Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models.

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERß dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERß agonists among OH-PCB congeners.

Ligand-based drug design of quinazolin-4(3H)-ones as breast cancer inhibitors using QSAR modeling, molecular docking, and pharmacological profiling.

BACKGROUND: Breast cancer is the most common tumor among females globally. Its prevalence is growing around the world, and it is alleged to be the leading cause of cancer death. Approved anti-breast cancer drugs display several side effects and resistance during the early treatment stage. Hence, there is a need for the development of more effective and safer drugs. This research was aimed at designing more potent quinazolin-4(3H)-one molecules as breast cancer inhibitors using a ligand-based design approach, studying their modes of interaction with the target enzyme using molecular docking simulation, and predicting their pharmacological properties. METHODS: The QSAR model was developed using a series of quinazoline-4(3H)-one derivatives by utilizing Material Studio v8.0 software and validated both internally and externally. Applicability domain virtual screening was utilized in selecting the template molecule, which was structurally modified to design more potent molecules. The inhibitive capacities of the design molecules were predicted using the developed model. Furthermore, molecular docking was performed with the EGFR target active site residues, which were obtained from the protein data bank online server (PDB ID: 2ITO) using Molegro Virtual Docker (MVD) software. SwissADME and pkCSM online sites were utilized in predicting the pharmacological properties of the designed molecules. RESULTS: Four QSAR models were generated, and the first model was selected due to its excellent internal and external statistical parameters as follows: R2 = 0.919, R2adj = 0.898, Q2cv = 0.819, and R2pred = 0.7907. The robustness of the model was also confirmed by the result of the Y-scrambling test performed with cR2p = 0.7049. The selected model was employed to design seven molecules, with compound 4 (pIC50 = 5.18) adopted as the template. All the designed compounds exhibit better activities ranging from pIC50 = 5.43 to 5.91 compared to the template and Doruxybucin (pIC50 = 5.35). The results of molecular docking revealed better binding with the EGFR target compared with the template and Doruxybucin. The designed compounds exhibit encouraging therapeutic applicability, as evidenced by the findings of pharmacological property prediction. CONCLUSIONS: The designed derivatives could be utilized as novel anti-breast cancer agents.

Virtual screening, pharmacokinetic, and DFT studies of anticancer compounds as potential V600E-BRAF kinase inhibitors.

Objectives: V600E-BRAF kinase is an essential therapeutic target in melanoma and other types of tumors. Because of its resistance to known inhibitors and the adverse effects of some identified inhibitors, investigation of new potent inhibitors is necessary. Methods: In the present work, in silico strategies such as molecular docking simulation, pharmacokinetic evaluation, and density functional theory (DFT) computations were used to identify potential V600E-BRAF inhibitors from a set of 72 anticancer compounds in the PubChem database. Results: Five top-ranked molecules (12, 15, 30, 31, and 35) with excellent docking scores (MolDock score ≥90 kcal mol-1, Rerank score ≥60 kcal mol-1) were selected. Several potential binding interactions were discovered between the molecules and V600E-BRAF. The formation of H-bonds and hydrophobic interactions with essential residues of V600E-BRAF suggested the high stability of these complexes. The selected compounds had excellent pharmacological properties according to the drug likeness rules (bioavailability) and pharmacokinetic properties. Similarly, the energy for the frontier molecular orbitals, such as the HOMO, LUMO, energy gap, and other reactivity parameters, was computed with DFT. The frontier molecular orbital surfaces and electrostatic potentials were investigated to demonstrate the charge-density distributions potentially associated with anticancer activity. Conclusion: The identified compounds were found to be potent hit compounds for V600E-BRAF inhibition with superior pharmacokinetic properties; therefore, they may be promising cancer drug candidates.

New flavone-based arylamides as potential V600E-BRAF inhibitors: Molecular docking, DFT, and pharmacokinetic properties.

Objectives: The V600E-BRAF protein kinase is an attractive and essential therapeutic target in melanoma and other tumors. However, because of its resistance to the known inhibitors and side effects of some identified inhibitors, new potent inhibitors need to be identified. Methods: In the present work, in silico strategies such as the molecular docking simulation, DFT (Density-Functional-Theory) computations, and pharmacokinetic evaluation were used to determine potential V600E-BRAF inhibitors from a set of 31 synthesized novel flavone-based arylamides. Results: The docking result demonstrated that four compounds (10, 11, 28, and 31) had acceptable docking scores (MolDock score of -167.523 kcal mol-1, -158.168 kcal mol-1, -160.581 kcal mol-1,-162.302 kcal mol-1, and a Rerank score of -124.365, -129.365, -135.878 and -117.081, respectively) appeared as most active and potent V600E-BRAF inhibitors that topped vemurafenib (-158.139 and -118.607 kcal mol-1). The appearance of H-bonds and hydrophobic interactions with essential residues for V600E-BRAF proved the high stability of these complexes. The energy for the frontier molecular orbitals such as HOMO, LUMO, energy gap, and other reactivity parameters was computed using DFT. The frontier molecular-orbital surfaces and electrostatic potentials (EPs) were investigated to demonstrate the charge-density distributions that might be linked to anticancer activity. Similarly, the chosen compounds revealed superior pharmacological properties according to the drug-likeness rules (bioavailability) and pharmacokinetic properties. Conclusion: The chosen compounds were recognized as potent V600E-BRAF inhibitors with superior pharmacokinetic properties and could be promising cancer drug candidates.

Pharmacokinetic profiling of quinazoline-4(3H)-one analogs as EGFR inhibitors: 3D-QSAR modeling, molecular docking studies and the design of therapeutic agents.

Objectives: Breast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents. Methods: Robust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software. Results: Internal validations of the best CoMFA and CoMSIA models (R2 = 0.855 and 0.895; Q2 = 0.570 and 0.599) passed the threshold values for the establishment of a consistent QSAR model. The constructed models were further validated externally using six compounds as a test set, thus revealing a satisfactory predicted correlation coefficient (R2 pred = 0.657 and 0.681). The CoMSIA_SHE models with the best statistical parameters were further subjected to applicability domain checks and only three influentials were detected. These were then utilized to design five novel compounds with activities ranging from 5.62 to 6.03. Molecular docking studies confirmed that compounds 20 to 26, with docking scores ranging from -163.729 to -169.796, represented lead compounds with higher docking scores compared to Gefitinib (-127.495). Furthermore, the designed compounds exhibited better docking scores ranging from -171.379 to -179.138. Conclusions: Pharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally.

Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies.

PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor ß dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

In silico analysis of noscapine compounds as anti-tumor agents targeting the tubulin receptor.

Objective: This research aims to develop a mathematical model that relates the structural features of noscapine with anti-tumor activity, to explains the mode of binding between noscapine compounds and the target receptor tubulin by docking analysis. By considering the results of docking analysis and predictions of pharmacokinetic properties/drug likeness, we designed novel noscapine compounds as anti-tumor agents against pancreatic cancer. Methods: We used an in silico quantitative structure-activity relationship (QSAR) approach, molecular docking analysis and online tools for pharmacokinetics and drug likeness prediction to develop novel compounds. Results: A QSAR model with good validations parameters and quality of fit (R2 = 0.9731, Q2 CV = 0.9434, R2 adj = 0.9647 and R2 test set = 0.8343) was built utilizing 70% of the dataset as a training set and the remaining 30% as an external validation to ascertain its predictive capability. Three novel compounds were designed: D3, D4 and D6 with binding scores of -11.2, -10.2 and 10.6 kcal/mol, respectively, exhibiting high affinity towards the tubulin receptor than the template (parent compound) and the co-crystallized ligand (E∗) with a binding score of 9.2 kcal/mol. Conclusion: The QSAR approach and molecular docking analysis is an important approach for modern drug discovery. Pharmacokinetics studies of the selected novel compounds revealed good drug properties and can be used as candidate compounds for the development of anti-tumor agents for pancreatic cancer.

Theoretical modeling and design of some pyrazolopyrimidine derivatives as Wolbachia inhibitors, targeting lymphatic filariasis and onchocerciasis.

Lymphatic filariasis and onchocerciasis are two common filarial diseases caused by a group of parasitic nematodes called filarial worms, which play host to the bacteria organism Wolbachia. One good treatment approach seeks Wolbachia as drug target. Here, a QSAR study was conducted to investigate the anti-wolbachia activities (pEC50) of 52 pyrazolopyrimidine analogues, while using the built model to predict the pEC50 values of the newly designed analogues. Density Functional Theory was used for the structural optimization, while the model building was based on Genetic Function Algorithm approach. The built QSAR model was validated thus: R2 = 0.8104, R2 adj = 0.7629, Q2 cv = 0.6981, R2 test = 0.7501 and cRp2 = 0.7476. The predicted pEC50 of all newly designed compounds were higher than that of the template (43). The new compounds were; observed to pass the drug-likeness criteria, uniformly distributed to the brain, and found to be non-mutagenic. Also, the new compounds and the reference drug (doxycycline), were docked onto Ovarian Tumor (OTU) deubiquitinase receptor (PDB ID: 6W9O) using iGEMDOCK tool. This protein is known to help Wolbachia subvert host ubiquitin signaling. The resulting binding scores of the newly designed compounds except A5 were higher than that of doxycycline, while the protein-ligand interactions were majorly characterized by Hydrogen-bonding and hydrophobic interaction types. Therefore, the newly designed molecules could be developed as potential drug candidates for the treatment of lymphatic filariasis and onchocerciasis.

A computational insight into endocrine disruption by polychlorinated biphenyls via non-covalent interactions with human nuclear receptors.

Production of polychlorinated biphenyls (PCBs) was banned a long time ago because of their harmful health effects but humans continue to be exposed to residual PCBs in the environment. In this study, the susceptibility of human nuclear receptors to binding by PCBs was investigated using molecular docking simulation. Findings revealed that PCBs belonging to ortho-substituted, mono-ortho-substituted and non-ortho-substituted congeners could bind to agonistic conformations of androgen (AR), estrogen (ER α and ER ß), glucocorticoid (GR) and thyroid hormone (TR α and TR ß) receptors as well as antagonistic conformation of androgen receptor (AR an) but only ortho-substituted and mono-ortho-substituted PCBs could bind to estrogen receptors in their antagonistic conformations (ER α an and ER ß an). Further molecular docking analyses showed that PCBs mimic the modes of interaction observed for the co-crystallized ligands in the crystal structures of the affected receptors, utilizing 81%, 83%, 78%, 60%, 75%, 60%, 86%, 100% and 75% of the amino acid residues utilized by the co-crystallized ligands for binding in AR, AR an, ER α, ER α an, ER ß, ER ß an, GR, TR α and TR ß respectively. This computational study suggests that PCBs may cause endocrine disruption via formation of non-covalent interactions with androgen, estrogen, glucocorticoid and thyroid hormone receptors.

Theoretical study on endocrine disrupting effects of polychlorinated dibenzo-p-dioxins using molecular docking simulation.

Polychlorinated dibenzo-p-dioxins (PCDDs) are hypothesized to exert their toxic effects in wildlife and humans via endocrine disruption. However, very scanty information is available on the underlying molecular interactions that trigger this disruption. In this study, molecular docking simulation was used to predict the susceptibility of 12 nuclear receptors to disruption via PCDD bindings. Findings revealed that androgen (AR and AR an), estrogen (ER α and ER ß), glucocorticoid (GR) and thyroid hormone (TR α and TR ß) receptors are the most probable protein targets that bind to PCDDs. Further molecular docking analyses showed that PCDD molecules mimic the modes of interaction observed for the co-crystallized ligands of the affected receptors, resulting in the formation of ligand-receptor complexes that were stabilized through electrostatic, van der Waals, pi-effect and hydrophobic interactions with 18, 17, 17, 16, 18, eight and four amino acid residues in the active sites of AR, AR an, ER α, ER ß, GR, TR α and TR ß respectively. The commonalities of these interacting amino acid residues with those utilized by dihydrotestosterone in AR, bicalutamide in AR an, 17ß-estradiol in ER α, 17ß-estradiol in ER ß, cortisol in GR, thyromimetic GC-1 in TR α and thyromimetic GC-1 in TR ß are 86%, 74%, 94%, 80%, 82%, 50% and 43% respectively. The results obtained in this study provide supporting evidence that PCDD molecules may interfere with the endocrine system via binding interactions with some vital amino acid residues in the binding pockets of AR, ERs, GRs and TRs.

QSAR, molecular docking, design, and pharmacokinetic analysis of 2-(4-fluorophenyl) imidazol-5-ones as anti-breast cancer drug compounds against MCF-7 cell line.

The anti-proliferative activities of Novel series of 2-(4-fluorophenyl) imidazol-5-ones against MCF-7 breast cancer cell line were explored via in-slico studies which includes Quantitative structure-activity relationship QSAR, molecular docking studies, designing new compounds, and analyzing the pharmacokinetics properties of the designed compounds. From the QSAR analysis, model number one emerged the best as seen from the arithmetic assessments of (R2) = 0.6981, (R2adj) = 0.6433, (Q2) = 0.5460 and (R2pred) of 0.5357. Model number one was used in designing new derivative compounds, with higher effectiveness against estrogen positive breast cancer (MCF-7 cell line). The Molecular docking studies between the derivatives and Polo-like kinases (Plk1) receptor proved that the derivatives of 2-(4-fluorophenyl) imidazol-5-ones bind tightly to the receptor, thou ligand 24 and 27 had the highest binding affinities of -8.8 and - 9.1 kcal/mol, which was found to be higher than Doxorubicin with a docking score of -8.0 kcal/mol. These new derivatives of 2-(4-fluorophenyl) imidazol-5-ones shall be excellent inhibitors against (plk1). The pharmacokinetics analysis performed on the new structures revealed that all the structures passed the test and also the Lipinski rule of five, and they could further proceed to pre-clinical tests. They both revealed a revolution in medicine for developing novel anti-breast cancer drugs against MCF-7 cell line.

Computational studies of a series of 2-substituted phenyl-2-oxo-, 2-hydroxyl- and 2-acylloxyethylsulfonamides as potent anti-fungal agents.

Botrytis Cinerea is a plant pathogen that affect a large number of plant species like tomatoes, Lettuce, Grapes, and Strawberries among others. Sulfonamides are widely used in pharmaceutical industries as anti-cancer, anti-inflammatory and anti-viral agents. To complement our previous QSAR study, a ligand-based design and ADME/T study were carried out on these sulfonamides compounds for their fungicidal activity toward "Botrytis Cinerea". With the help of AutoDock Vina version 4.0 in Pyrex software, the docking analysis was performed after optimization of the compounds at DFT/B3LYP/6-31G∗ quantum mechanical method using Spartan 14 softwar. Using the model generated in the previous QSAR work, the descriptors of the chosen model were considered in modifying the most promising compound '9' in which twelve (12) derivatives were designed and found to have better activity than the template (compound 9). With compound 9j having the highest activity that turns out to be about 14 and 15 times more potent than the commercial fungicides "procymidone and chlorothalonil". Furthermore, ADME/T properties of the designed compounds were calculated using the SwissADME online tool in which all the compounds were found to have good pharmacokinetic profile. Moreover, a molecular docking study on selected compounds of the dataset (compound 8, 13, 14, 19, 20, 21, 22 and 29) revealed that compound '20' turned out to have the highest docking score of -8.5 kJ/mol. This compound has a strong affinity with the macromolecular target point (PDB ID: 3wh1) producing H-bond and hydrophobic interaction at the target point of amino acid residue. The molecular docking analysis gave an insight on the structure-based design of the new compounds with better activity against B. cinerea.

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2.

A dataset of seventy-two (72) cytotoxic compounds of the National Cancer Institute (NCI) was studied by QSAR and docking approaches to gain deeper insights into ligands selectivity on SK-MEL-2 cell line. The QSAR model was built using fifty (50) molecules and the best-generated model based on multiple linear regression showed, respectively good quality of fits ( R 2 (0.864), R a d j u s t e d 2 (0.845), Q2 cv (0.799) and R p r e d 2 (0.706)). The model's predictive ability was determined by a test set of twenty-two (22) compounds. Compounds 30 and 41 were selected as templates for in silico design because they had high pGI50 activity and are in the model's applicability domain. The obtained information from the model was explored to design novel molecules by introducing various modifications. Moreover, the designed compounds with better-predicted activity (pGI50) values were selected and docked on the active site of the protein (PDB-CODE: 3OG7) which is responsible for melanoma cancer to elucidate their binding mode. AN2 (-12.1kcalmol-1) and AC4 (-12.4kcalmol-1) showed a better binding score for the target when compared with (vemurafenib, -11.3kcalmol-1) the known inhibitor of the target (V600E-BRAF). These findings may be very helpful in early anti-cancer drug development.

In silico studies of piperazine derivatives as potent anti-proliferative agents against PC-3 prostate cancer cell lines.

Quantitative Structure Activity Relationship studies were carried out on arylpiperazine derivatives to investigate their anti-proliferate activity against prostate PC-3 cancer cell lines. The built model with statistical parameters; R2 = 0.8483, R2 adj = 0.8078, Q2 cv = 0.7122 and external validation (R2 test) 0.6682 revealed that the anti-proliferate activities were strongly dependent on the descriptors: MATS7c, MATS3e, maxwHBa and WPSA-3. The Variance Inflation Factor of the descriptors were all greater than one but less than two and all descriptors were poorly correlated (r < 0.4). A graph of the experimental activities and predicted activities showed a high correlation and a William's plot showed the presence of only one outlier compound. These results are similar to those reported for stable and robust models with high predicting power. Molecular docking studies of compounds 5 (1-phenyl-4-(4-(2-(p-tolyloxy)ethyl)benzyl)piperazine) and 17 (4-(4-((4-phenylpiperazin-1-yl)methyl)phenethoxy)benzonitrile) with the androgen receptor gave binding affinities of -7.5 and -7.1 kcal/mol respectively. Compound 5 formed a more stable complex having hydrogen, electrostatic and hydrophobic bond interactions while compound 17 had hydrogen and hydrophobic bond interactions only. This study provides a roadmap to the design of more potent anti-prostate cancer compounds.

Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors.

QSAR modelling on Thirty (34) novel quinazoline derivatives (EGFRWT inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFRWT inhibitors were optimized at B3LYP/6-31G* level of theory using Density Functional Theory (DFT) method. Multi-Linear Regression using Genetic Function Approximation (GFA) method was adopted in building the models. The best one among the models built was selected and reported because it was found to have passed the minimum requirement for the assessment of QSAR models with the following assessment parameters: R2 of 0.965901, R2 adj of 0.893733, Qcv 2 of 0.940744, R2 test of 0.818991 and LOF of 0.076739. The high predicted power, reliability, robustness of the reported model was verified further by subjecting it to other assessments such VIF, Y-scrambling test and applicability domain. Molecular docking was also employed to elucidate the binding mode of some selected EGFRWT inhibitors against EGFR receptor (4ZAU) and found that molecule 17 have the highest binding affinity of -9.5 kcal/mol. It was observed that the ligand interacted with the receptor via hydrogen bond, hydrophobic bond, halogen bond, electrostatic bond and others which might me the reason why it has the highest binding affinity. Also, the ADME properties of these selected molecules were predicted and only one molecule (34) was found not orally bioavailable because it violated more than the permissible limit set by Lipinski's rule of five filters. This findings proposed a guidance for designing new potents EGFRWT inhibitors against their target enzyme.

In-silico activity prediction and docking studies of some 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as Anti-proliferative agents.

In-silico activity prediction was performed to predict new inhibitory activities of 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as anti-proliferative agents using QSAR technique. The anti-proliferative agents were optimized using Density Functional Theory (DFT) method utilizing the B3LYP/6-31G* level of theory. Genetic Function Algorithm (GFA) was used to build the QSAR models. Out of the models built, the best one was selected and reported because of its fitness statistically with the following assessment parameters: R2 trng = 0.919035, R2 adj = 0.893733, Q2 cv = 0.866475, R2 test = 0.636217, and LOF = 0.215884. The selected model was further subjected to other assessment such as VIF, Y-scrambling test, applicability domain and found to be statistically significant. The binding mode of some selected 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9H-purine (ligands) in the active site of EGFR-tyrosine kinase (EGFR-TK) (receptor) was studied via Molecular docking. Molecule 22 was identified to have the highest binding energy (-10.4 kcal/mol) among the other selected ligands which it might be as a result of hydrogen interactions formed with MET793 (2.48599 Å, 2.04522 Å) & THR854 (3.76616 Å) amino acid residues and hydrophobic/other interactions with amino acid residues (LEU718, LEU844, MET766, VAL726, ALA743, LYS745 and MET790) in the active site of EGFR-tyrosine kinase (EGFR-TK). The drug-likeness of these selected anti-proliferative agents were predicted via the pharmacokinetics profile of the molecules utilizing SWISS ADME. The anti-proliferative agents were found to be orally safe by not having more than 1 violation of the Lipinski's rule of five. This research proposed a way for designing potent anti-proliferative agents against their target enzyme.

Molecular design of antioxidant lubricating oil additives via QSPR and analysis dynamic simulation method.

Alternatives antioxidant lubricant additives have been proposed by many researchers to replace long-time use of multifunctional lubricant additive, Zinc-dialkyl-dithiophosphate (ZDDP). Computational methods (QSPR and MD) were successfully used to design five novel anti-oxidant lubricating oil additives with improved properties and dynamic binding energies. The five novel antioxidant lubricant additives with improved properties and without sulfated ash, phosphorus, and sulfur (SAPS) were successfully designed. These group of newly designed additives were better than other similar research from the literature and could stop or terminate complete oxidation of the lubricant. Moreover, the result of molecular dynamics simulations (MD) in which 3-(2-(3-amino-4,5-dihydroxyphenyl)-3-chloro-4-oxoazetidin-1-yl)-2-argioquinazolin-4(3H)-one with the most promised dynamic binding energy of -1487.68 kcal/mol was found to be dynamically bound better on the simulated steel coated surface than the DLC coated surface and was also revealed to be excellently good when compared with commercially sold multifunctional additives, ZDDP (197.143 kcal/mol). These groups of five newly designed additives could be easily synthesized in the wet laboratory by adding -OH and or NH2 around the ortho, meta and para position of the phenyl group of the structure template. This research will help designing new oxidation resistance lubricating oil additives with improved properties that will reduce the capacity of base oil to oxidize and form sludge during the autoxidation process of the lubricating oil.