Combinatorial Chemistry & High Throughput Screening - Volume 20, Issue 4, 2017

Background: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides. Methods: The present study is aimed to generate accurate classification models using machine learning techniques, which could identify IDE modulators from a bioassay dataset consisting of IDE inhibitors as well as non-inhibitors. The identified compounds were subjected to docking and Molecular dynamics (MD) studies for an in-depth analysis of the binding modes along with the complex stability. This study proposes that the identified potential active compounds, STK026154 (PubChem ID: CID2927418) with Glide score of -7.70 kcal/mol and BAS05901102 (PubChem ID: CID3152845) with Glide score of -7.06 kcal/mol, could serve as promising leads for the development of novel drugs against AD. Conclusion: The present study shows that such in silico approaches can be effectively used to discover and select active compounds from unseen data for accelerated drug development process. The machine learning models generated in the present study were used to screen Traditional Chinese Medicine (TCM) database to identify the phytocompounds already been reported to have therapeutic effects against AD.

Aim and Objective: Altered activity of PI3K/mTOR signaling pathway is one of the most common aberrations found in various forms of neoplastic lesions. Dual inhibition of PI3K and mTOR represents a reasonably attractive concept in potential cancer treatment. The main aim of this work was to design novel PI3K/mTOR inhibitors with enhanced antiproliferative activity. Materials and Methods: 3D-QSAR pharmacophore modeling studies were performed on two groups comprised of 37 and 48 dual PI3K/mTOR inhibitors. Obtained 3D-pharmacophores were used in design of new dual PI3K/mTOR inhibitors. Based on the in silico ADMET data, structure-based virtual screening and docking studies, the most promising novel candidates were selected. Results: Four reliable PLS models with good statistical parameters (q² = 0.72, r² pred = 0.93; q² =0.81, r² pred = 0.88 for 3D-QSAR (mTOR) models and q² = 0.79, r²pred = 0.93; q² = 0.79, r²pred 0.94 for 3D-QSAR (PI3K) models) were obtained and new highly selective and potent dual PI3K/mTOR inhibitors were designed. Further in silico ADMET profiling of the designed compounds selected the most promising novel PI3K/mTOR inhibitors as drug candidates. Results of the 3D-QSAR studies were confirmed by structure-based virtual screening protocol that identified selected designed compounds as a best fit for PI3K and mTOR receptors. Molecular docking studies on PI3K and mTOR crystal structures revealed the key active site residues involved in binding of PI3K/mTOR ligands. Conclusion: After combining the results of 3D-QSAR, ADMET profiling, virtual screening and docking, compounds 56-57 and 56-62 were chosen as the most promising new dual PI3K/mTOR inhibitors.

Aims & Scope: Thiazole derivatives are produced using one-pot multicomponent reactions of acid chlorides, potassium thiocyanate, amino acids, alkyl bromides and ZnO nanorods (NR-ZnO) as the catalyst in water at ambient temperature. These reactions were no't performed without using NR-ZnO as the catalyst. Nanorods of ZnO have been prepared by reflux procedure using sodium dodecylsulfate (SDS). Nanorods of ZnO showed a considerable improvement in the yield of the product and displayed significant reusable activity. Materials and Methods: In these reactions, all chemicals were prepared from Fluka (Buchs, Switzerland). Nanorods of ZnO were synthesized in the laboratory according to literature report. By using an electrothermal 9100 apparatus, melting points of synthesized compounds were determined. Heraeus CHN-O-Rapid analyzer was employed for elemental analyses for C, H, and N. FINNIGANMAT 8430 spectrometer operating at an ionization potential of 70 eV was used for mass spectra. Shimadzu IR-460 spectrometer was employed for IR spectra. BRUKER DRX-500 AVANCE spectrometer at 500.1 and 125.8 MHz was used for ¹H, and ¹³C NMR spectra for solutions in DCl3 with TMS as internal standard or 85% H3PO4 as external standard, respectively. Results: We describe a facile and green synthetic method for the synthesis of thiazole derivatives 5 from acid chlorides, potassium thiocyanate, alkyl bromides and amino acids using NR-ZnO- as the catalyst in water at room temperature. Conclusion: In conclusion, we describe an efficient, green procedure and high yielding synthesis of thiazole derivatives using acid chlorides, potassium thiocyanate, alkyl bromides and amino acids in the presence of NR-ZnO as the catalyst in water at room temperature.

Objective: Metabolic disorders associated with diabetic patients are a serious concern. Aldose reductase (ALR2) has been identified as first rate-limiting enzyme in the polyol pathway which catalyzes the reduction of glucose to sorbitol. It represents one of the validated targets to develop potential new chemical entities for the prevention and subsequent progression of microvascular diabetic complications. In order to further understand the intricate structural prerequisites of molecules to act as ALR2 inhibitors, ligand-based pharmacophore model, atombased 3D-QSAR and structure based drug design studies have been performed on a series of 2,4-thiazolidinedione derivatives with ALR2 inhibitory activity. Methods: In the present study, a validated six point pharmacophore model (AAADNR) with three hydrogen bond acceptor (A), one hydrogen bond donor (D), one negative ionic group (N) and one aromatic ring (R) was developed using PHASE module of Schrodinger suite with acceptable PLS statistics (survival score = 3.871, cross-validated correlation coefficient Q² = 0.6902, correlation coefficient of multiple determination r² = 0.9019, Pearson-R coefficient = 0.8354 and F distribution = 196.2). In silico predictive studies (pharmacophore modeling, atom-based 3D QSAR and docking combined with drug receptor binding free energetics and pharmacokinetic drug profile) highlighted some of the important structural features of thiazolidinedione analogs required for potential ALR2 inhibitory activity. Results: The result of these studies may account to design a legitimate template for rational drug design of novel, potent and promising ALR2 inhibitors.

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Materials & Methods: Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. Results & Conclusion: The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance.

Background and Objective: The rapidly growing number of protein data available creates necessity of computational methods with low complexity to infer accurate protein structure, function, and evolution. Method: A new description of proteins based on five topological indices of star-like graph representation and the occurrence frequency of 20 amino acids was proposed to compare the similarities of proteins. Results: A phylogenetic tree of eight ND6 proteins was constructed to demonstrate the effectiveness and rationality of our approach. Analogously, we applied this method to RNA polymerase proteins of some subtypes of influenza virus to infer their phylogenetic relationship. The results showed that the phylogenetic relationship among RNA polymerase of influenza virus is closely related to distributions of species virus host and geographical distribution. Conclusion: This novel approach is based on a mapping which can be recaptured mathematically without loss of information.

Aim and Objective: Due to biological activity of a significant number of compounds containing benzothiazole ring system and in continuation of our interest in developing new environmentally benign methods for the synthesis of heterocyclic compounds by MCRs in this study we performed an efficient three-component from benzothiazole, acetylenic esters and hydroxyl aromatics compounds to synthesize of 2- benzothiazole derivatives in high yield. Materials and Methods: IR spectra were recorded using an FTIR apparatus. Melting points measured on an Electrothermal 9100 apparatus. Spectra were obtained in solution of CDCl3 using TMS as internal standard. Elemental analyses were carried out using a Heracus CHN–O– Rapid analyzer. A mixture of benzothiazole, dimethyl acetylenedicarboxylate and phenol were placed in a mortar. The mixture was ground with a mortar and pestle at room temperature for 12 min. After completion of the reaction, as indicated by TLC (ethyl acetate: n-hexane, 1: 3), the solvent was distilled off under reduced pressure and the residue was crystallized from diethyl ether. Results: Treatment of benzothiazole and dialkyl acetylenedicarboxylates in presence of resorcinol and β-naphthol led to products 4 (dialkyl (E)-2-(2-(2-hydroxyphenyl)benzo[d]thiazol-3(2H)-yl-3- methylbut-2-enedioate), while we observed two isomer (Z) and (E) configurations (major and minor) in nearly 70:30 ratio when the reaction was repeated in presence of 8-hydroxy quinolone and DMAD. Also when we examined 2-nitro phenol, 4-nitrophenol, and 4-hydroxy quinoline, only one product 5 was obtained. This indicates that the reaction proceeds efficiently with electron-releasing substituted phenols. Conclusion: The reaction between benzothiazole and dialkyl acetylenedicarboxylates in the presence of some phenols without electron-withdraw substitution, presents a novel, one-pot, clean, convenient, simple and inexpensive approach into the synthesis of 2-benzothiazole derivatives of potential synthetic and pharmacologically interest. This procedure carries significant advantages because of the minimization of labor, time, and cost.

Background: Chemical toxicity is an important reason for late-stage failure in drug R However, it is time-consuming and expensive to identify the multiple toxicities of compounds using the traditional experiments. Thus, it is attractive to build an accurate prediction model for the toxicity profile of compounds. Materials and Methods: In this study, we carried out a research on six types of toxicities: (I) Acute Toxicity; (II) Mutagenicity; (III) Tumorigenicity; (IV) Skin and Eye Irritation; (V) Reproductive Effects; (VI) Multiple Dose Effects, using local lazy learning (LLL) method for multi-label learning. 17,120 compounds were split into the training set and the test set as a ratio of 4:1 by using the Kennard-Stone algorithm. Four types of properties, including molecular fingerprints (ECFP_4 and FCFP_4), descriptors, and chemical-chemical-interactions, were adopted for model building. Results: The model ‘ECFP_4+LLL’ yielded the best performance for the test set, while balanced accuracy (BACC) reached 0.692, 0.691, 0.666, 0.680, 0.631, 0.599 for six types of toxicities, respectively. Furthermore, some essential toxicophores for six types of toxicities were identified by using the Laplacian-modified Bayesian model. Conclusion: The accurate prediction model and the chemical toxicophores can provide some guidance for designing drugs with low toxicity.