3D-QSAR Models Research Articles

Design of potential anti-cancer agents as COX-2 inhibitors, using 3D-QSAR modeling, molecular docking, oral bioavailability proprieties, and molecular dynamics simulation.

Modeling the structural properties of novel morpholine-bearing 1, 5-diaryl-diazole derivatives as potent COX-2 inhibitor, two proposed models based on CoMFA and CoMSIA were evaluated by external and internal validation methods. Partial least squares analysis produced statistically significant models with Q 2 values of 0.668 and 0.652 for CoMFA and CoMSIA, respectively, and also a significant non-validated correlation coefficient R² with values of 0.882 and 0.878 for CoMFA and CoMSIA, respectively. Both models met the requirements of Golbraikh and Tropsha, which means that both models are consistent with all validation techniques. Analysis of the CoMFA and CoMSIA contribution maps and molecular docking revealed that the R1 substituent has a very significant effect on their biological activity. The most active molecules were evaluated for their thermodynamic stability by performing MD simulations for 100 ns; it was revealed that the designed macromolecular ligand complex with 3LN1 protein exhibits a high degree of structural and conformational stability. Based on these results, we predicted newly designed compounds, which have acceptable oral bioavailability properties and would have high synthetic accessibility.

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

“Design, Synthesis, and Molecular Docking Analysis of Novel Benzo[4,5]imidazo[1,2-a]pyrimidine Hybrids Targeting Plasmodium falciparum DHFR: A Comprehensive In Silico and Biological Study”

Synthesis of Benzo[4,5]imidazo[1,2-a]pyrimidine derivatives using an environmentally friendly Biginelli-type reaction in ionic liquid DIPEAc was performed. Remarkably, this ionic liquid exhibits efficient recyclability over five cycles. Synthesized compounds underwent rigorous in vitro antimalarial efficacy screening, complemented by computational and in vitro studies, assessing their potential as Plasmodium falciparum dihydrofolate reductase (Pf-DHFR) inhibitors. A robust 3D-QSAR model was established and validated, elucidating structure-activity relationships. Estimated ADMET descriptors highlighted favorable pharmacokinetics, suggesting their role in new antimalarial drug development. This study exemplifies the synergy of sustainable synthesis, enzyme inhibition, and pharmacokinetic profiling, promising a transformative outlook for antimalarial innovation.

Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure–activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q2 ​= ​0.8735, R2(train) ​= ​0.9096) and ANN [3-2-1] (Q2 ​= ​0.8987, R2(train) ​= ​0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S ​+ ​E (Q2 ​= ​0.559, R2(train) ​= ​0.939) and CoMSIA_S ​+ ​E (Q2 ​= ​0.577, R2(train) ​= ​0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Development of Sustainable Insecticide Candidates for Protecting Pollinators: Insight into the Bioactivities, Selective Mechanism of Action and QSAR of Natural Coumarin Derivatives against Aphids.

Plants employ abundant toxic secondary metabolites to withstand insect attack, while pollinators can tolerate some natural defensive compounds. Coumarins, as promising green alternatives to chemical insecticides, possess wide application prospects in the crop protection field. Herein, the bioactivities of 30 natural coumarin derivatives against Aphis gossypii were assessed and revealed that 6-methylcoumarin exhibited potent aphicidal activity against aphids but displayed no toxicity to honeybees. Additionally, using biochemical, bioinformatic, and molecular assays, we confirmed that the action mode of 6-methylcoumarin against aphids was by inhibiting acetylcholinesterase (AChE). Meanwhile, functional assays revealed that the difference in action site, which located in Lys585 in aphid AChE (equivalent to Val548 in honeybee AChE), was the principal reason for 6-methylcoumarin being toxic to aphids but safe to pollinators. This action site was further validated by mutagenesis data, which uncovered how 6-methylcoumarin was unique selective to the aphid over honeybee or mammalian AChE. Furthermore, a 2D-QSAR model was established, revealing that the central structural feature was H3m, which offers guidance for the future design of more potent coumarin compounds. This work provides a sustainable strategy to take advantage of coumarin analogues for pest management while protecting nontarget pollinators.

Structural optimization of pyrrolopyrimidine BTK inhibitors based on molecular simulation.

BTK is a critical regulator involved in the proliferation, differentiation, and apoptosis of B cells. BTK inhibitors can effectively alleviate various diseases such as tumors, leukemia, and asthma. During this study, a range of novel BTK inhibitors were designed using 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. We selected 41 pyrrolopyrimidine derivatives as BTK inhibitors to structure a 3D-QSAR model. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were adopted to research the connection between the pharmacological activities and chemical structures of the compounds. The CoMFA model (q2= 0.519,R2= 0.971), CoMSIA model (q2= 0.512,R2= 0.990), and external validation demonstrated excellent predictive performance and reliability of the 3D-QSAR model. We designed eight novel molecules with higher inhibitory activities according to the three-dimensional equipotential fields and explored the interactions between the compounds and BTK by molecular docking, which showed that the novel molecules had higher binding affinities with BTK than the template molecule 18. Then, the results of molecular docking were further verified by MD simulation, which showed that amino acid residues such as Leu528, Val416, and Met477 played vital parts in the interaction, and the binding free energy analysis showed that the novel molecules had higher stability with BTK. Finally, the ADME/T properties were predicted for all of the novel compounds, and the results showed that the majority of them had favorable pharmacokinetic properties. Therefore, this study provides strong support for the development of novel BTK inhibitors.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q 2 = 0.7634, R 2 train = 0.8666) and ANN[4-3-1] (Q 2 = 0.8699, R 2 train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q 2 = 0.504, R 2 train = 0.805) and CoMSIA/SEDHA (Q 2 = 0.619, R 2 train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a–e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies. Communicated by Ramaswamy H. Sarma

QSAR Studies of Nitric Oxide Synthase Inhibitors

Background:: Nitric oxide is a free radical bioregulator controlling homeostasis, vasodilation, and inhibition of platelet aggregation, significantly implicated in the nervous and immune system functionality. In vivo it is produced by nitric oxide synthases (NOSs). Objective:: Overproduction of nitric oxide is linked to several inflammatory, immunological, and neurodegenerative diseases and for that, various compounds have been synthesized as inhibitors of NOSs. In this review, the QSAR analyses were summarized in a variety of compounds as potent inhibitors of NOSs, and the models derived through 1D, 2D and 3D QSAR analyses. Conclusion:: Ten groups of various NOS inhibitors and 17 1D, 2D, and 3D QSAR models and analyses were presented and discussed. A lack of hydrophobic terms was noticed in most of the cases. Chemical substituents were selected considering the increase either of the hydrophilicity and/or of hydrophobicity, bulkiness supported steric interactions, and point to potent inhibitors. CMR (Calculated Molar Refractivity) a steric variable, with a negative sign, underlines the critical effects participating on (in) an active site on the enzymes. Indicator variables imply the influence of specific structural moieties. Electronic parameters were found to be significant.

Screening of Oral Potential Angiotensin-Converting Enzyme Inhibitory Peptides from Zizyphus jujuba Proteins Based on Gastrointestinal Digestion In Vivo.

Plant proteins are a good source of active peptides, which can exert physiological effects on the body. Predicting the possible activity of plant proteins and obtaining active peptides with oral potential are challenging. In this study, the potential activity of peptides from Zizyphus jujuba proteins after in silico simulated gastrointestinal digestion was predicted using the BIOPEP-UWM™ database. The ACE-inhibitory activity needs to be further investigated. The actual peptides in mouse intestines after the oral administration of Zizyphus jujuba protein were collected and analyzed, 113 Zizyphus jujuba peptides were identified, and 3D-QSAR models of the ACE-inhibitory activity were created and validated using a training set (34 peptides) and a test set (12 peptides). Three peptides, RLPHV, TVKPGL and KALVAP, were screened using the 3D-QSAR model and were found to bind to the active sites of the ACE enzyme, and their IC50 values were determined. Their values were 6.01, 3.81, and 17.06 μM, respectively. The in vitro digestion stabilities of the RLPHV, TVKPGL, and KALVAP peptides were 82%, 90%, and 78%. This article provides an integrated method for studying bioactive peptides derived from plant proteins.

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.

3D-QSAR Combination with Molecular Dynamics Simulations to Effectively Design the Active Ryanodine Receptor Agonists against Spodoptera frugiperda.

Computer-aided molecular modeling was applied to design a series of Spodoptera frugiperda RyR agonists. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. MD simulations in the complex with S. frugiperda native, mutant RyR, and mammalian RyR1 under physiological conditions were used to validate the detailed binding mechanism. Binding free energy calculation by molecular mechanics generalized surface area (MM-GBSA) explained the role of key amino acid residues in ligand-receptor binding. Therefore, 14 new compounds were effectively designed and synthesized, and a bioassay indicated that compounds A-2 and A-3 showed comparable activity to that of chloranthraniliprole with LC50 values of 0.27, 0.18, and 0.20 mg L-1, respectively, against S. frugiperda. Most target compounds also displayed good activity against Mythinma separata at 0.1 mg L-1. Molecular docking and MM-GBSA calculations demonstrated that A-3 had a better binding capacity with native and mutant S. frugiperda RyRs.

Molecular design of environment-friendly amide herbicide substitutes with high efficacy, low phytotoxicity and medication safety

The primary goal of this investigation was to formulate an ecologically sustainable alternative to amide herbicides (AHs) characterized by robust herbicidal effectiveness, minimal corn phytotoxicity, and commendable pharmaceutical safety. We employed comparative molecular similarity index analysis (CoMSIA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, which systematically outlined parameters such as herbicidal effectiveness, corn phytotoxicity, and AHs biodegradability. Subsequently, after thorough evaluation, we carefully selected a group of fourteen stable AH-substitute compounds known for their safety and environmental compatibility, considering aspects like pharmacokinetics, toxicokinetics, functional properties, and environmental friendliness. This resulted in a significant increase in herbicidal effectiveness, ranging from 21.64% to 34.07%, alongside a decrease in corn phytotoxicity within the range of 12.19–20.87%. Furthermore, we achieved an improvement in biodegradability, measured within the spectrum of 4.92–9.40%. Importantly, these changes also correlated with the reduction of hepatotoxicity, mutagenicity, and cutaneous health risks. Finally, we delved into the mechanisms underlying the improved herbicidal effectiveness, reduced corn phytotoxicity, and enhanced biodegradability of AHs substitutes through molecular docking and analysis of amino acid interactions. The investigation concluded that non-covalent forces governing the interaction between AHs substitutes and receptor proteins are crucial in determining herbicidal effectiveness, corn phytotoxicity, and biodegradability. Specifically, Van der Waals and electrostatic forces emerged as key factors governing the binding affinities of AH molecules with receptor proteins, both before and after modification. In summary, this study introduces innovative approaches in the field of agricultural chemical weeding technology and provides a theoretical framework for the environmentally responsible management of AHs herbicides.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes’ binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. −87.437 ± 19.728 and −73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure–activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity. Communicated by Ramaswamy H. Sarma

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

3D QSAR study on substituted 1, 2, 4 triazole derivatives as anticancer agents by kNN MFA approach

Background and objectivesResearchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinalvalue. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method. MethodsUsing the molecular design suite, a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation. ResultsThe most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047––0.0780––0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities. ConclusionThe good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

QSAR, Molecular Docking & ADMET Studies of Pyrrolo[2,3-d] Pyrimidine Derivatives as CDK4 Inhibitors for the Treatment of Cancer

Background:: Cancer is caused by dysregulation of the cell cycle, which results in abnormal proliferation and the inability of cells to differentiate or die. Cyclins and cyclin-dependent kinases (CDK4) inhibitors are drugs that target a specific enzyme, CDK4 that controls cell cycle progression in cancer. Aim & Objective:: The aim of this study is to obtain an optimized pharmacophore of pyrrolo[2,3-d] pyrimidine as a CDK4 inhibitor using QSAR studies. This aids in determining the link between structure and activity in newly developed chemical entities (NCE’s). To perform molecular docking and ADMET analysis to determine the binding affinity and drug-likeness of NCE’s. Materials and Methods:: The Multiple linear regression approach (MLR) method was utilised to generate the QSAR Model using the programme QSARINS v.2.2.4. For molecular docking, the Autodock vina software was employed. While the Swiss ADME and ToxiM online tools were used to predict toxicity. Results and Discussion:: The best models generated for 2D QSAR had correlation coefficients of R2= 0.9247 & Q2= 0.924 and for 3D QSAR, coefficients were R2 = 0.9297 and Q2 = 0.876. A novel series of 68 derivatives was designed based on QSAR investigations. Molecule C-58 has shown maximum binding affinity in molecular docking as compared to the standard Ribociclib. result: For 2D QSAR, the best models produced has correlation coefficients of R2= 0.9247 and Q2= 0.924. The 3D-QSAR model obtained with R2= 0.9297 and Q2 = 0.876. Based on QSAR studies, a new series of 68 derivatives was generated Conclusion:: Fifteen compounds have shown potential as CDK4 inhibitors based on docking studies, pharmacokinetic behavior and toxicity profile. The maximum binding affinity was demonstrated by molecule C-58. other: N/A

Evaluation of dimedone-derived compounds as inhibitors against human colon cancer: Insights from 2D-QSAR, ADMET prediction, Osiris, Molinspiration, and molecular modeling

For this research, we performed a 2D-QSAR analysis on a set of 34 molecules derived from dimedone with inhibitory activity against human colon cancer (HT-29). The investigation incorporated principal component analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR), and artificial neural network (ANN). The evaluations of the QSAR models demonstrated high predictive power (R2MLR = 0.884; R2CV (MLR) = 0.86), (R2MNLR = 0.810; R2CV (MNLR) = 0.879), and (R2ANN = 0.9; R2CV (ANN) = 0.89). The reliability of the models was validated through internal, external, Y-randomization, and validation domain applicability assessments. Utilizing the QSAR model predictions, we designed four new molecular structures that exhibited superior inhibitory activity against the HT-29 human colon cancer cell line compared to the 34 previously tested compounds. Subsequently, we examined the ADMET predictions, Molinspiration, and Osiris properties of the four compounds. The results revealed excellent ADMET predictions and Molinspiration for all four compounds, while only one designed compound fulfilled all Osiris properties. Molecular docking was employed to investigate the bindings between the newly designed molecule C and the c-Met protein. The findings indicated that the newly designed compound exhibited high stability in c-Met. Finally, MD simulations were employed to assess the stability and binding modes of compound C. Based on the MD results, compound C shows promise as a potential c-Met agonist candidate. Overall, our results suggest that this investigated compound has the potential to serve as a novel inhibitor against human colon cancer.

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored. Communicated by Ramaswamy H. Sarma

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Developmental landscape of computational techniques to explore the potential phytochemicals from Punica granatum peels for their antioxidant activity in Alzheimer's disease.

Alzheimer's disease (AD) is more commonly found in women than in men as the risk increases with age. Phytochemicals are screened in silico from Punica granatum peels for their antioxidant activity to be utilized for Alzheimer's disease. Alzheimer's disease is inhibited by the hormone estrogen, which protects the brain from the bad effects of amyloid beta and acetylcholine (ACh), and is important for memory processing. For the purpose, a library of about 1,000 compounds from P. granatum were prepared and studied by applying integrated computational calculations like 3D-QSAR, molecular docking, MD simulation, ADMET, and density functional theory (DFT). The 3D-QSAR model screened the active compounds B25, B29, B35, B40, B45, B46, B48, B61, and B66 by the field points and activity atlas model from the prepared library. At the molecular level, docking was performed on active compounds for leading hit compounds such as B25 and B35 that displayed a high MolDock score, efficacy, and compatibility with drug delivery against the antioxidant activity. Optimization of the structure and chemical reactivity parameter of the hit compound was calculated by DFT. Moreover, ADMET prediction was evaluated to check the bioavailability and toxicity of the hit compound. Hesperidin (B25) is found to be a hit compound after the whole study and can be synthesized for potent drug discovery in the future.