Current Computer - Aided Drug Design - Volume 15, Issue 3, 2019

Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.

Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.

Background: Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance. Objective: In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives. Methods: The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation. Results: Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models. Conclusion: According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.

Background: Phosphodiesterase 4 (PDE4) and phosphodiesterase 7 (PDE7), PDE superfamily members, increase inflammatory processes in immunomodulatory as well as pro-inflammatory cells via breakdown of cyclic adenosine monophosphate. Dual inhibitors of PDE4 and PDE7 are a novel class of drug candidates which can regulate pro-inflammatory as well as T-cell function and can be particularly advantageous in the treatment of a wide-ranging disorders associated with the immune system as well as inflammatory diseases with fewer unwanted adverse effects. Objective: The current research work was planned to design and synthesize some newer substituted 1,3- thiazolidine-2,4-dione derivatives as dual inhibitors of PDE4 and PDE7 followed by evaluation of their anti-inflammatory activity and in silico docking studies. Methods: A new series of substituted 1,3-thiazolidine-2,4-dione derivatives was synthesized followed by evaluation of their anti-inflammatory activity in animal models. In silico docking studies were performed for the evaluation of the binding pattern of synthesized derivatives in the binding site of both PDE4 and PDE7 proteins. Results: Amongst the newly synthesized derivatives, compounds 5 and 12 showed higher antiinflammatory activity in the animal model. The results of in vivo animal studies were found to be in concordance with the results of molecular docking studies. Conclusion: These newly synthesized derivatives can act as the lead molecules for the design of safe and therapeutically effective agents for various inflammatory diseases acting via inhibition of both PDE4 and PDE7.

Background: A considerable worldwide increase in the rate of invasive fungal infections and resistance toward antifungal drugs was witnessed during the past few decades. Therefore, the need for newer antifungal candidates is paramount. Nature has been the core source of therapeutics for thousands of years, and an impressive number of modern drugs including antifungals were derived from natural sources. In order to facilitate the recognition of potential candidates that can be derived from natural sources, an iterative stochastic elimination optimization technique to index natural products for their antifungal activity was utilized. Methods: A set of 240 FDA-approved antifungal drugs, which represent the active domain, and a set of 2,892 natural products, which represent the inactive domain, were used to construct predictive models and to index natural products for their antifungal bioactivity. The area under the curve for the produced predictive model was 0.89. When applying it to a database that is composed of active/inactive chemicals, we succeeded to detect 42% of the actives (antifungal drugs) in the top one percent of the screened chemicals, compared with one-percent when using a random model. Results and Conclusion: Eight natural products, which were highly scored as likely antifungal drugs, are disclosed. Searching PubMed showed only one molecule (Flindersine) out of the eight that have been tested was reported as an antifungal. The other seven phytochemicals await evaluation for their antifungal bioactivity in a wet laboratory.

Objective: We present three robust, validated and statistically significant quantitative structure-activity relationship (QSAR) models, which deal with the calculated molecular descriptors and experimental inhibition constant (Ki) of 42 coumarin and sulfocoumarin derivatives measured against CA I and II isoforms. Methods: The compounds were subjected to DFT calculations in order to obtain quantum chemical molecular descriptors. Multiple linear regression algorithms were applied to construct QSAR models. Separation of the compounds into training and test sets was accomplished using Kennard-Stone algorithm. Leverage approach was applied to determine Applicability Domain (AD) of the obtained models. Results: Three models were developed. The first model, CAI_model1 comprises 30/11 training/test compounds with the statistical parameters of R2=0.85, Q2=0.77, F=27.57, R2 (test) =0.72. The second one, CAII_model2 comprises 30/12 training/test compounds with the statistical parameters of R2=0.86, Q2=0.78, F=30.27, R2 (test) =0.85. The final model, ΔpKi_model3 consists of 25/3 training/ test compounds with the statistical parameters of R2=0.78, Q2=0.62, F=13.80 and R2(test) =0.99. Conclusion: Interpretation of reactivity-related descriptors such as HOMO-1 and LUMO energies and visual inspection of their maps of orbital electron density leads to a conclusion that the binding free energy of the entire binding process may be modulated by the kinetics of the hydrolyzing step of coumarins.

Background: Acquired immunodeficiency Syndrome (AIDS) is caused by Human immunodeficiency virus type 1 (HIV-1). Pyrazine and Thiazolidinone pharmacophore has diverse biological activities including anti HIV activity. Aims and Objectives: To study binding behavior of Pyrazine- thiazolidinone derivatives on four different crystal structures of HIV- 1RT.These molecules which were already reported as anti-TB were investigated for dual activity as Anti-HIV and Anti-TB. Materials and Methods: In the present study we describe a comparative docking study of twentythree derivatives of N-(4-oxo-2 substituted thiazolidin-3-yl) pyrazine-2-carbohydrazide. Binding pattern of these derivatives was gauged by molecular docking studies on four different receptors bearing PDB code 1ZD1, 1RT2, 1FKP and 1FK9 of HIV–RT enzyme using V. Life MDS software Genetic algorithm docking method. Result and Discussion: The studies revealed hydrogen bonds, hydrophobic interaction and pi-pi interactions playing significant role in binding of the molecules to the enzyme. Conclusion: Most of the molecules have shown good dock score and binding energy with anti-HIV receptors but Molecules 13 and 14 have potential to act as anti-tubercular and Anti HIV and hence can be further explored for dual activity.

Introduction: Steroid compounds are widely distributed in nature throughout scientific history. Living organisms such as animals and vegetables have steroids that show a significant effect on their vital activities. Sterols are key components of all eukaryotic cell membranes. Methods: Steroidal compounds; 3β-oxo-[1’,3’,2’-oxathiaphos-phalidine-2’-one] stigmast-5-ene and 3β- oxo[1`,3`,2`-dioxaphosphalidine-2`-one]-stigmast-5-ene were successfully prepared using easily accessible 3β-hydroxy stigmast-5-ene with phosphorous oxychloride (POCl3), 2- mercaptoethanol/ethylene glycol and triethylamine (Et3N) in dry diethyl ether. Products were obtained in semi-solid state and characterized using physicochemical techniques. Results: The results of the bioassay showed that the synthesized compound containing the sulfur atom had antibacterial activity. Molecular docking was also done in order to show in silico antibacterial activity and to make out the probable binding mode of compound with the amino acid residues of protein. Conclusion: The results of the docking study showed that synthesized compound 2 had minimal binding energy with substantial affinity for the active site.

Introduction: The regulation of apoptosis via compounds originated from marine organisms signifies a new wave in the field of drug discovery. Marine organisms produce potent compounds as they hold the phenomenal diversity in chemical structures. The main focus of drug development is anticancer therapy. Methods: Expertise on manifold activities of compounds helps in the discovery of their derivatives for preclinical and clinical experiment that promotes improved activity of compounds for cancer patients. Results: These marine derived compounds stimulate apoptosis in cancer cells by targeting Bcl-2 and Survivin, highlighting the fact that instantaneous targeting of these proteins by novel derivatives results in efficacious and selective killing of cancer cells. Conclusion: Our study reports the identification of Aplysin and Haterumaimide J as Bcl-2 inhibitors and Cortistatin A as an inhibitor of survivin protein, from a sequential virtual screening approach.