3D Quantitative Structure-activity Relationship Research Articles

‘3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity

Iron overload is a primary cause of vital organ failure in patients with blood transfusion-dependent beta-thalassemia, and the hypoxia-inducible factor-1 α (HIF-1α) plays an important role in iron homeostasis pathway. HIF-1α modulation as a potential therapeutic target approach for iron chelation in hepatocyte cells. In this study, we used a 3D quantitative structure-activity relationship (QSAR) analysis to predict the inhibitory activity of HIF-1α inhibitors for iron chelation in liver cells. These feature descriptors were used to build a 3D-QSAR model, which was validated using Cost analysis and Fischer's randomization test. The model was used to virtually search the chemical compound libraries for potential inhibitor candidates with least inhibitory activity. The High-throughput Docking (Libdock) approach was used to dock large repositories of chemical molecules. Following Libdock score screening, the protein-ligand poses were docked using docking optimization (Cdocker) method. Binding energy were calculated for the protein-ligand poses of lowest –Cdocker Energy and –Cdocker Interaction. Further, side chain hopping method was used to generate lead novel ligand from best hit pose of ligand. Molecular dynamics simulation study to evaluate the lead novel ligand. Our study demonstrates the utility of 3D-QSAR pharmacophore screening in predicting the inhibitory activity for target. Inhibition strategy for iron chelation provides an alternative routes for reducing the iron content.

Bisphenol analogues inhibit human and rat 17β-hydroxysteroid dehydrogenase 1: 3D-quantitative structure-activity relationship (3D-QSAR) and in silico docking analysis

Bisphenols, estrogenic endocrine-disrupting chemicals, disrupt at least one of three endocrine pathways (estrogen, androgen, and thyroid). 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) is a steroidogenic enzyme that catalyzes the activation of estradiol from estrone in human placenta and rat ovary. However, whether bisphenols inhibit 17β-HSD1 and the mode of action remains unclear. This study we screened 17 bisphenols for inhibiting human 17β-HSD1 in placental microsomes and rat 17β-HSD1 in ovarian microsomes and determined 3D-quantitative structure-activity relationship (3D-QSAR) and mode of action. We observed some bisphenols with substituents were found to significantly inhibit both human and rat 17β-HSD1 with the most potent inhibition on human enzyme by bisphenol H (IC50 = 0.90 μM) when compared to bisphenol A (IC50 = 113.38 μM). Rat enzyme was less sensitive to the inhibition of bisphenols than human enzyme with bisphenol H (IC50 = 32.94 μM) for rat enzyme. We observed an inverse correlation between IC50 and hydrophobicity (expressed as Log P). Docking analysis showed that they bound steroid-binding site of 17β-HSD1. The 3D-QSAR models demonstrated that hydrophobic region, hydrophobic aromatic, ring aromatic, and hydrogen bond acceptor are key factors for the inhibition of steroid synthesis activity of 17β-HSD1.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Exploration of Structure-Activity Relationship Using Integrated Structure and Ligand Based Approach: Hydroxamic Acid-Based HDAC Inhibitors and Cytotoxic Agents.

The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for histone deacetylase (HDAC) inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anticancer activity. We have selected a dataset from earlier research findings. The target and ligand molecules were procured from recognized databases and incorporated into pivotal findings such as molecular docking (XP glide), e-pharmacophore study and 3D QSAR model designing study (phase). Docking revealed molecule 39 with better docking score and well binding contact with the protein. 3D QSAR analysis, which was performed for partial least squares factor 5 reported good 0.9877 and 0.7142 as R2 and Q2 values and low standard of deviation: 0.1049 for hypothesis AADRR.139. Based on the computational outcome, it has been concluded that molecule 39 is an effective and relevant candidate for inhibition of HDAC activity. Moreover, these computational approaches motivate to discover novel drug candidates in pharmacological and healthcare sectors.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib’s action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors. Communicated by Ramaswamy H. Sarma

DESIGN OF POTENT ANTICANCER MOLECULES COMPRISING PYRAZOLYL-THIAZOLINONE ANALOGS USING MOLECULAR MODELLING STUDIES FOR PHARMACOPHORE OPTIMIZATION

Objectives: Numerous tiny receptor tyrosine kinase inhibitors have been reported as anticancer medications over the past 10 years. However, a lot of them lack effectiveness in vivo, selectivity, or do not last long before developing resistance. Methods: We used molecular modeling research to improve the pharmacophore to get beyond these limitations. For the purpose of linking the chemical makeup of pyrazolyl thiazolinone analogs with their anticancer activity, quantitative structure activity relationship (QSAR) investigations in two dimensions (2D) and three dimensions (3D) were carried out. Pyrazolyl thiazolinone pharmacophore’s stearic, electronic, and hydrophobic requirements were calculated using 3D QSAR. Results: By leveraging the findings of QSAR investigations, the pharmacophore was refined and new chemical entities (NCEs) were generated. The r2 and q2 values obtained for the best model No. 4 of 2D QSAR were 0.9244 and 0.8701, respectively. A drug-like pharmacokinetic profile was ensured by studying the binding affinities of proposed NCEs on epidermal growth factor receptor-TK using docking studies and estimating their absorption, distribution, metabolism, and excretion features. Conclusion: When statistical significance is closely examined, predictability of the model and its residuals (actual activity minus predicted activity) is found to be close to zero, leading us to draw the conclusion that the logic behind the design of NCEs was determined to be sound.

The discovery of the natural compound Boeravinone-C as a potential antiobesity drug candidate targeting pancreatic lipase using chemo-informatics-based approaches.

Obesity is a metabolic disorder distinguished by excess fat deposition in fatty tissues. Pancreatic lipase is one of the promising drug targets for treating obesity due to its critical role in the hydrolysis of triglycerides into mono-glycerides and free fatty acids. Due to unsatisfactory results and severe side effects of the current drugs available for treating obesity, there is an urgent need to identify novel therapeutic options. Boerhaavia diffusa is one of the widely known species of flowering plant commonly known as Punamava. Extracts from Punamava plants have been widely used in treating countless ailments in traditional medicine. Recently, multiple reports demonstrated the potential antiobesity activity of B. diffusa plant extracts. In this scenario, we have evaluated numerous reported B. diffusa against pancreatic lipase drug targets to identify which reported phytochemicals to have the most promising potential to act as an inhibitor for pancreatic lipase using computational approaches. All the twenty-four phytochemicals from Boerhaavia diffusa were identified as significantly strong binders with a range of binding energies between -6.0 to -8.0 Kcal/mol inside the pancreatic lipase active binding site. On the other hand, we calculated 2D Quantitative Structure-Activity Relationship (QSAR) molecular descriptor properties adhered to Lipinski's rule of five. Between twenty-four phytochemicals evaluated, Boeravinone-C, with a range binding energy of -8.0 Kcal/mol, was discovered as the best lead-like molecule, compared to marketed Orlistat, which has shown -5.6 Kcal/mol of binding energy. Conclusively, Boeravinone-C from B. diffusa extract showed promising inhibitory potential against pancreatic lipase worth further evaluation.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

High accuracy prediction of dipeptide angiotensin-converting enzyme (ACE) inhibitory activity by improving the credibility of the 3D-quantitative structure-activity relationship (3D-QSAR) model database and investigating inhibition mechanism

The 3D-QSAR model is one of the most effective techniques for predicting the peptide bioactivity based on their structure properties. The accuracy of model prediction is highly dependent on the bio-activity of the peptides in its dataset. However, the same peptide used for constructing the database was reported to have obviously different activities in different published articles, which may significantly decrease the prediction accuracy. Based on this, we chose ACE inhibitory (ACE-I) dipeptides that can be absorbed directly into the human small intestine as research object. Ten dipeptides were randomly selected from the literatures for molecular docking with ACE and synthesized to test their activity. Ultimately, a linear equation was developed between bio-activities and docking scores, which was used to revalidate the reliable ACE-I activities of all reported dipeptides for constructing a credible dataset of 3D-QSAR model. The prediction accuracy was significantly enhanced and the model we established had a high Q2 = 0.840 and R2 = 0.998. Furthermore, dipeptide CW was predicted and tested in vitro to achieve the highest ACE-I activity (IC50 = 0.16 μM), and the inhibition mechanism was also investigated. This study introduced a new method for predicting bio-activity with high precision using the 3D-QSAR model.

One-pot multicomponent synthesis of novel pyridine derivatives for antidiabetic and antiproliferative activities.

Background: Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cores were designed and integrated by artificial intelligence. Methods: Novel pyridines were designed and synthesized. Compounds MNS-1-MNS-4 were evaluated for their structure and were screened for their in vitro antidiabetic (α-amylase) activity and anticancer (HepG2) activity by methyl thiazolyl tetrazolium assay. Comparative 3Dquantitative structure-activity relationship analysis and pharmacophore generation were carried out. Results: The study revealed MNS-1 and MNS-4 as good alternatives to acarbose as antidiabetic agents, and MNS-2 as a more viable, better alternative to doxorubicin in the methyl thiazolyl tetrazolium assay. Conclusion: This combination of studies identifies new and more active analogs of existing FDA-approved drugs for the treatment of diabetes.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods.

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

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.

Design, Synthesis, and Anti-PVY Biological Activity of 1,3,5-Triazine Derivatives Containing Piperazine Structure.

In this study, a commercial agent with antivirus activity and moroxydine hydrochloride were employed to perform a lead optimization. A series of 1,3,5-triazine derivatives with piperazine structures were devised and synthesized, and an evaluation of their anti-potato virus Y (PVY) activity revealed that several of the target compounds possessed potent anti-PVY activity. The synthesis of compound C35 was directed by a 3D-quantitative structure-activity relationship that used the compound's structural parameters. The assessment of the anti-PVY activity of compound C35 revealed that its curative, protective, and inactivation activities (53.3 ± 2.5%, 56.9 ± 1.5%, and 85.8 ± 4.4%, respectively) were comparable to the positive control of ningnanmycin (49.1 ± 2.4%, 50.7 ± 4.1%, and 82.3 ± 6.4%) and were superior to moroxydine hydrochloride (36.7 ± 2.7%, 31.4 ± 2.0%, and 57.1 ± 1.8%). In addition, molecular docking demonstrated that C35 can form hydrogen bonds with glutamic acid at position 150 (GLU 150) of PVY CP, providing a partial theoretical basis for the antiviral activity of the target compounds.

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative’s Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R 2 of 0.9092, R 2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

2D-QSAR modeling for risk assessment of pharmacotherapy applied during pregnancy.

The risk evaluation for pharmacological therapy during pregnancy is critical for maternal and fetal health. The initial risk assessment stage, the risk measurement, begins with pregnancy-labeling categories (A, B, C, D, and X) for pharmaceuticals defined by the US Food and Drug Administration (FDA). Recently, in silico methods have been preferred in toxicology studies to eliminate ethical issues before conducting clinical toxicology studies and animal experiments. Quantitative structure-activity relationship (QSAR) modeling is one of the in silico methodologies. The research focuses on creating a QSAR model that predicts the five FDA pregnancy categories of medications. Our dataset included 868 pharmaceuticals containing nearly every pharmacological group collected from the FDA. 2D-molecular descriptors were calculated using PaDEL software. Twenty-four QSAR models were developed, and the best four models were discussed. The results of the models were compared according to sensitivity, accuracy, F-score, specificity, ROC values, and Matthews correlation coefficient. Considering the statistical results, Random Forest is the best model for determining the pregnancy risk category of drugs. The accuracy of the model was 76.49% for internal and 93.58% for external validation. According to the kappa statistics, there is an average agreement of 0.583 for internal and a perfect agreement of 0.893 for external validation. Since the error rates of the model are very close to 0, the model is highly accurate. Consequently, our novel QSAR model gives guidance on the safe use of pharmaceuticals during pregnancy without requiring animal tests or clinical trials on pregnant women.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

2D-QSAR STUDY ON SOME NOVEL DIHYDROPYRIMIDINE-4-CARBONITRILE ANALOGS AS AN ANTIFUNGAL ACTIVITY

Objective: The present study was designed to study the antifungal activity of Dihydropyrimidine-4-Carbonitrile analogs against the fungi Candida albicans by a 2D quantitative structure-activity relationship (QSAR) model. Methods: The pyrimidine derivatives were produced using lipophilic, electronic, and steric parameters by Quantitative Structure Activity-Relationships (QSAR). A relationship between dependent and independent variables (biological activities and physicochemical descriptors, respectively) was resolved statistically using regression analysis. The F value shows the level of statistical significance of the regression (r2) was used to report the fitness of data. The newly synthesized derivatives were evaluated for in vitro antifungal activity against Candida albicansby Nutrient agar and Seaboard dextrose agar media. Results: Multiple linear regression is a method of crucial importance, it allowed us to obtain a relation between the calculated parameters and the antifungal activity; this we can interpret the variance of the activity by contribution to the calculated descriptors. Quantitative structure-activity relationship (QSAR) model showing a significant activity-descriptors relationship accuracy of 90% (R2 ≥ 0.90) and activity prediction accuracy of 81% (R²cv = 0.81). These values prove that the model obtained is reliable. Out of the three descriptors studied; log P has minimum potency, molar refractivity has more potency and heat of formation has moderate potency. Conclusion: Important structural understanding in the pattern of potent antifungal agents by Quantitative Structure Activity-Relationships (QSAR) study. The acquired physicochemical properties (electronic, topological, and steric) show the important structural features required for antifungal activity against Candida albicans.

An improved 3D quantitative structure-activity relationships (QSAR) of molecules with CNN-based partial least squares model

Ligand-based virtual screening plays an important role for cases in which protein structures are not available. Among ligand-based methods, accurate and fast prediction of protein-ligand binding affinity is crucial for reducing computational cost and exploring the chemical search space efficiently. Here we proposed a CNN-based method, termed as L3D-PLS for building the quantitative structure-activity relationships without target structures. In L3D-PLS, a CNN module was designed for extracting the key interaction features from the grids around aligned ligands, and a partial least square (PLS) model fits the binding affinity with the extracted features of the pre-trained CNN module. In 30 publicly available pre-aligned molecular datasets, L3D-PLS outperformed the traditional CoMFA method. This results highlight that L3D-PLS can be useful for lead optimization based on small datasets which is often true in drug discovery compaign.

3D quantitative structure–activity relationships study on anti-gastric cancer of chrysin derivatives

3D quantitative structure–activity relationships study on anti-gastric cancer of chrysin derivatives

QSAR Analysis, Molecular Docking and ADME Studies of Thiobarbituric Acid Derivatives as Thymidine Phosphorylase Inhibitors: A Rational Approach to Anticancer Drug Design by in silico Modelling

Background: Thymidine Phosphorylase (TP) is an imperative target for cancer researchers. In the current research, quantitative structure-activity relationship (QSAR) models were demonstrated to identify new TP inhibitors. Objective: The main objective is to perform a QSAR study on a series of 19 derivatives of thiobarbituric acid and new molecules designed and dock to check potency and efficacy for anticancer activity. Methods: Multiple linear regression analysis (MLR) was used to establish a two-dimensional quantitative structure-activity relationship (2D-QSAR) with regression coefficient values of 0.9781, 0.9513, and 0.9819 for the training set (r2), leave-one-out (LOO) dependent internal regression (q2), and external test set regression (r2 _pred), respectively. Three-dimensional quantitative structure-activity relationship (3DQSAR) model, obtained by using the simulated annealing k nearest neighbour (SA-KNN) method (q2 = 0.7880). Newly designed molecules were subjected to docking studies with 7-deazaxanthine taken as standard. Results: Molecular modelling, structure-based drug design and docking study analysis were performed. The new chemical entities (NCE’s) designed, docked towards targeted receptor and show good results as compared to the standard 7-deazaxanthine. It was found that these molecules bind similar amino acid pocket regions as that of standard. Molecules bind at the active site of TP enzyme involving H bond interactions with shorter distances showed greater affinity. At last, the oral bioavailability and toxic effect were evaluated as absorption, distribution, metabolism, and elimination (ADME) studies by computational means of the Qikprop tool of Schrodinger. Conclusion: One of the most successful and fast-increasing methodologies is molecular modelling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. QSAR and docking study was performed, and most of the molecules have shown good dock scores. Based on these results, NCE’s for anticancer activity were successfully designed and analysed in this research work which will be helpful for effective drug synthesis with less toxicity in the future. Others: 2D QSAR model was generated by three methods, and the best one was selected for further study. NCEs were planned based on descriptors such as topological, electrostatic, steric, and hydrophobic substitutions around the core.