In-silico Studies of Thiopyridine Compounds as Anti-Bacterial agents Targeting Enoyl - Acyl Carrier Protein Reductase

In the Fatty Acid Synthase II system, Enoyl-(acyl-carrier-protein) reductase (ENR) encoded by FabI genes is a limiting step enzyme and there is no homologue ENR found in invertebrates which makes it selective target for drug discovery. From Molecular dynamics simulations it was concluded that the solvated protein stabilized at 2.5 ns with larger mobility in the substrate - binding loop and the conformational flexibility of the molecule was revealed. To study the inhibitory effects of novel small molecules in the thiopyridine series, a 2D QSAR model was developed and evaluated for its efficiency. The R2 > 0.96 and Q2 = 0.978 depicted the predictive ability of the models which was determined using a test set of 3 compounds. The receptor-ligand interactions were studied and highest affinity was reported for GCT ID, 343129 (-9.09 Kcal/mol), 341772 (-8.90 Kcal/mol) and 268776 (-8.85 Kcal/mol). These compounds were analysed for their drug like properties and toxicity which projected acceptable blood brain barrier permeation and human intestinal absorption and reduced lipotoxicity. Thus the results suggest further synthesis of new thipyridine series of compounds and experimental testing against drug resistant Staphylococcal infections

Plasmodium falciparum Dihydroorotate Dehydrogenase, Fragment-based Drug Design, 2D-QSAR, DFT Calculation, Lead Optimization, Induced Fit Docking

Plasmodium falciparum dihydroorotate dehydrogenase (PfDODH) is one of the enzymes currently explored in the treatment of malaria. Although there is currently no clinically approved drug targeting PfDODH, many of the compounds in clinical trials have [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine backbone structure. This study sought to design new compounds from the fragments of known experimental inhibitors of PfDODH. Nine experimental compounds retrieved from Drug Bank online were downloaded and broken into fragments using Schrodinger power shell; the fragments were recombined to generate new ligand structures using BREED algorithm. The new compounds were docked with PfDODH crystal structure, after which the compounds were filtered with extensive drug-likeness and toxicity parameters. A 2D-QSAR model was built using the multiple linear regression method and externally validated. The compounds electronic behaviours were studied using DFT calculations. Structural investigation of the six designed compounds, which had lower binding energies than the standard inhibitors, showed that five of them had [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine moieties and interacted with essential residues at the PfDODH binding site. In addition to their drug-like and pharmacokinetic properties, they also showed minimal toxicities. The externally validated 2D-QSAR model with R2 and Q2 values of 0.6852 and 0.6691, confirmed the inhibitory prowess of these compounds against PfDODH. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attack. The current study thus provides insight into the development of a new set of potent PfDODH inhibitors.

Anti-breast cancer drug screening based on Neural Networks and QSAR model

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

4D-QSAR Analyses for EGFR Inhibitors Based on CDDA-OPS-GA Method

Epidermal growth factor receptor is a preferred target for treating cancer. Compared to 3D-QSAR, 4D-QSAR has the feature of conformational flexibility and free alignment for individual ligands. In present studies, the 4D-QSAR of 131 analogs of 4-anilino quinazoline for EGFR inhibitors was built. The GROMACS package was employed to yield the conformational ensemble profile. The field descriptors of Coulomb and Lennard−Jones potentials were calculated by LQTA-QSAR. The filter descriptors and variable selection is very important, which was performed by means of comparative distribution detection algorithm (CDDA), ordered predictors selection (OPS) and genetic algorithm (GA) method. Best 4D-QSAR model yielded satisfactory statistics (R2 = 0.71), good performance in internal (Q2LOO = 0.60) and external prediction (R2pred = 0.69, k = 0.97, k′ = 1.01). The 4D-QSAR was shown to be robust (Q2LMO = 0.59) and was not built by chance (R2YS = 0.17, Q2YS = −0.25). The model has a good potential for rational design new EGFR inhibitors.

3D-QSAR Studies of 1,2,4-Oxadiazole Derivatives as Sortase A Inhibitors

Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the “S. aureus Sortase A” substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC ( pMIC = 2.77 ) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_ r 2 ) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_ r 2 ), validation ( q 2 ), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential ( R 2 = 0.9235 ) which was confirmed by the Y -randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.

vHTS, 3-D Pharmacophore, QSAR and Molecular docking Studies for the Identification of Phyto-derived ATP-Competitive Inhibitors of the BCR-ABL Kinase Domain

Background: Chronic myelogenous leukaemia (CML) constitutes about 15% of adult leukaemia and is characterized by the overproduction of immature myeloid cells. Methods: In this study, a virtual high throughput screening (vHTS) technique was employed to screen a library of phytochemicals of reported anti-cancer plants. A docking score of -10 kcalmol­1 was used as the cut-off for the selection of phyto-compounds for pharmacophore-based virtual screening. Statistically robust and thoroughly validated QSAR model (R = 0.914, R2 = 0.836, Adjusted R2 = 0.764, LOO-CV= 0.6680) was derived for the inhibition of BCR-ABL kinase domain. Results: The virtual screening, pharmacophore screening, QSAR model and molecular docking techniques applied herein revealed ellagic acid, a polyphenolic compound, as a potential competitive inhibitor of the BCR-ABL kinase domain. Ellagic acid binds to the inactive ABL state and forms similar interactions with key residues within the BCR-ABL Kinase domain as obtained in ponatinib (possesses inhibitory effects on the ABL thr-315I mutant). It forms hydrogen bond interaction with thr-315 residue (the gatekeeper residue). It is not likely to be prone to the various mutations associated with nilotinib because of its small size. Conclusion: The procedure of VHTs, Pharmacophore, QSAR, and molecular docking applied in this study could help in detecting more anti-CML compounds.