Double Variational Binding--(SMILES) Conformational Analysis by Docking Mechanisms for Anti-HIV Pyrimidine Ligands

Identification of Novel Diarylpyrimidines as Potent HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors by Exploring the Primer Grip Region

HIV-1 reverse transcriptase (RT) plays a crucial role in the viral replication cycle, and RT inhibitors can represent a promising pathway in treating AIDS. To explore the primer grip region of HIV-1 RT, using -CH2O- as a linker, substituted benzene or pyridine rings were introduced into the left wing of diarylpyrimidines (DAPYs). A total of 17 compounds with new structures were synthesized. It showed that all compounds exhibited anti-HIV-1 (wild-type) activity values ranging from 7.6−199.0 nM. Among them, TF2 (EC50 = 7.6 nM) showed the most potent activity, which was better than that of NVP (EC50 = 122.6 nM). Notably, compared with RPV (CC50 = 3.98 μM), TF2 (CC50 > 279,329.6 nM) showed low cytotoxicity. For HIV-1 mutant strains K103N and E138K, most compounds showed effective activities. Especially for K103N, TF2 (EC50 = 28.1 nM), TF12 (EC50 = 34.7 nM) and TF13 (EC50 = 28.0 nM) exhibited outstanding activity, being superior to that of NVP (EC50 = 7495.1 nM) and EFV (EC50 = 95.1 nM). Additionally, TF2 also showed the most potent activity against E138K (EC50 = 44.0 nM) and Y181C mutant strains (EC50 = 139.3 nM). In addition, all the compounds showed strong enzyme inhibition (IC50 = 0.036−0.483 μM), which demonstrated that their target was HIV-1 RT. Moreover, molecular dynamics simulation studies were implemented to predict the binding mode of TF2 in the binding pocket of wild-type and K103N HIV-1 RT.

Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies

A series of novel uracil derivatives such as bispyrimidine dione and tetrapyrimidine dione derivatives were designed based on the existing four-point pharmacophore model as effective HIV capsid protein inhibitors. The compounds were initially docked with an HIV capsid protein monomer to rationalize the ideas of design and to find the potential binding modes. The successful design and computational studies led to the synthesis of bispyrimidine dione and tetrapyrimidine dione derivatives from uracil and aromatic aldehydes in the presence of HCl using novel methodology. The in vitro evaluation in HIV p24 assay revealed five potential uracil derivatives with IC₅₀ values ranging from 191.5 μg ml⁻¹ to 62.5 μg ml⁻¹. The meta-chloro substituted uracil compound 9a showed promising activity with an IC₅₀ value of 62.5 μg ml⁻¹ which is well correlated with the computational studies. As expected, all the active compounds were noncytotoxic in BA/F3 and Mo7e cell lines highlighting the thoughtful design. The structure activity relationship indicates the position priority and lower log P values as the possible cause of inhibitory potential of the uracil compounds.

The paper describes the design, synthesis, computational and biological validation of a series of novel uracil derivatives as effective HIV capsid protein inhibitors.

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors.

Structure-Based Design and in Silico Screening of Virtual Combinatorial Library of Benzamides Inhibiting 2-trans Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Predicted Pharmacokinetic Profiles

Background: During the previous decade a new class of benzamide-based inhibitors of 2-trans enoyl-acyl carrier protein reductase (InhA) of Mycobacterium tuberculosis (Mt) with unusual binding mode have emerged. Here we report in silico design and evaluation of novel benzamide InhA-Mt inhibitors with favorable predicted pharmacokinetic profiles. Methods: By using in situ modifications of the crystal structure of N-benzyl-4-((heteroaryl)methyl) benzamide (BHMB)-InhA complex (PDB entry 4QXM), 3D models of InhA-BHMBx complexes were prepared for a training set of 19 BHMBs with experimentally determined inhibitory potencies (half-maximal inhibitory concentrations IC50exp). In the search for active conformation of the BHMB1-19, linear QSAR model was prepared, which correlated computed gas phase enthalpies of formation (∆∆HMM) of InhA-BHMBx complexes with the IC50exp. Further, taking into account the solvent effect and entropy changes upon ligand, binding resulted in a superior QSAR model correlating computed complexation Gibbs free energies (∆∆Gcom). The successive pharmacophore model (PH4) generated from the active conformations of BHMBs served as a virtual screening tool of novel analogs included in a virtual combinatorial library (VCL) of compounds containing benzamide scaffolds. The VCL filtered by Lipinski’s rule-of-five was screened by the PH4 model to identify new BHMB analogs. Results: Gas phase QSAR model: −log₁₀(IC₅₀^exp) = pIC₅₀^exp = −0.2465 × ∆∆H_MM + 7.95503, R² = 0.94; superior aqueous phase QSAR model: pIC₅₀^exp = −0.2370 × ∆∆G_com + 7.8783, R² = 0.97 and PH4 pharmacophore model: pIC50exp = 1.0013 × pIC50exp − 0.0085, R² = 0.95. The VCL of more than 114 thousand BHMBs was filtered down to 73,565 analogs Lipinski’s rule. The five-point PH4 screening retained 90 new and potent BHMBs with predicted inhibitory potencies IC₅₀^pre up to 65 times lower than that of BHMB1 (IC₅₀^exp = 20 nM). Predicted pharmacokinetic profile of the new analogs showed enhanced cell membrane permeability and high human oral absorption compared to current anti-tuberculotics. Conclusions: Combined use of QSAR models that considered binding of the BHMBs to InhA, pharmacophore model, and ADME properties helped to recognize bound active conformation of the benzamide inhibitors, permitted in silico screening of VCL of compounds sharing benzamide scaffold and identification of new analogs with predicted high inhibitory potencies and favorable pharmacokinetic profiles.

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R²(train) = 0.935, R²(test) = 0.902, Q²(LOO) = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC(-)), oprea's lead-like (opr_leadlike), subdivided van der Waal's surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R²(train) = 0.944, R²(test) = 0.892, Q²(LOO) = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC50 values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity.