Quantitative Structure-activity Relationship Studies Research Articles

Computer-Aided Design of Novel Active Components in Plant Protection

The production demands highly specific environmentally and toxicologically acceptable plant protection products are increasing. Computer-aided molecular design of new active components has a great deal in developing plant protection products to avoid that long-lasting and expensive process. Computational design of future compounds and their synthesis, evaluation of their effectiveness on harmful and beneficial organisms in the soil, as well as detailed research mechanism of action at the molecular level, represents an initial stage in the long-lasting and expensive process of plant protection products. In this paper, the recent advances in quantitative structure-activity relationship (QSAR) studies, molecular docking, and calculation of “Pesticidelikeness properties “, as well, have been reviewed. QSAR models for antifungal activities against phytopathological fungi were obtained for the thiazoline and coumarine derivatives, coumarinyl Schiff bases, and coumarin-1,2,4-triazoles. A molecular docking study revealed that antifungal activities of fluorinated pyrazole aldehydes are related to the inhibition of proteinase K, coumarinyl Schiff bases with endoglucanase and pectinase, hybrids of coumarins and 1,2,4-triazoles with sterol 14α-demethylase inhibition, 3-gydroxycoumarin chitin synthase, while γ-thionins strongly binds to fungal membrane moieties.

Design, Synthesis, and Biological Activity of Novel Triketone-Containing Phenoxy Nicotinyl Inhibitors of HPPD.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a crucial target enzyme in albino herbicides. The inhibition of HPPD activity interferes with the synthesis of carotenoids, blocking photosynthesis and resulting in bleaching and necrosis. To develop herbicides with excellent activity, a series of 3-hydroxy-2-(6-substituted phenoxynicotinoyl)-2-cyclohexen-1-one derivatives were designed via active substructure combination. The title compounds were characterized via infrared spectroscopy, 1H and 13C nuclear magnetic resonance spectroscopies, and high-resolution mass spectrometry. The structure of compound III-17 was confirmed via single-crystal X-ray diffraction. Preliminary tests demonstrated that some compounds had good herbicidal activity. Crop safety tests revealed that compound III-29 was safer than the commercial herbicide mesotrione in wheat and peanuts. Moreover, the compound exhibited the highest inhibitory activity against Arabidopsis thaliana HPPD (AtHPPD), with a half-maximal inhibitory concentration of 0.19 μM, demonstrating superior activity compared with mesotrione (0.28 μM) in vitro. A three-dimensional quantitative structure-activity relationship study revealed that the introduction of smaller groups to the 5-position of cyclohexanedione and negative charges to the 3-position of the benzene ring enhanced the herbicidal activity. A molecular structure comparison demonstrated that compound III-29 was beneficial to plant absorption and conduction. Molecular docking and molecular dynamics simulations further verified the stability of the complex formed by compound III-29 and AtHPPD. Thus, this study may provide insights into the development of green and efficient herbicides.

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity.

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Design of acyl salicylic acid derivates as COX-1 inhibitors using QSAR approach, molecular docking and QSPR analysis

Background: Acetylsalicylic acid (aspirin), widely used as an antiplatelet agent, is more likely to inhibit COX-1. Along with discovering the cardioprotective role of COX-1 in controlling platelet aggregation, it is important to develop a selective COX-1 inhibitor. Objective: This study aims to design acyl salicylic acid derivatives intended as COX-1 inhibitors. Method: Fourteen derivatives (AcS1-14) were subjected to a quantitative structure-activity relationship (QSAR) study, and 31 QSAR models were obtained using multiple linear regression (MLR) analysis. Molecular docking was performed on COX-1 (PDB. 1PTH) using the Molecular Orbital Environment (MOE) program ver2022.02, and QSPR analysis was conducted to ascertain the contribution of physicochemical descriptors to the free energy score (S) of ligand-receptor complexes. Results: The QSAR-Hansch model predicted hydrophobicity (LogP) and molecular energy (Etotal) and contributed to pain inhibitory action. All derivatives displayed higher in silico affinity than aspirin (S= -4.33±0.00 kcal/mol), and compound AcS7 afforded the highest (S= -5.32 kcal/mol). In QSPR, Etotal also revealed a positive contribution to the affinity. AcS1, AcS2, AcS5, AcS7, and AcS8 expressed higher drug-like properties than aspirin. Conclusion: Derivatives with optimum hydrophobicity and high energy would generate potent COX-1 inhibition. The five selected compounds were recommended to be developed as drug candidates for COX-1 inhibitors.

Benchmarking of BMDC assay and related QSAR study for identifying sensitizing chemicals

The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle.This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method.The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.

Ligand-based Molecular Modeling of HDL Receptor SR-BI Inhibitors As Potent Anti-Hyperlipidemic Agents

Introduction:: The High-density lipoprotein (HDL) receptor, Scavenger receptor class B, type I (SRBI) plays a crucial role in lipoprotein metabolism, cholesterol homeostasis, and atherosclerosis. In the present study, a quantitative structure-activity relationship study (QSAR) investigation was conducted on a data set of 31 novel indolinyl thiazole-based inhibitors of SR-BI mediated lipid uptake. Method:: To build the QSAR model, Multiple linear regression analysis (MLR), partial least square analysis (PLS), and neural analysis (NN) were performed which were further evaluated internally as well as externally for the prediction of activity. The best QSAR model for MLR was selected with a correlation coefficient (r2) of 0.937, cross-validation r2cv of 0.908, and a standard error (S) value of 0.253. For PLS, r2 was 0.937 and for FFNN r2 was 0.961 (for the training set). This was further evaluated externally by a test set having r2 values 0.870 (MLR), 0.863(PLS), and 0.933(neural network) analysis. Result:: The final model comprised hydrophobic parameters (Lipole Z component) and steric parameters (molar refractivity and K alpha2 index). Conclusion:: All these descriptors generated comparable results which prove that the model generated is sound and has good predictability.

Metabolite Formation of Pesticide Isoprocarb in Coffee Beans During Short-Term Storage and Condition

Isoprocarb (IPC) is one of the most important carbamate pesticides for white flea control in coffee plants. The prevalence of isoprocarb pesticide residues in coffee, i.e., green coffee beans, is a cause for concern. Green coffee beans were intentionally contaminated with isoprocarb at concentrations >0.01 mg/kg, which was investigated in this research. Quantitative analysis of isoprocarb with the QuEChERS method and qualitative analysis for metabolite formation was characterized using UV-Vis, FTIR, 1H-NMR, GC-MS, and LC-QTOF-MS. Based on the data, the metabolite formed is o-cumenol through the hydrolysis reaction of o-aryl carbamate with the enzyme carboxylesterase. o-cumenol or 2- isopropylphenol as a phenol derivative. To verify the existence of metabolite analytes that are believed to be there in coffee tainted with isoprocarb, more reliable analytical techniques utilizing analytical standards must be developed. Isoprocarb concentrations decreased along the storage time, especially in unsterilized conditions. As a toxic compound, a quantitative structural activity relationship study (QSAR) was initially carried out through a software application for estimating toxicity (TEST) provided by the US Environmental Protection Agency (EPA). For additional information, based on the LC50 and LD50 data confirmed from the TEST application, it was concluded that isoprocarb is more toxic than o-cumenol.

Novel 5-Sulfonyl-1,3,4-thiadiazole-Substituted Flavonoids as Potential Bactericides and Fungicides: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship Studies.

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae, Rhizoctonia solani, and Colletotrichum orbiculare. Compounds 1, 3, 7, 9, 13, and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC50 values below 10 μg/mL, which were superior to bismerthiazol (70.89 μg/mL). Compound 2 (EC50 = 0.41 μg/mL) displayed the most effective inhibitory potency against R. solani in vivo, comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35-53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani, with an EC50 value of 0.28 μg/mL and a half-maximal effective concentration of 0.46 μg/mL.

Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies

ObjectivesSchistosomiasis, a neglected tropical disease, is a leading cause of mortality in affected geographic areas. Currently, because no vaccine for schistosomiasis is available, control measures rely on widespread administration of the drug praziquantel (PZQ). The mass administration of PZQ has prompted concerns regarding the emergence of drug resistance. Therefore, new therapeutic targets and potential compounds are necessary to combat schistosomiasis. MethodsTwenty-four potent derivatives of PZQ were optimized via density functional theory (DFT) at the B3LYP/6-31G∗ level. Quantitative structureactivity relationship (QSAR) models were generated and statistically validated, and a lead candidate was selected to develop therapeutic options with improved efficacy against schistosomiasis. The biological and binding energies of the designed compounds were evaluated. In addition, molecular dynamics; drug-likeness; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and DFT studies were performed on the newly designed compounds. ResultsFive QSAR models were generated, among which model 1 had favorable validation parameters (R2train: 0.957, R2adj: 0.941, LOF: 0.101, Q2cv: 0.906, and R2test: 0.783) and was chosen to identify a lead candidate. Other statistical parameters for the chosen model included variance inflation factor values ranging from 1.242 to 1.678, and a Y-scrambling coefficient (cRp2) of 0.747. Five new compounds were designed with improved predicted activity (ranging from 5.081 to 7.022) surpassing those of both the lead compound and PZQ (predicted pEC50 of 5.545). Molecular dynamics simulation revealed high binding affinity of the proposed compounds toward the target receptor. ADMET and drug-likeness assessments indicated adherence to Lipinski's rule of five criteria, thereby suggesting pharmacological and oral safety. In addition, DFT analysis indicated resistance to electronic alteration during chemical reactions. ConclusionThe proposed compounds exhibited potential drug characteristics, thus indicating their suitability for further investigation to enhance schistosomiasis treatment options.

In-silico design of novel potential HDAC inhibitors from indazole derivatives targeting breast cancer through QSAR, molecular docking and pharmacokinetics studies

Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via quantitative structure activity relationship (QSAR) and molecular docking simulations were adapted to 43 compounds of indazole derivatives with HDAC inhibition for anticancer activity against breast cancer. The QSAR models were built from multiple linear regression (MLR), and models predictability was cross-validated by leave-one-out (LOO) method. Based on these results, compounds C32, C26 and C31 from model 3 showed superior inhibitory activity with pIC50 of 9.30103, 9.1549 and 9.1549. We designed 10 novel compounds with molecular docking scores ranging from −7.9 to −9.3 kcal/mol. The molecular docking simulation results reveal that amino acid residues ILE1122 and PRO1123 play a significant role in bonding with 6CE6 protein. Furthermore, newly designed compounds P5, P2 and P7 with high docking scores of −9.3 kcal/mol, −8.9 kcal/mol and −8.8 kcal/mol than FDA-approved drug Raloxifene (-8.5 kcal/mol) and aid in establishment of potential drug candidate for HDAC inhibitors. The in-silico ADME functionality is used in the final phase to evaluate newly designed inhibitors as potential drug candidates. The results suggest that newly designed compounds P5, P2 and P7 can be used as a potential anti-breast cancer drug candidate.

QSAR modelling to predict structural features of certain sulfonamide as Urokinase-type Plasminogen Activator inhibitors

The serine hydrolase family includes serine proteases. It is essential for hydrolyzing protein peptide bonds and breaking them. The urokinase-type plasminogen activator (uPA) selectively binds to the uPAR on numerous cell types, including cancer cells. Pericellular proteolysis of cell-bound proteins requires this interaction. High uPA and uPAR levels regularly worsen cancer prognoses. Thus, small chemical active-site inhibitors that block uPA may diminish cancer cell invasion and metastasis. In compliance with Organization for Economic Corporation and Development guidelines, this research performed a complete Quantitative structure activity relationship analysis of sulfonamide compounds as Urokinase-type Plasminogen Activator inhibitors. Py-Descriptors were used for this investigation. PyDescriptor uses PyMOL standards and idioms to calculate 11,145 simple molecular descriptors. This plugin calculates molecular descriptors irrespective of molecular representation properties like atom numbering or labelling, spatial reference frame, translational and rotational invariance, etc. The investigation sought to find essential and hidden structural characteristics that regulate sulfonamide-type drugs' Urokinase-type Plasminogen Activator Inhibitory action. Twenty-eight sulfonamide chemicals are used in the Quantitative structure activity relationship study to generate statistically robust and highly predictive univariate and multivariate models. All models were thoroughly evaluated and meet several statistical parameter thresholds (e.g., R2 =0.9259–0.9280, Q2Loo= 0.8579–0.8558, Q2LMO= 0.8013–0.7865). The analysis reveal that occurance of ring carbon atoms exactly at 3 A0 from carbon atom, number of negatively charged atoms from sulphur atoms within 5 bonds, presence of hydrogen atom exactly at 3 bonds from donar atoms, presence of carbon atom exactly at 4 A0 from donar atom, presence of acceptor atom exactly at 5 A0 from sulphur atom and sum of partial charges of lipo atoms within 6 bonds from sulphur atom are important pharmacophoric features for Urokinase-type Plasminogen Activator Inhibition binding affinity. Thus, the developed Quantitative structure activity relationship study has an equilibrium of quantitative and qualitative tactics. The results could be useful for future optimizations of sulfonamide analogues.

Computation of Structural Descriptors of Pyrene Cored Dendrimers through Quotient Graph Approach and Its Graph Entropy Measures

Dendrimers are highly defined hyperbranched artificial macromolecules, synthesised by convergent or divergent approach with specific applications in various fields. Dendrimers can be represented as graph models, from which a quantitative description can be drawn in relation with their structural properties. The distance-based and the degree-based descriptors have great importance and huge applications in structural chemistry. These indices together with entropy measures are found to be more effective and have found application in scientific fields. The idea of graph entropy is to characterise the complexity of graphs. The use of these graph invariants in quantitative structure property relationship and quantitative structure activity relationship studies has become of major interest in recent years. In this paper, the distance-based molecular descriptors of pyrene cored dendrimers are studied applying the technique of converting original graph into quotient graphs using Θ∗-classes. It is to be noted that, since the pyrene cored dendrimer, Gn is not a partial cube, usual cut method is not applicable. Further, various degree-based descriptors and their corresponding graph entropies of the pyrene cored dendrimers are also studied. Based on the obtained results, a comparative analysis as well as a regression analysis was carried out.

DFT and QSAR study of Catechol-O-methyltransferase (COMT) as inhibitors for Parkinson’s disease treatment

Parkinson’s disease is characterized by a lack of the neurotransmitter or cell-signaling molecule dopamine. Levodopa is a well-known drug for Parkinson’s disease since it induces dopamine. Catechol-O-methyltransferase (COMT) are enzymes that break down levodopa, limiting the amount delivered to the brain. COMT inhibitors act by extending the duration of action of levodopa, thus improving the amount of time benefit from levodopa. There are several FDA-approved COMT inhibitors used, such as Entacapone and Tolcapone. Tolcapone can penetrate blood-brain barrier (BBB), but most of the drug stays in the plasma because its high protein bound, and it has severe side effects, while Entacapone cannot penetrate BBB, which reduces drug efficiency. This study aims to design higher-efficiency drug inhibitors by investigating the physical properties in terms of total energy, total dipole moment and HOMO/LUMO band gap at DFT: B3LYP level using the LAN2DZ basis set, in addition to quantitative structure activity relationship (QSAR) calculations to test the biological activity of these drug inhibitors for the treatment of Parkinson’s disease.

Pharmacophore & QSAR Guided Design, Synthesis, Pharmacokinetics, and in vitro Evaluation of Curcumin Analogs for Anticancer Activity.

As a part of our discovery of plant-based lead molecules, we provide a helpful tool, which helps in identification, designing, optimising, structural modifications, and prediction of curcumin, to discover novel analogs with enhanced bioavailability, pharmacologically safe, and anticancer potential. QSAR (Quantitative structure-activity relationship) and pharmacophore mapping models were developed and further used to design, synthesize, pharmacokinetics, and in vitro evaluation of curcumin analogs for anticancer activity. The QSAR model yielded a high activity-descriptors relationship accuracy (r2) of 84%, a high activity prediction accuracy (rcv2) of 81%, and external set prediction accuracy of 89%. The QSAR study indicates that the five chemical descriptors were significantly correlated with anticancer activity. The important pharmacophore features identified were a hydrogen bond acceptor, a hydrophobic centre, and a negative ionizable centre. The model's predictive ability was evaluated against a set of chemically synthesized curcumin analogs. Among the tested compounds, nine curcumin analogs were found with IC50 values of 0.10 to 1.86 µg/mL. The active analogs were assessed for pharmacokinetics compliance. EGFR was identified as a potential target of synthesized active curcumin analogs through docking studies. Integrating in silico design, QSAR-driven virtual screening, chemical synthesis, and experimental in vitro evaluation may lead to the early discovery of novel and promising anticancer compounds from natural sources. The developed QSAR model and common pharmacophore generation were used as a designing and predictive tool to develop novel curcumin analogs. This study may help optimize the therapeutic relationships of studied compounds for further drug development and their potential safety concerns. This study may guide compound selection and designing novel active chemical scaffolds or new combinatorial libraries of the curcumin series.

Topological analysis of tetracyanobenzene metal–organic framework

Metal–organic frameworks (MOFs) are vital in modern material science, offering unique properties for gas storage, catalysis, and drug delivery due to their highly porous and customizable structures. Chemical graph theory emerges as a critical tool, providing a mathematical model to represent the molecular structure of these frameworks. Topological indices/molecular descriptors are mathematical formulations applied to molecular models, enabling the analysis of physicochemical properties and circumventing costly lab experiments. These descriptors are crucial for quantitative structure-property and structure-activity relationship studies in mathematical chemistry. In this paper, we study the different molecular descriptors of tetracyanobenzene metal–organic framework. We also give numerical comparison of computed molecular descriptors.

QSAR and docking based lead optimization of nitrogen heterocycles for enhanced prostaglandin EP2 receptor agonistic potency

In the existing effort, a dataset of 309 experimentally screened molecules for in vitro (Ki) agonist potential for Prostaglandin E2 (PGE2) receptor 2 subtype (EP2), which is a metabolite of arachidonic acid that binds with and regulates cellular responses to PGE2, was investigated in the QSAR (Quantitative structure–activity relationship) study. A six-parameter QSAR model was developed that meets the specified values ​​for internal and external validation as well as random parameters such as R2tr = 0.808, Q2LMO = 0.794, R2ex = 0.781. Insightful and quantitative opinion reveals several underappreciated and distinct structural features that are responsible for the agonist potency of these molecules on Prostaglandin EP2 receptor such as; the hydrogen atom is correct 2 bonds from the donor atom, the sp2 hybridized carbon atom is correct 2 bonds from the cyclic nitrogen atom, and so on. The developed QSAR model captures the narrative as well as the novel pharmacophoric features. The QSAR effect was further demonstrated using the reported crystalline buildings of CP533536 with the Prostaglandin EP2 receptor activity. The evaluation led to the identification of valuable new pharmacophoric properties that will be used to optimize lead compounds in the future.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure–activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets’ stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO–LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive. Communicated by Ramaswamy H. Sarma

QSAR studies of quinoline alkaloids camptothecin derivatives for prediction anticancer activity using linear and nonlinear methods

AbstractQuinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC50) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC50. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC50 values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.

Computation of Wiener and Wiener Polarity Indices of a Class of Nanostar Dendrimer Using Vertex Weighted Graphs

Nanostar dendrimers are tree-like nanostructures with a well-defined, symmetrical architecture. They are built in a step-by-step, controlled synthesis process, with each layer or generation building on the previous one. Dendrimers are made up of a central core, a series of repeating units or branches, and a surface group shell. A weighted graph is a type of graph in which vertices or edges are assigned weights that represent cost, distance, and a variety of other relative measuring units. The weighted graphs have many applications and properties in a mathematical context. The topological indices are numerical values that represent the symmetry of a molecular structure. They have rich applications in theoretical chemistry. Various topological indices can be used to investigate a wide range of properties of chemical compounds with a molecular structure. They are very important in mathematical chemistry, especially in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. In this paper, we examine the topological properties of the molecular graphs of nanostar dendrimers. For this purpose, the topological indices, namely, the Wiener index and the Wiener polarity index are computed for a class of nanostar dendrimers.

QSAR of SARS-CoV-2 Main Protease Inhibitors Utilizing Theoretical Molecular Descriptors

Background: COVID-19 is caused by a novel strain of severe acute respiratory syndrome coronaviruses (SARS-CoV-2). It has claimed casualties around the world since the end of 2019 due to its high virulence and quick multiplicity in the human body. Hence, there has been a requirement to develop effective remedial measures to mitigate the mortality. Scientists have been able to develop corona vaccines to provide immunity, but there are no specific small-molecule chemotherapeutics to combat the novel coronavirus which has spread to the whole world due to its contagiousness. In the viral genome exploration, it has been found that the main protease, also known as chymotrypsin-like cysteine protease ([Mpro] or 3C-like protease [3CLpro]) is responsible for the novel coronavirus replication, transcription, and host immunity destruction. Objectives: Therefore, the main protease has been selected as one of the major targets for the design of new inhibitors. The protein crystallographic and molecular docking studies on SARS-CoV-2 Mpro inhibitors and some quantitative structure-activity relationship (QSAR) studies have been carried out on SARSCoV main protease inhibitors to get some lead molecules for SARS-CoV-2 inhibition. However, there is hardly any QSAR done on the diverse data of SARS-CoV-2 main protease inhibitors. In view of it, QSAR studies have been attempted on SARS-CoV-2 Mpro inhibitors utilizing theoretical molecular descriptors solely computed from the structures of novel corona viral main protease inhibitors. Methods: Methods: As the number of structural descriptors is more than the observations, a genetic algorithm coupled with multiple linear methods has been applied for the development of QSAR models taking diverse SARS-CoV-2 Mpro inhibitors. Results: The developed best QSAR model showing R2 , Q2 Loo, and R2 pred values of 0.7389, 0.6666, and 0.6453 respectively has been further validated on an external data set where a good correlation (r = 0.787) has been found. Conclusion: Therefore, this model may be useful for the design of new SARS-CoV-2 main protease inhibitors.