QSAR-based solubility model for drug-like compounds

Solubility plays a very important role in the selection of compounds for drug screening. In this context, a QSAR model was developed for predicting water solubility of drug-like compounds. First, a set of relevant parameters for establishing a drug-like chemical space was defined. The comparison of chemical structures from the FDAMDD and PHYSPROP databases allowed the selection of properties that were more efficient in discriminating drug-like compounds from other chemicals. These filters were later on applied to the PHYSPROP database and 1174 chemicals fulfilling these criteria and with experimental solubility information available at 25 °C were retained. Several QSAR solubility models were developed from this set of compounds, and the best one was selected based on the accuracy of correct classifications obtained for randomly chosen training and validation subsets. Further validation of the model was performed with a set of 102 drugs for which experimental solubility data have been recently reported. A good agreement between the predictions and the experimental values confirmed the reliability of the QSAR model.     

Design and synthesis of novel series of 5-HT6 receptor ligands having indole,a central aromatic core and 1-amino-4 methyl piperazine as a positive ionizable group

The exclusive distribution of 5-HT₆ receptor in the brain regions and high affinity for antipsychotic and antidepressant drugs makes 5-HT₆ receptor a promising target in treatment of CNS diseases. Based on a pharmacophore model reported in the literature, we designed and synthesized a novel series of 5-HT₆ receptor ligands having indole as a central aromatic core and 1-amino-4-methyl piperazine as positive ionizable group. Out of 32 compounds we have successfully identified 10 new compounds as 5-HT₆ receptor antagonists. The structure–activity relationship (SAR) studies have been carried out by mapping the compounds with the 3D QSAR model.     

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.     

Chemometric modelling of antimalarial activity of aryltriazolylhydroxamates

We have performed quantitative structure–activity relationship (QSAR) and quantitative activity–activity relationship (QAAR) studies for aryltriazolylhydroxamates having antimalarial activity data against both chloroquine-sensitive (D6 clone) and chloroquine-resistant (W2 clone) strains of Plasmodium falciparum to understand the relationships between the biological activity and molecular properties for the design of new compounds. The QSAR studies were performed using 35 compounds among which 26 molecules were taken using k-means clustering technique in the training set for the derivation of the QSAR models and nine molecules were kept as the test-set compounds to evaluate the predictive ability of the derived models. The chemometric tool used for the analysis was the genetic function approximation. The developed models were analysed in terms of their predictive ability, and comparable results were obtained for cross-validated predictive variance (Q ²) and externally predicted variance (R ² _pred) values (0.761 and 0.829, respectively, for the D6 model, 0.708 and 0.748, respectively, for the W2 model and 0.984 and 0.982, respectively for the QAAR model). The QSAR models suggest that the number of methylene groups (between the triazolyl and hydroxamate moieties) and partially negatively charged surface areas of the molecules are important parameters for the antimalarial activity.     

Structure-based screening,ADMET profiling,and molecular dynamic studies on mGlu2 receptor for identification of newer antiepileptic agents

Structure-based screening approach targeting mGlu2 receptor was carried out to identify good chemical starting points for anti-epileptic therapy. Interactive modes of final 12 compounds identified on the basis of screening of Asinex library, binding energy analysis, ADME profiling with special emphasis for CNS ranges, and toxicity analysis were studied and showed good binding modes in the mGluR2-active site. Enrichment studies for validating screening protocol were carried out which gave ROC values 0.98 (AUC = 0.96) for SP, 0.97 (AUC = 0.95) for XP with BEDROC analysis. Our results indicate that all the 12 hits showed good CNS drug-like properties, have better binding free energy and ADME profile as compared to co-crystallized ligand with the best ligand hit retaining conserved hydrogen bond interactions with Ala-166, Thr-168, Ser-145, and Arg-61 residues in bilobatevenus fly-trap domain of mGluR2 receptor. Molecular dynamics simulations proved that the two potential hits, qualifying all screening parameters, are stable in the receptor active site pocket, confirming the potential of the identified hits as a specific target for mGluR2. Because the newly discovered mGluR2 agonists are structurally different with T_c values ranging from 0.57 to 0.92, all of them can be considered for further de novo design methods.     

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation.Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their K_D values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range.     

Pharmacophore mapping,molecular docking and QSAR studies of structurally diverse compounds as CYP2B6 inhibitors

Pharmacophore mapping, molecular docking and quantitative structure–activity relationship (QSAR) studies were carried out for a structurally diverse set of 48 compounds as CYP2B6 inhibitors. The generated best pharmacophore hypotheses from the three methods of conformer generation (FAST, BEST and conformer algorithm based on energy screening and recursive buildup) indicate the importance of two features, namely, hydrogen bond acceptor [electron-rich centre] and ring aromaticity. The distance between the two centres of the important features for ideal inhibitors varied from 5.82 to 6.03 Å. The chemometric tools used for the QSAR analysis were genetic function approximation (GFA) and genetic partial least squares. The developed QSAR models indicate the importance of an electron-rich centre, size of molecule, impact of branching and ring system and distribution of charges in the molecular surface. The docking study confirms the importance of an electron-rich centre for binding with the iron atom of the cytochrome enzyme. A GFA model with spline option was found to be the best model based on internal validation as well as the r ² _{m (overall)} criterion (Q ² = 0.772, r ² _{m (overall)} = 0.774). According to the external prediction statistics (R ² _pred = 0.876), another GFA-derived model with spline option outperforms the remaining models.     

2D QSAR,Design, and in Silico Analysis of Thiophene-Tethered Lactam Derivatives as Antimicrobial Agents

Background : A very high rate of resistance causes health-care-associated and community-acquired infections. E. coli is one of the nine pathogens of highest concern to most of the antibiotics and other class of antimicrobials. Objective : The objective of the present study is to develop novel thiophene derivatives using 2D QSAR and in silico approach for E. coli resistance. Methods : Substituted thiophene series reported by Nishu Singla et al., were taken for QSAR analysis. From the results, a set of 15 new compounds were designed. A complete in silico analysis has been done using PADEL, Autodock vina, Swiss ADME, Protox II software. Results : The designed compounds obey the Lipinski's rule of five and were known to have excellent inhibitory action (pIC₅₀ values −0.87 to −1.46) which is similar to the most active compound of the data set (pIC₅₀ −0.69) taken for the study. The bioavailability score (0.65) with no toxicity representing that the designed compounds are suitable for oral administration. Conclusion : The designed compounds are inactive for mutagenicity and cytotoxicity and ADMET studies states that these molecules are likely to be orally bioavailable and could be easily transported, diffused, and absorbed. So, the designed compounds will definitely serve as a lead antibacterial agent for E. coli resistance.     

2D QSAR studies on thyroid hormone receptor ligands

2D QSAR studies were carried out for a series of 55 ligands for the Thyroid receptors, TRalpha and TRbeta. Significant cross-validated correlation coefficients (q(2)=0.781 (TRalpha) and 0.693 (TRbeta)) were obtained. The models' predictive abilities were proved more valuable than the classical 2D-QSAR, and were further investigated by means of an external test set of 13 compounds. The predicted values are in good agreement with experimental values, suggesting that the models could be useful in the design of novel, more potent TR ligands. Contribution map analysis identified a number of positions that are promising for the development of receptor isoform specific ligands.     

An Insight into the Structural Requirements and Pharmacophore Identification of Carbonic Anhydrase Inhibitors to Combat Oxidative Stress at High Altitudes: An In-Silico Approach

Carbonic anhydrases (CA) inhibitory action could be linked to the treatment of a number of ailments, including cancer, osteoporosis, glaucoma, and several neurological problems. For the development of effective CA inhibitors, a variety of heterocyclic rings have been investigated. Furthermore, at high altitudes, oxygen pressure drops, resulting in the formation of reactive oxygen and nitrogen species, and CA inhibitors having role in combating this oxidative stress. Acetazolamide contains thiadiazole ring, which has aroused researchers’ interest because of its CA inhibitory action. In the present study, we used a number of drug design tools, such as pharmacophore modeling, 3D QSAR, docking, and virtual screening on twenty-seven 1,3,4-thiadiazole derivatives that have been described as potential CA inhibitors in the literature. An atom-based 3D-QSAR analysis was carried out to determine the contribution of individual atoms to model generation, while a pharmacophore mapping investigation was carried out to find the common unique pharmacophoric properties required for biological activity. The coefficient of determination for both the training and test sets were statistically significant in the generated model. The best QSAR model was chosen based on the values of R² (0.8757) and Q² (0.7888). A molecular docking study was also conducted against the most potent analogue 4m, which has the highest SP docking score (−5.217) (PDB ID: 6g3v). The virtual screening revealed a number of promising compounds. The screened compound ZINC77699643 interacted with the amino acid residues, Pro201 and Thr199, in the virtual screening study (PDB ID: 6g3v). These interactions demonstrated the significance of the CA inhibitory activity of the compound. Furthermore, ADME study revealed useful information regarding compound’s drug-like properties. Therefore, the findings of the present investigation could aid in the development of more potent CA inhibitors, which could benefit the treatment of oxidative stress at high altitudes.     

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation,protein–ligand interaction fingerprint, 3D pharmacophore,docking and 3D QSAR

Abstract

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.

Communicated by Ramaswamy H. Sarma     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

New 8-amino-1,2,4-triazolo[4,3-a]pyrazin-3-one derivatives. Evaluation of different moieties on the 6-aryl ring to obtain potent and selective human A2A adenosine receptor antagonists

In this work, further structural investigations on the 8-amino-2-phenyl-6-aryl-1,2,4-triazolo[4,3-a]pyrazin-3-one series were carried out to achieve potent and selective human A_2A adenosine receptor (AR) antagonists. Different ether and amide moieties were attached at the para-position of the 6-phenyl ring, thus leading to compounds 1–9 and 10–18, respectively. Most of these moieties contained terminal basic rings (pyrrolidine, morpholine, piperidine and substituted piperazines) which were thought to confer good physicochemical and drug-like properties.Compounds 11–16, bearing the amide linker, possessed high affinity and selectivity for the hA_2A AR (K_i = 3.6–11.8 nM). Also derivatives 1–9, featuring an ether linker, preferentially targeted the hA_2A AR but with lower affinity, compared to those of the relative amide compounds. Docking studies, carried out at the hA_2A AR binding site, highlighted some crucial ligand-receptor interactions, particularly those provided by the appended substituent whose nature deeply affected hA_2A AR affinity.