Cross-validated Q2 Research Articles

Combining Mendelian Randomization Analysis and 3D-QSAR to Investigate the Effectiveness of a New Series of Hydroxyquinolines in Osteoarthritis.

Osteoarthritis (OA) represents a persistent degenerative joint ailment. As OA advances, profound joint pain coupled with diminished joint function inflicts substantial physical distress and psychological strain on patients. Presently, pharmacological solutions for arthritis remain limited, primarily encompassing analgesics and joint replacement surgical procedures. Hence, non-operative strategies to mitigate osteoarthritis progression have captured significant attention in orthopedic research. This study aims to discern a definitive causal linkage between ADAMTS-4/5 and osteoarthritis through Mendelian randomization analysis. Moreover, it seeks to anticipate the therapeutic efficacy of a suite of emergent hydroxyquinolines for osteoarthritis using the Quantitative Structure-Activity Relationship (QSAR) methodology. Within this study, genetic variants specific to knee osteoarthritis were procured as exposure variables from a genome-wide association study (GWAS). Genetic variant data for ADAMTS-4/5 served as the endpoint to evaluate the causal nexus employing univariate Mendelian randomization. This analysis underpins the hypothesis that ADAMTS-4/5 presents a promising therapeutic target for osteoarthritis management. The suppressive properties of novel hydroxyquinolines against ADAMTS-4/5 were subsequently examined through conformational analyses, underscoring the potential of these compounds as therapeutic candidates for osteoarthritis. IVW outcomes from the Mendelian randomization revealed a significant association of KOA (OR: 1.1675, 95% CI: 1.0003-1.3627, P = 0.0495) with ADAMTS-5. However, KOA (OR: 1.0801, 95% CI: 0.9256-1.2604, P = 0.3278) displayed no evident connection with ADAMTS-4. Notably, the instrumental variables manifested neither heterogeneity nor horizontal pleiotropy. In this research endeavor, 16 pharmacological models were formulated via the CoMSIA method within 3D conformational relationship evaluations. A synergistic interplay of hydrophobic, spatial, and hydrogen-bonded receptor domains emerged as the most predictively potent. The cross-validation coefficient q2 for the optimum model stood at 0.716, with a principal component score of 5, a regression coefficient r2 of 0.971, a standard estimation error of 0.351, and an f-value of 156.951. Such metrics intimate the commendable predictive prowess of our devised CoMSIA models. The research unearthed a robust causal interrelation between ADAMTS-5 and osteoarthritis via Mendelian randomization. Furthermore, a credible drug model targeting ADAMTS-5 was constructed. Collectively, these findings illuminate a path forward in the pursuit of target-specific drugs for osteoarthritis management in subsequent investigations.

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND: Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS). OBJECTIVE: In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials. METHODS: To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA. RESULTS: Maps that were generated were used to determine the screen’s r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors. CONCLUSION: With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model’s robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Design and Pharmacophore Study of Triazole Analogues as Aromatase Inhibitors.

In current scenario breast cancer measured as one of the dangerous health issues. An effective therapeutic class of drug known as aromatase inhibitors (AIs) is dominant against estrogen receptorpositive breast cancer. However, there is an urgent need to create target-specific AIs with better anti-breast cancer profiles due to the increased toxicity and adverse effects related to currently existing anti-breast cancer drugs. In the present study, we have designed of 100 novel tiazole analogues as aromatase inhibitors their pharmacophoric features were explored. Molecular docking was applied to a series of 4-substituted-1, 2, 3-triazoles containing letrozole for their aromatase inhibitory effects. The aromatase inhibitory activity of the compound in a series varies in the range of (IC50 = 0.008-31.26 μM). A hydrogen atom positioned at R1 of the triazole ring in compound (01) was responsible for the most potent compound (IC50 = 0.008 μM) in the series of 28 compounds as compared to letrozole. The self-organizing molecular field study was used to assess the molecular characteristics and biological activities of the compounds. The four models were developed using PLS and MLR methods. The PLS method was good for statistical analysis. The letrozole scaffold-based 100 compounds were designed by selecting an effective pharmacophore responsible for aromatase inhibitory activity. The designed compound was placed on the previous model as a test set, and its IC50 values were calculated. Hydrogen bonds were established between the potent molecule (01) and the essential residues Met 374 and Arg 115, which were responsible for the aromatase-inhibiting action. Cross-validated q2 (0.6349) & noncross- validated r2 (0.7163) were discovered in the statistical findings as having reliable predictive power. Among 100 designed compounds, seven compounds showed good aromatase inhibitory activities. The additional final SOMFA model created for the interactions between the aromatase and the triazole inhibitors may be helpful for future modification and enhancement of the inhibitors of this crucial enzyme.

3D-QSAR Studies on High-affinity Phosphodiestera.

Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.

In-silico Design of Potent Anti-tubercular Agents containing Isatinylthiosemicarbazone Pharmacophore

A recent study reveals that Isatinylthiosemicarbazone analogues inhibit mycobacterial growth by inhibiting the isocitrate lyase (ICL). Hence two-dimensional (2D), three-dimensional (3D), and group QSAR (GQSAR) studies were performed to reduce the amount of pharmacophore needed to make effective ICL inhibitors. New chemical entities (NCEs) were created based on the findings of all QSAR studies. It was discovered that QSAR models produced noticeably positive statistical findings. i.e. (r2 > 0.7), cross-validation (q2 > 0.6), and external validation (pred_r2 > 0.6), indicating high predictability of all models. Utilizing molecular docking studies, the binding affinities of designed NCEs were investigated for the ICL enzyme (PDB code: 1F8M). The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of designed NCEs were expected to have a pharmacokinetic profile similar to their drug. Overall, it is important to state that the methodology used for pharmacophore optimization using 2D, 3D, G-QSAR, and molecular docking ADMET research works was discovered to be extremely accurate.

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.

Mechanistic Studies on Aluminum-Catalyzed Ring-Opening Alternating Copolymerization of Maleic Anhydride with Epoxides: Ligand Effects and Quantitative Structure-Activity Relationship Model.

Previous work has indicated that aluminum (Al) complexes supported by a bipyridine bisphenolate (BpyBph) ligand exhibit higher activity in the ring-opening copolymerization (ROCOP) of maleic anhydride (MAH) and propylene oxide (PO) than their salen counterparts. Such a ligand effect in Al-catalyzed MAH-PO copolymerization reactions has yet to be clarified. Herein, the origin and applicability of the ligand effect have been explored by density functional theory, based on the mechanistic analysis for chain initiation and propagation. We found that the lower LUMO energy of the (BpyBph)AlCl complex accounts for its higher activity than the (salen)AlCl counterpart in MAH/epoxide copolymerizations. Inspired by the ligand effect, a structure-energy model was further established for catalytic activity (TOF value) predictions. It is found that the LUMO energies of aluminum chloride complexes and their average NBO charges of coordinating oxygen atoms correlate with the catalytic activity (TOF value) of Al complexes (R2 value of 0.98 and '3-fold' cross-validation Q2 value of 0.88). This verified that such a ligand effect is generally applicable in anhydride/epoxide ROCOP catalyzed by aluminum complex and provides hints for future catalyst design.

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles.

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP-treated cell lines. The model achieves mean cross-validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14900 candidates/min, to identify the best-performing selectively cytotoxic NPs. As proof-of-concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning.

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Structure-Activity Relationship Studies Based on 3D-QSAR CoMFA/CoMSIA for Thieno-Pyrimidine Derivatives as Triple Negative Breast Cancer Inhibitors.

Triple-negative breast cancer (TNBC) is defined as a kind of breast cancer that lacks estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptors (HER2). This cancer accounts for 10-15% of all breast cancers and has the features of high invasiveness and metastatic potential. The treatment regimens are still lacking and need to develop novel inhibitors for therapeutic strategies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, based on a series of forty-seven thieno-pyrimidine derivatives, were performed to identify the key structural features for the inhibitory biological activities. The established comparative molecular field analysis (CoMFA) presented a leave-one-out cross-validated correlation coefficient q2 of 0.818 and a determination coefficient r2 of 0.917. In comparative molecular similarity indices analysis (CoMSIA), a q2 of 0.801 and an r2 of 0.897 were exhibited. The predictive capability of these models was confirmed by using external validation and was further validated by the progressive scrambling stability test. From these results of validation, the models were determined to be statistically reliable and robust. This study could provide valuable information for further optimization and design of novel inhibitors against metastatic breast cancer.

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors.

Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Revision and Extension of a Generally Applicable Group Additivity Method for the Calculation of the Refractivity and Polarizability of Organic Molecules at 298.15 K

In a continuation and extension of an earlier publication, the calculation of the refractivity and polarizability of organic molecules at standard conditions is presented, applying a commonly applicable computer algorithm based on an atom group additivity method, where the molecules are broken down into their constituting atoms, these again being further characterized by their immediate neighbor atoms. The calculation of their group contributions, carried out by means of a fast Gauss–Seidel fitting calculus, used the experimental data of 5988 molecules from literature. An immediate subsequent ten-fold cross-validation test confirmed the extraordinary accuracy of the prediction of the molar refractivity, indicated by a correlation coefficient R2 and a cross-validated analog Q2 of 0.9997, a standard deviation σ of 0.38, a cross-validated analog S of 0.41, and a mean absolute deviation of 0.76%. The high reliability of the predictions was exemplified with three classes of molecules: ionic liquids and silicon- and boron-containing compounds. The corresponding molecular polarizabilities were calculated indirectly from the refractivity using the inverse Lorentz–Lorenz relation. In addition, it could be shown that there is a close relationship between the “true” volume and the refractivity of a molecule, revealing an excellent correlation coefficient R2 of 0.9645 and a mean absolute deviation of 7.53%.

3D-QSAR and Docking Studies on Pyrimidine Derivatives of Second-Generation ALK Inhibitors

AbstractAnaplastic lymphoma kinase (ALK) is a promising target for the treatment of non-small cell lung cancer. Under crizotinib treatment, drug resistance and progressive disease appeared after the point mutations arising in the kinase domain of ALK. Second-generation ALK inhibitors can solve the deficiencies of the first generation, especially the drug resistance in cancer chemotherapy. Ceritinib (LDK378), a pyrimidine derivative, for example, can inhibit the activity of ALK with an IC50 value of 40.7 nmol/L, and can experience disease progression after initial treatment with crizotinib. Unfortunately, clear structure–activity relationships have not been identified to date, impeding the rational design of future compounds possessing ALK inhibition activity. To explore interesting insights into the structures of pyrimidine derivatives that influence the activities of the second-generation ALK inhibitors, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking were performed on a total of 45 derivatives of pyrimidine. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were used to generate 3D-QSAR models. CoMFA and CoMSIA were performed using the Sybyl X 2.0 package. Molecular docking analysis was performed using the Surflex-Dock module in SYBYL-X 2.0 package. We found in the CoMFA model that the non-cross-validated r2 value was 0.998, the cross-validated q 2 value was 0.663, and the F statistic value was 2,401.970, while the r2 value was 0.988; q 2 value was 0.730, and F value was 542.933 in CoMSIA models, suggesting the good predictability of the CoMFA and CoMSIA models. 3D contour maps and docking results suggested that different groups on the core parts of the compounds could enhance the biological activities. Based on these results, the established 3D-QSAR models and the binding structures of ALK inhibitors obtained favor the prediction of the activity of new inhibitors and will be helpful in the reasonable design of ALK inhibitors in the future.

Hologram QSAR and Topomer CoMFA Study on Naphthyridone Derivatives as ATAD2 Bromodomain Inhibitors

Background: ATAD2 is closely related to the occurrence and proliferation of many tumors. Thus, exploring ATAD2 inhibitors is greatly significant for the prevention and treatment of tumors. In this study, the quantitative structure–activity relationship (QSAR) analyses of 57 naphthyridone derivatives were conducted using hologram quantitative structure–activity relationship (HQSAR) and topomer comparative molecular field analysis (topomer CoMFA). Method: The 57 naphthyridone derivatives were divided into the training (44 derivatives) and testing (13 derivatives) sets. HQSAR and topomer CoMFA models were obtained by applying the SYBYL-X software and validated using various validation parameters. Contribution maps from the best HQSAR model and the contour maps from the best topomer CoMFA model were analyzed. Results: The most effective HQSAR model exhibited significant cross-validated (q2 = 0.872) and non cross-validated (r2 = 0.972) correlation coefficients, and the most effective topomer CoMFA model had q2 = 0.861 and r2 = 0.962. Several external validation parameters, such as , , , , and , were used to calculate the correlation coefficients of the test set samples and validate both models. The result exhibited a powerful predictive capability. Graphical results from HQSAR and topomer CoMFA were validated by the binding mode in the crystal structure. Conclusion: The models may be beneficial to enhance the understanding of the structure–activity relationships for this class of compounds and also provide useful clues for the design of potential ATAD2 bromodomain inhibitors.

A Developed QSPR Model for the Melting Points of Isatin Derivatives

This paper suggests a developed quantitative structure property relationship (QSPR) model for coping the melting point (M.P) which is considered as the main and important physical property of solid state. The development was based on the decreasing in number of descriptors in order to be statistically intensive with excellent values of statistical parameters. The model was applied successfully to the already published data of M.P for 32 biologically active molecules derived from 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides. The calculations of descriptors were carried out using density functional theory (DFT) with bases set of 6-311G (d, P). A statistically intensive QSPR model contains only three descriptors with physical meaning has been introduced. Two of them are belonging to the direct theoretical calculations but the third was considered as three dimensional correcting term of which depending on the chemical structure of the substituent. The theoretically calculated descriptors were the total connectivity (TC) and the average charge on the aryl group (AQArH) as both depending on the packing of molecules and responsible on M.P. The last descriptor was suggested as a correction term with respect to the packing of molecules of which depending on their three dimensional chemical structure which only taking the values of -1, 0 and 1. A relatively excellent statistical parameters for the developed model were obtained with square regression coefficient (r2), cross-validation (q2) and root mean squared error (RMSE) are equal to 0.925, 0.903 and 15.26oC, respectively. It was concluded that the developed model gives more confidence results in addition to physical significance which can be considered as a helpful tool for understanding the factors affecting the melting point.

3D QSAR Analysis of Flavones as Antidiabetic agents

Diabetes is the most prevailing disease worldwide and emerged as the fourth leading cause of mortality. Inhibition of intestinal α-Glucosidase enzyme is an effective approach for controlling post prandial hyperglycemia. α-Glucosidase inhibitors are known to be very effective in decreasing post-prandial hyperglycemia but the existing drugs are weak inhibitors of α-Glucosidase and also have side effects. Hence it needs for new therapeutic candidate which can effectively inhibit the activity of α-Glucosidase. Flavones recognized as the potential lead structure for many pharmacological activities. In the present research work 3D QSAR (comparative molecular field analysis and comparative molecular similarity indices analysis) was carried out on a series of flavones to identify structural requirement for effective inhibition of α-Glucosidase enzyme. The QSAR results shows that the LOO cross-validated q2 values of CoMFA and CoMSIA models are 0.742 and 0.759, respectively. The outcome of this research work could be effectively utilized for design of better α-Glucosidase inhibitors.

Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background: In recent years, the number of people infected with the hepatitis C virus (HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti- HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs. Methods: In order to explore the relationship between the structure of benzimidazole derivative and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors. Results: The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a high predictive ability. Through the analysis of the contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE. Conclusion: The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

Molecular Modelling Studies on Thiazole-Based α-Glucosidase Inhibitors Using Docking and CoMFA, CoMSIA and HQSAR.

The biological dataset was retrieved from two series of α-glucosidase inhibitors synthesized by Rahim et al. and Taha et al. and consisted of a total of 46 (forty-six) α- glucosidase inhibitors. The α-glucosidase inhibitory IC50 values (μM; performed against α-glucosidase from Saccharomyces cerevisiae) were converted into negative logarithmic units (pIC50). The CoMFA and CoMSIA models were developed using 37 as a training set, and externally validated using 9 as a test set. The CoMFA models MMFF94 were generated, ranging from 3.4661 to 5.2749 using leave-oneout PLS analysis cross-validated correlation coefficient q2 0.787, a high non-cross-validated correlation coefficient r2 0.819, with a low Standard Error Estimation (SEE) 0.041, F value 1316.074 and r2 pred 0.996. The steric and electrostatic fields contributions were 0.507 and 0.493, respectively. The CoMSIA model q2 0.805, r2 0.833 was attained, (SEE) 0.065, F value 520.302 and r2 pred 0.990. Contribution of steric, electrostatic, hydrophobic, donor and acceptor fields was 0.151, 0.268, 0.223, 0.234, 0.124, respectively. The HQSAR model of the training set exhibits a significant cross-validated correlation coefficient q2 0.800 and non-cross-validated correlation coefficient r2 0.943.

Revision and Extension of a Generally Applicable Group-Additivity Method for the Calculation of the Standard Heat of Combustion and Formation of Organic Molecules.

The calculation of the heats of combustion ΔH°c and formation ΔH°f of organic molecules at standard conditions is presented using a commonly applicable computer algorithm based on the group-additivity method. This work is a continuation and extension of an earlier publication. The method rests on the complete breakdown of the molecules into their constituting atoms, these being further characterized by their immediate neighbor atoms. The group contributions are calculated by means of a fast Gauss–Seidel fitting calculus using the experimental data of 5030 molecules from literature. The applicability of this method has been tested by a subsequent ten-fold cross-validation procedure, which confirmed the extraordinary accuracy of the prediction of ΔH°c with a correlation coefficient R2 and a cross-validated correlation coefficient Q2 of 1, a standard deviation σ of 18.12 kJ/mol, a cross-validated standard deviation S of 19.16 kJ/mol, and a mean absolute deviation of 0.4%. The heat of formation ΔH°f has been calculated from ΔH°c using the standard enthalpies of combustion for the elements, yielding a correlation coefficient R2 for ΔH°f of 0.9979 and a corresponding standard deviation σ of 18.14 kJ/mol.