aim to provide suggestions for compounds that may serve as potent inhibitors of the dengue NS2B-NS3 protein. Here, a ligand-based pharmacophore model was generated and the ZINC database was screened through ZINCPharmer to identify molecules with similar features. 2D QSAR model was developed and validated using reported 4-Benzyloxy Phenyl Glycine derivatives and was utilized to predict the IC50 values of unknown compounds. Further, the study is extended to molecular docking to investigate interactions at the active pocket of the target protein. ZINC36596404 and ZINC22973642 showed a predicted pIC50 of 6.477 and 7.872, respectively. They also showed excellent binding with NS3 protease as is evident from their binding energy of -8.3and -8.1kcal/mol, respectively. ADMET predictionsofcompounds have shown high drug-likeness. Finally, the molecular dynamic simulations integrated with MM-PBSA binding energy calculations confirmedboth identified ZINC compounds as potential hit moleculeswith good stability.

Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

Identification of key structural features of meprin β inhibitors by ML models and binding mode of interaction analysis.