Quantitative Structure-activity Relationship Studies Research Articles

Quantitative structure-activity relantionship for thiazolidine-2,4-dione derivatives as inhibitory activities of 15-PGDH using MLR and ANN

Quantitative structure activity relationship studies were applied on a series of 22 molecules of thiazolidine-2,4-dione, The compounds used are potent inhibitors of the 15-hydroxyprostaglandin dehydrogenase (15-PGDH). The present study was performed using multiple regression analysis (MLR) and artificial neural network (ANN)to predict a QSAR model using molecular descriptors. Our results suggest QSAR model based of the following descriptors: polarizability (Pol), molar volume (MV), hydration energy (HE), Surface area grid (SAG), molar weight (MW), Energy of frontier orbital’s EHOMO (The Highest Occupied Molecular Orbital) and ELUMO (The Lowest Unoccupied Molecular Orbital) and atomic net charges (qN3, qC4, qC5 and qO7) for the inhibitory activities of 15-hydroxyprostaglandin dehydrogenase. The best predictive models by MLR and ANN methods gave highly significant square correlation coefficient (R2) values of 0.9623 and 0.9963 respectively. The model also exhibited good predictive power confirmed by the high value of R2pred (0.7839and 0.6324 respectively).

Preclinical Evaluation of an Imidazole-Linked Heterocycle for Alzheimer's Disease.

Humanity is facing a vast prevalence of neurodegenerative diseases, with Alzheimer's disease (AD) being the most dominant, without efficacious drugs, and with only a few therapeutic targets identified. In this scenario, we aim to find molecular entities that modulate imidazoline I2 receptors (I2-IRs) that have been pointed out as relevant targets in AD. In this work, we explored structural modifications of well-established I2-IR ligands, giving access to derivatives with an imidazole-linked heterocycle as a common key feature. We report the synthesis, the affinity in human I2-IRs, the brain penetration capabilities, the in silico ADMET studies, and the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of this new bunch of I2-IR ligands. Selected compounds showed neuroprotective properties and beneficial effects in an in vitro model of Parkinson's disease, rescued the human dopaminergic cell line SH-SY5Y from death after treatment with 6-hydroxydopamine, and showed crucial anti-inflammatory effects in a cellular model of neuroinflammation. After a preliminary pharmacokinetic study, we explored the action of our representative 2-(benzo[b]thiophen-2-yl)-1H-imidazole LSL33 in a mouse model of AD (5xFAD). Oral administration of LSL33 at 2 mg/Kg for 4 weeks ameliorated 5XFAD cognitive impairment and synaptic plasticity, as well as reduced neuroinflammation markers. In summary, this new I2-IR ligand that promoted beneficial effects in a well-established AD mouse model should be considered a promising therapeutic strategy for neurodegeneration.

Identification of new dihydrophenanthrene derivatives as promising anti-SARS-CoV-2 drugs through in silico investigations

To research, evaluate, and invent novel compounds that inhibit SARS-CoV-2 activity, a series of reported 39 substituted 9, 10-dihydrophenanthrene derivatives were subjected to a quantitative structure-activity relationship (QSAR) study. Gaussian 09 and ChemOffice programs were used to calculate the molecular descriptors employed to determine their impact on the studied activity. Then we reduced the number of descriptors by eliminating the redundant information using principal component analysis (PCA). The creation of molecular models was done by using multiple linear regression (MLR) according to the principles established by the Organization for Economic Co-operation and Development (OECD) and the validation by using external and internal validation, Y-randomization tests, and domain of applicability. Moreover, we evaluated the toxicity of developed compounds using ADMET and Molecular docking to determine their optimal position to form a stable complex. As a result, four molecules may be used to develop a novel drug that can inhibit SARS-CoV-2 without causing the side effect.

In silico identification of novel heterocyclic compounds combats Alzheimer’s disease through inhibition of butyrylcholinesterase enzymatic activity

Increasing evidence indicates that heterocyclic molecules possess properties against butyrylcholinesterase (BChE) enzymatic activity, which is a potential therapeutic target for Alzheimer's disease (AD). Thus, this study aimed to further evaluate the relationship between heterocyclic molecules and their biological activities. A dataset of 38 selective and potent heterocyclic compounds (−log[the half‑maximal inhibitory concentration (pIC50)]) values ranging from 8.02 to 10.05) was applied to construct a quantitative structure-activity relationship (QSAR) study, including Bayesian model average (BMA), artificial neural network (ANN), multiple nonlinear regression (MNLR), and multiple linear regression (MLR) models. Four models met statistical acceptance in internal and external validation. The ANN model was superior to other models in predicting the pIC50 of the outcome. The descriptors put into the models were found to be comparable with the target-ligand complex X-ray structures, making these models interpretable. Three selected molecules possess drug-like properties (pIC50 values ranged from 9.19 to 9.54). The docking score between candidates and the BChE receptor (RCSB ID 6EYF) ranged from −8.4 to −9.0 kcal/mol. Remarkably, the pharmacokinetics, biological activities, molecular dynamics, and physicochemical properties of compound 18 (C20H22N4O, pIC50 value = 9.33, oxadiazole derivative group) support its protective effects on AD treatment due to its non-toxic nature, non-carcinogen, cholinergic nature, capability to penetrate the blood-brain barrier, and high gastrointestinal absorption. Communicated by Ramaswamy H. Sarma

Interpretable QSAR Modelling and QSAR-Based Virtual Screening of Novel 3H-Thiazolo[4,5-b]pyridin-2-one Derivatives as Potential Antioxidant Drug Candidates

Quantitative structure-activity relationship (QSAR) study has been carried out for 32 N3 substituted 3H-thiazolo[4,5-b]pyridin-2-one derivatives as potential antioxidant drug candidates. The genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used as appropriate techniques for descriptors selection and correlation models generation. The four best regressions for the prediction of the ability to scavenge the DPPH radical were generated as three-parameter QSAR models with the highest statistical characteristics and predictive ability. Based on the validation parameters of the generated models, it may be stated that they all satisfy the statistical requirements for their goodness-of-fitting with no current overfitting. The predictive ability of the constructed models was assessed with both internal and external validation approach and estimated with the leave-one-out and leave-group-out cross-validation coefficients (Q2LOO and Q2LGO). The values of Q2LOO (0.7060  0.7480) and Q2LGO (0.6647  0.7711) are reasonable, showing that the models are significant and robust to predict the free radical scavenging activity of the compounds from both training and validation sets. Applicability domain defining technique was employed to the obtained models and it was indicated that most structures were adequately represented by the chemical space of the models.

A COMPARATIVE STUDY ON FEATURE SELECTION METHODS IN QSAR

A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. This paper studied the comparative analysis of the Chi-square, Mutual Information, Anova F-value, Fisher Score and SHAP feature selection methods used in QSAR modeling. The Python code written to get experimental results in this article has been uploaded to Github.

Design of Acetylcholinesterase Inhibitors as Promising Anti‐Alzheimer's Agents Based on QSAR, Molecular Docking, and Molecular Dynamics Studies of Liquiritigenin Derivatives

AbstractCholinesterase inhibitors are promising drugs for the symptomatic treatment of Alzheimer's disease. In the current study, we explored the quantitative structure activity relationship (QSAR) of a series of Liquiritigenin derivatives recently assessed for their anti‐acetylcholinesterase activity. The multiple linear regression was applied to establish a QSAR model relating the biological activities of the studied compounds to their molecular descriptors. The QSAR model quality was evaluated by adopting Golbraikh and Tropsha's criteria. The obtained results highlight the fact that the developed model is perfectly valid (MSE=0.038; R2=0.789; R2adj=0.747; Q2CV=0.670; F=18.751) and exhibit high predictive power (R2test=0.760). Besides, Balaban Index (J), total energy (ET), dipole moment (μ) and electrophilicity index (ω) were identified to be the most relevant descriptors governing the anti‐acetylcholinesterase activity. By analyzing these molecular features, new Liquiritigenin derivatives with enhanced anti‐acetylcholinesterase activity were designed. Moreover, both molecular docking and molecular dynamics were performed to support the obtained results. In addition, the pharmacokinetics properties corresponding to the designed molecules were estimated. As a result, the compounds D10 and D13 were found to possess good pharmacokinetics properties and predicted to form stable complexes with the acetylcholinesterase protein, supporting their use as anti‐Alzheimer drug candidates.

Exploring potential Plasmodium kinase inhibitors: a combined docking, MD and QSAR studies

Malaria is a disease caused mostly by Plasmodium falciparum, affects millions of people each year. The kinases are validated targets for malaria infection. In this study, we investigate for real and hypothetical compounds that can inhibit cyclic guanosine monophosphate (CGMP)-dependent protein kinase using molecular docking via combined similarity analysis, molecular dynamics simulations, quantitative structure activity relationship (QSAR). Using Tanimoto similarity scores, ∼8.4 million compounds were screened. Compounds that have at least 70% similarity are used in further analysis. These compounds are assessed by means of docking, MMBPSA, MMGBSA and ANI_LIE. Based on consensus of different free energy methods and docking we revealed two potential inhibitors that can be useful for treatment of malaria. Apart from screening of real compounds, we have also selected the 10 most plausible hypothetical compounds by performing QSAR. By QSAR proposed pharmacophores, we generated over 247 hypothetical compounds and among them 19 molecules with lower QSAR predicted IC50 values and high docking scores were selected for further analysis. We selected the top 10 inhibitor candidates and performed MD simulations for free energy calculations like the protocol applied for real compounds. According to the free energy calculations, we suggest 2 real (C34H29F5N8O4S and C30H27F2N7O2S2, PubChem IDs: 140564801 and 89035196, respectively) and 2 hypothetical (C23H27FN6O2S, MOL3 and C23H25FN6O2S, MOL4) compounds that can be effective inhibitors against the protein kinase of Plasmodium falciparum. Communicated by Ramaswamy H. Sarma

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.

In silico exploration of promising heterocyclic molecules against both acetylcholinesterase and butyrylcholinesterase enzymes

We aimed to further explore the relationship between heterocyclic molecules and their associated biological activities for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. A dataset of 36 heterocycles was used to predict the activity of AChE and BChE inhibitors (the pIC50 values ranged from 7.84 to 12.49). A quantitative structure-activity relationship (QSAR) study was generated with the help of four different models (BMA, MNLR, MLR, and ANN). Four of the models were statistically acceptable based on both internal and external validation. The descriptors used in the models were similar to the X-ray structures of the target-ligand complexes, which made it possible to predict the pIC50 for AChE and BChE enzymes. Five selected molecules (compounds 6 (C21H21F3N4O), compound 7 (C22H23F3N4O), and compound 8 (C22H23F3N4O2) belong to the oxadiazole derivative group; compound 16 (C17H13ClN2O3) is classified into the chemical structures of different N, O, and S-based heterocycle groups; and compound 25 (C19H17NO2) pertains to the pyrimidine derivative group) possessed high pIC50 values for AChE and BChE enzymes (pIC50 values for AChE and BChE ranged from 9.01 to 10.32). The range of docking scores between the AChE and BChE receptors and their respective candidates was from −8.1 to −9.2 kcal/mol. The pharmacokinetics, biological activities, and physicochemical properties of five selected compounds supported their ability to protect against AD because they are not toxic, have a cholinergic effect, can cross the blood-brain barrier, and are well absorbed by the gastrointestinal tract. Communicated by Ramaswamy H. Sarma

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.

Quantitative structure-activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning.

The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure-activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set, = 0.97 and RMSE = 0.01; in a test set, = 0.95, RMSE = 0.01, and = 0.96. This result is in line with the experimental expectations, which indicate that the MIX-SVR modeling approach has good applications in the study of amide derivatives.

Boron clusters sheets with algebraic properties

Boron clusters are small, three-dimensional structures made of boron atoms. These come in a wide range of shapes and sizes, from basic planar constructions to complex polyhedral patterns. Boron clusters are fascinating from both a fundamental and practical perspective because of their unique electrical, optical, and chemical properties. More focus has lately been placed on boron cluster sheets, which are two-dimensional structures made of boron clusters. Quantitative structure-activity relationship (QSAR) studies implement M-polynomials, a type of molecular descriptor, to describe the topology of molecules. These are based on the notion of graph theory, which provides a theoretical framework for quantitatively studying molecular graphs. In this article, we discussed some novel topological descriptors-based M-polynomials and found algebraic formulations for the boron cluster or borophene sheets. We discussed first Zagreb, second Zagreb and Randić M-polynomials, based on the different differential and integral operators.

2D QSAR STUDY OF FUSED 5,6-BICYCLIC HETEROCYCLES AS ANTI ALZHEIMER’S AGENTS USING TOPOLOGICAL PARAMETERS

Alzheimer’s disease (AD) is a degrading and irreversible neurodegenerative disorder associated with loss of brain functions. It is manifested clinically into psychological symptoms that collectively form cognitive dysfunction and the loss of thinking skills through the progressive degeneration of central nervous system neurons. Over the last decade, γ-secretase emerged as a promising target for the treatment of Alzheimer’s disease. The γ-secretase modulator compounds would be good candidates for AD therapeutics. In previous years, a series fused 5,6-bicyclic heterocycles were investigated as γ-secretase modulators or anti Alzheimer’s agents. In the present study, a quantitative structure activity relationship (QSAR) study of twenty-eight derivatives of fused 5,6-bicyclic heterocycles has been made with the help of topological parameters. The descriptors that have been used are solvent accessible surface area, valence connectivity indices of order 0, 1 and 2 and shape indices of order 1, 2 and 3. The best QSAR model for this set of derivatives has been obtained by using solvent accessible surface area as first descriptor, valence connectivity index (order-0) as second descriptor, valence connectivity index (order-1) as third descriptor and shape index (order-3) as fourth descriptor. The correlation coefficient (r 2 ) and cross validation coefficient (rCV2 ) for this model are 0.773037 and 0.688568, respectively.

DFT and QSAR studies of PTFE/ZnO/SiO2 nanocomposite

Polytetrafluoroethylene (PTFE) is one of the most significant fluoropolymers, and one of the most recent initiatives is to increase its performance by using metal oxides (MOs). Consequently, the surface modifications of PTFE with two metal oxides (MOs), SiO2 and ZnO, individually and as a mixture of the two MOs, were modeled using density functional theory (DFT). The B3LYPL/LANL2DZ model was used in the studies conducted to follow up the changes in electronic properties. The total dipole moment (TDM) and HOMO/LUMO band gap energy (∆E) of PTFE, which were 0.000 Debye and 8.517 eV respectively, were enhanced to 13.008 Debye and 0.690 eV in the case of PTFE/4ZnO/4SiO2. Moreover, with increasing nano filler (PTFE/8ZnO/8SiO2), TDM changed to 10.605 Debye and ∆E decreased to 0.273 eV leading to further improvement in the electronic properties. The molecular electrostatic potential (MESP) and quantitative structure activity relationship (QSAR) studies revealed that surface modification of PTFE with ZnO and SiO2 increased its electrical and thermal stability. The improved PTFE/ZnO/SiO2 composite can, therefore, be used as a self-cleaning layer for astronaut suits based on the findings of relatively high mobility, minimal reactivity to the surrounding environment, and thermal stability.

Isoprenylcysteine carboxyl methyltransferase inhibitors: QSAR, docking and molecular dynamics studies

A quantitative structure-activity relationship (QSAR) investigation was conducted to build models that correlate the structures of 273 compounds to their actions against Isoprenylcysteine Carboxyl Methyltransferase (ICMT) protein. Genetic Algorithm (GA) was used to select the final set of descriptors and create the correlation models that associate the structural characteristics of the chemicals to their biological activity. Three QSAR models (four descriptor based) were identified having coefficient of determination (R2) for the training set as 0.7183, 0.7358 and 0.7475 and for external validation set 0.813, 0.746 and 0.728. Artificial Neural Network(ANN) was used to establish the strength of the QSAR models. ANN model assessed the descriptor's contribution to the structure-activity association. The descriptors that formed the best models were used to form the ANN model. The correlation coefficient(R) of the ANN model was = 0.919. The MSE was 0.24 for the training set, 0.30 for the validation set and 0.15 for the test set. In accordance with the statistical findings, the QSAR models were significant and exhibited excellent stability with respect to data fluctuation in the validation technique. The docking study revealed the best molecule, which formed a hydrophobic contact and hydrogen bond with ICMT amino acid residues and had a docking affinity of -9.5 kcal/mol. Molecular dynamics studies were also performed to understand the interactions between the protein and the ligands. Ligand 264 complex trajectory showed stable behavior with lesser fluctuations and supported the molecular docking result. The results of the QSAR, molecular docking and dynamics study can be further analysed to develop specific inhibitors of ICMT.

Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression and structural relationships

Nanomaterials have been widely applied and developed due to its unique physicochemical characteristics, such as their small size. The environmental and biological effects caused by nanomaterials have raised concerns. In particular, some nanometal oxides have obvious biological toxicity and pose a major safety problem. The prediction model established by combining the expression levels of key genes with quantitative structure–activity relationship (QSAR) studies can predict the biotoxicity of nanomaterials by relying on both structural information and gene regulation information. This model can fill the gap of missing mechanisms in QSAR studies. In this study, we exposed A549 cells and BEAS-2B cells to 21 nanometal oxides for 24 h. Cell viability was assessed by measuring absorbance values using the CCK8 assay, and the expression levels of the Dlk1-Dio3 gene cluster were measured. By using the theoretical basis of the nano-QSAR model and the improved principles of the SMILES-based descriptors to combine specific gene expression and structural factors, new models were constructed using Monte Carlo partial least squares (MC-PLS) for the biotoxicity of the nanometal oxides on two different lung cells. The overall quality of the nano-QSAR models constructed by combining specific gene expression and structural parameters for A549 and BEAS-2B cells was better than that of the models constructed based on structural parameters only. The coefficient of determination (R2) of the A549 cell model increased from 0.9044 to 0.9969, and the Root Mean Square Error (RMSE) decreased from 0.1922 to 0.0348. The R2 of the BEAS-2B cell model increased from 0.9355 to 0.9705, and the RMSE decreased from 0.1206 to 0.0874. The model validation proved the proposed models have a good prediction, generalization ability and model stability. This study offers a new research perspective for the toxicity assessment of nanometal oxides, contributing to a more systematic safety evaluation of nanomaterials.

Novel Degree-Based Topological Descriptors of Fenofibrate Using M-Polynomial

Chemical graph theory is currently expanding the use of topological indices to numerically encode chemical structure. The prediction of the characteristics provided by the chemical structure of the molecule is a key feature of these topological indices. The concepts from graph theory are presented in a brief discussion of one of its many applications to chemistry, namely, the use of topological indices in quantitative structure-activity relationship (QSAR) studies and quantitative structure-property relationship (QSPR) studies. This study uses the M-polynomial approach, a newly discovered technique, to determine the topological indices of the medication fenofibrate. With the use of degree-based topological indices, we additionally construct a few novel degree based topological descriptors of fenofibrate structure using M-polynomial. When using M-polynomials in place of degree-based indices, the computation of the topological indices can be completed relatively quickly. The topological indices are also plotted. Using M-polynomial, we compute novel formulas for the modified first Zagreb index, modified second Zagreb index, first and second hyper Zagreb indices, SK index, S K 1 index, S K 2 index, modified Albertson index, redefined first Zagreb index, and degree-based topological indices.

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.