Quantitative Structure-activity Relationship Pharmacophore Research Articles

Computer-Aided Virtual Design of 3-Dehydroquinic Acid Analogues Inhibitors of Dehydroquinase Type II of Mycobacterium tuberculosis with Favorable Pharmacokinetics Profile

New potent 3-dehydroquinic acid (AQs) inhibitors of 3-dehydroquinate dehydratase (dehydroquinase type II) of Mycobacterium tuberculosis (MtDHQ2) were obtained by using structure-based molecular design via the in situ modification of the template inhibitor AQ1 within the MtDHQ2-AQ1 crystal structure (PDB ID: 2XB8), in order to describe the interactions upon the formation of the complex MtDHQ2-inhibitor. A training set of 14 AQs with known inhibition constants (Kiexp) was used to establish a quantitative structure-activity relationship (QSAR) model for correlating the pKiexp (pKiexp = ‑log10 (Kiexp)) to the computed Gibbs free energies of formation (ΔΔGcom) of MtDHQ2-AQs complexes (pKiexp= -0.278 ΔΔGcom + 7.653 , R² = 0.98) is derived. This accounts for the solvent effect and the loss of inhibitor entropy upon enzyme binding. Validation of this QSAR model was performed with 3D- QSAR pharmacophore generation (PH4). The structural information derived from the 3D model and breakdown of computed MtDHQ2-AQs interaction energies up to individual active site residue contribution led to the design of a virtual combinatorial library of 1600 AQs that was screened through the PH4 filter to retain about forty molecules. The absorption, distribution, metabolism and excretion parameters of these designed molecules were computed with the QikProp program while the inhibition constants were predicted by the generated QSAR model. Our computational approach, which combines molecular modeling, pharmacophore generation and analysis of MtDHQ2-AQ interaction energies, resulted in the proposed novel predicted potent antitubercular agents with favorable pharmacokinetic profile, of which the best candidate predicted inhibition constant (Kipre) value within the picomolar range.

Theoretical support for the enhancement of infrared spectrum signals by derivatization of phthalic acid esters using a pharmacophore model

ABSTRACTThree-dimensional quantitative structure–activity relationship pharmacophore models of phthalic acid ester (PAE) infrared characteristic vibration spectrum signals were constructed. Then, three PAEs were substituted with hydrophobic groups using the optimal pharmacophore model. The results showed that the pharmacophore model (Hypo 1) was significant and that 16 PAE derivatives were generated. The infrared characteristic vibration spectrum signals of three derivatives were increased by 76.98, 26.49, and 16.42%. The substitution reaction orders were determined, and the enhancement mechanisms of PAEs were analyzed. The findings provide theoretical support for the development of technology to enhance infrared spectrum signals.

Identification of potential type 4 cAMP phosphodiesterase inhibitors via 3D pharmacophore modeling, virtual screening, DFT and structural bioisostere design

Cyclic nucleotide phosphodiesterase Type 4 specifically metabolizes Cyclic Adenosine Monophosphate and has widespread distribution across the human body. The aim of this study was to generate a well-validated ligand-based 3D Quantitative Structure Activity Relationship pharmacophore model to identify potential phosphodiesterase Type 4 inhibitors using a set of 18 known chemically diverse phosphodiesterase Type 4 inhibitors. The HypoGen module of Discovery Studio v4.1 was used to generate the aforementioned pharmacophore model which was then employed as 3D query for virtual screening of four chemical and two natural product databases. The top hits were evaluated for their drug-like properties. The binding orientations of the best fits were predicted by molecular docking. Orbital energies were predicted for top hits and the density functional theory based minimum energy gap (Highest Occupied Molecular Orbital–Lowest Unoccupied Molecular Orbital gap) was used to further cull the selection and identify the most potential phosphodiesterase Type 4 inhibitors. Chemical similarity search was performed and structural analogs of the best hits were designed to discover novel potential phosphodiesterase Type 4 inhibitors. Use of Hypo1 as 3D query for virtual screening yielded 1243 compounds and subsequent molecular docking studies narrowed it down to 19 potential phosphodiesterase Type 4 inhibitors while a density functional theory-based study further culled this selection down to six most potential inhibitors. Six structurally diverse chemical structures with novel scaffolds and six analogs of the best hits were identified using pharmacophore modeling to be potential phosphodiesterase Type 4 inhibitors.

Prediction of stability for polychlorinated biphenyls in transformer insulation oil through three-dimensional quantitative structure-activity relationship pharmacophore model and full factor experimental design

Based on the obtained data of half-lives(t1/2) for 31 polychlorinated biphenyl congeners(PCBs), 3D quantitative structure-activity relationship(QSAR) pharmacophore was used to establish a 3D QSAR model to predict the t1/2 values of the remaining 178 PCBs, using the structural parameters as independent variables and lgt1/2 values as the dependent variable. Among this process, the whole data set(31 compounds) was divided into a training set(24 compounds) for model generation and a test set(7 compounds) for model validation. Then, the full factor experimental design was used to research the potential second-order interactional effect between different substituent positions, obtaining the final regulation scheme for PCB. At last, a 3D QSAR pharmacophore model was established to validate the reasonable regulation targeting typical PCB with respect to half-lives and thermostability. As a result, the cross-validation correlation coefficient(q2) obtained by the 3D QSAR model was 0.845(>0.5) and the coefficient of determination(r2) obtained was 0.936(>0.9), indicating that the models were robust and predictive. CoMSIA analyses upon steric, electrostatic and hydrophobic fields were 0.7%, 85.9%, and 13.4%, respectively. The electrostatic field was determined to be a primary factor governing the t1/2. From CoMSIA contour maps, t1/2 increased when substituents possessed electropositive groups at the 2′-, 3-, 3′-, 5- and 5′- positions and electronegative groups at the 3-, 3′-, 5-, 6- and 6′- positions, which could increase the PCB stability in transformer insulation oil. Modification of two typical PCB congeners(PCB-77 and PCB-81) showed that the lgt1/2for three selected modified compounds increased by 13%(average ratio) compared with that of each congener and the thermostability of them were higher, validating the reasonability of the regulatory scheme obtained from the 3D QSAR model. These results are expected to be beneficial in predicting t1/2 values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the stability of PCBs.

An in silico protocol for identifying mTOR inhibitors from natural products

The mammalian target of rapamycin (mTOR) is an anti-cancer target. In this study, we propose an in silico protocol for identifying mTOR inhibitors from the ZINC natural product database. First, a three-dimensional quantitative structure-activity relationship pharmacophore model was built based on known mTOR inhibitors. The model was validated with an external test set, Fischer's randomization method, a decoy set and pharmacophore mapping conformation testing. The results showed that the model can predict the mTOR inhibition activity of the tested compounds. Virtual screening was performed based on the best pharmacophore model, and the results were then filtered using a molecular docking approach. In addition, molecular mechanics/generalized born surface area analysis was used to refine the selected candidates. The top 20 natural products were selected as potential mTOR inhibitors, and their structural scaffolds could serve as building blocks in designing drug-like molecules for mTOR inhibition.

Investigation into Potent Inflammation Inhibitors from Traditional Chinese Medicine

Microsomal prostaglandin E synthase-1 (mPGES-1) is the key enzyme for prostaglandin E2 (PGE2) generation during inflammation and is a potential target for designing anti-inflammatory drugs. Potential inhibitors of m-PGES-1 were selected from traditional Chinese medicine (TCM Database@Taiwan) based on the pharmacophore map generated by the top HypoGen hypothesis and validated using structure- and ligand-based analysis. Key features for potential m-PGES-1 inhibitors include pi-interactions and H-bond donors. TCM compounds, shanciol B, shanciol A, castilliferol, and aurantiamide acetate, contoured to the quantitative structure-activity relationship pharmacophore and exhibited high docking scores and binding stability with m-PGES-1. Bioactivity models multiple linear regression (MLR) and support vector machine also supported activity predictions for the candidate compounds. Our results indicate that the investigated TCM compounds could be of use for development into mPGES-1 inhibitors.