In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.

Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors

The idea of using ranked binding energies of residues and data fusion are presented here for the first time as a valuable tool to classify active and selective inhibitors. Selective inhibitors of JAK3 can inhibit inflammatory cytokine while preventing targeting other subtypes of JAK1 and JAK2. Herein, we report a novel way to identify both active JAK3 and selective JAK1/JAK3 and JAK2/JAK3 inhibitors using the effective activity and selectivity classifications. The most important residues (top 10) responsible for the inhibition mechanism are sorted from high to low energies, which are considered as variables in the classification process. In addition, the ranked energies of ligands' heteroatoms (top 5), ranked energies of hydrogen bonds (top 5) and important molecular descriptors (top 10) were used to construct different data fusion possibilities. It is shown that the proposed data fusion strategy can increase the accuracy of the activity classification to 100% and the selectivity classification to 96.4%. The proposed strategies represented in this paper can help medicinal or pharmaceutical chemist in evaluation of both active and selective inhibitors before synthesizing new pharmaceuticals.

Molecular docking-based classification and systematic QSAR analysis of indoles as Pim kinase inhibitors

Pim kinase enzyme has an essential role in the treatment of prostate, colon and acute myeloid leukaemia cancers. The indoles inhibitors were docked in the enzyme's active pocket in order to survey the inhibition mechanism and extract the ligands' conformations. The docking outcome shows that the active inhibitors have strong van der Waals interactions with residues of Ile185, Leu44, Leu120 and Leu174, hydrogen bonds with residues of Asp128, Arg122 and Glu171 and π-π interaction with the residue of Phe49. The sum of these interactions is ~80 kcal mol^-1 contributing ~90% of total binding free energies. Using docking-based molecular descriptors, the unsupervised and supervised classifications were successfully carried out with the accuracy of 0.82 and 0.95, respectively, to categorize the active/inactive Pim kinase inhibitors. The vigorous quantitative assessment was performed using different machine learning techniques. The constructed QSAR model [(r ² _cal, r ² _p, r ² _m and Q ² _LOO) > 0.80 and (SE _cal, SE_p and SE _LOO) < 0.22] indicates that the molecular descriptors of nN, RDF20v and E1v can describe both the inhibition activities and the inhibition mechanism. The adequate evaluations of the molecular docking, classifications and QSAR analysis show that the current approaches can be used as valuable tools to design more effective new Pim kinase inhibitors for cancer treatment.

The critical factors affecting typical organophosphate flame retardants to mimetic biomembrane: An integrated in vitro and in silico study

Organophosphate flame retardants (OPFRs) have been reported to induce cytotoxicity in a structure-dependent manner. The toxic effects may be due to the damage of biomembrane integrity and/or the interference of membrane signal pathway. In this study, the damages of fifteen typical OPFRs (chlorinated phosphates, alkyl phosphates, aryl phosphates, and alkoxy phosphates) to mimetic biomembrane were determined by the electrochemical impedance spectroscopy (EIS). The molecular structure descriptors that characterized the action mechanisms were screened by stepwise regression. The six molecular descriptors (MATS7e, DLS_05, Mor19m, Mor22v, Mor12v and MATS8m) were screened to study the actions between OPFRs and mimetic biomembrane. A quantitative structure-activity relationship (QSAR) model was developed by the partial least squares (PLS) method. Statistical results indicated that the QSAR model had good robustness and mechanism interpretability. The distribution of atomic electronegativities (MATS7e) and atomic masses in three dimensional spaces (Mor19m) were the key factors influencing the actions between OPFRs and simulated biofilms. The compounds with strong electron-withdrawing property could invade the inner layer of membrane and destroy its integrity. High levels of steric hindrance could impair the damage capacity caused by electronegativity. Moreover, drug-like index (DLS_05), spatial structures of particle (Mor22v, Mor12v) and atomic masses (MATS8m) also affected the actions. The results revealed the mechanism of the actions of OPFRs with simulated biofilms and elucidated the key structural characteristics affecting the actions of OPFRs, which could provide theoretical basis for ecological risk assessment of OPFRs.

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.