Reliable structural information for rational design of benzoxazole type potential cholesteryl ester transfer protein (CETP) inhibitors through multiple validated modeling techniques

Investigation into the Quantitative Structure-Biotoxicity Relationship of Antibiotics and their Estrogenic Receptor Disruption Effects

In light of antibiotics being classified as environmental hormone-like compounds, their interference with the endocrine system has significantly impacted human health and ecological environments. This study employed Density Functional Theory (DFT) within Gaussian09, conducting structural optimizations and property calculations on 23 typical antibiotic molecules at the B3LYP/3-21G and B3LYP/6-31G(d) levels to obtain structural parameters and acquired physicochemical property parameters through the RDKit database in ChemDes platform for quantitative processing of the compounds. Multiple linear regression analysis identified the primary factors affecting antibiotics' biological toxicity (pLD50), and a QSAR model was established. The model's predictive capability was analyzed using leave-one-out cross-validation, and the binding modes and mechanisms of action between estrogen receptors (ER) and antibiotics were investigated via molecular docking and molecular dynamics simulations. The results indicate that six property parameters significantly influence the biological toxicity of antibiotics, with the established QSAR model C exhibiting regression coefficients R2 and Q2 of 0.92474 and 0.74913, respectively, demonstrating good stability and predictive power. Molecular surface electrostatic potential, frontier molecular orbitals, molecular docking, and molecular dynamics simulations revealed that stable hydrogen bonds and hydrophobic interactions primarily mediate the potential estrogenic disrupting effects between antibiotics and estrogen receptors. Predictions from an anticancer compound library identified ten compounds with strong estrogenic disrupting effects, and molecular docking validated the practical utility of model C. This provides a valuable exploration for discovering and screening PPCPs with potential estrogenic disrupting effects.

In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

The COVID-19 pandemic emerged in 2019, bringing with it the need for greater stores of effective antiviral drugs. This paper deals with the conformation-independent, QSAR model, developed by employing the Monte Carlo optimization method, as well as molecular graphs and the SMILES notation-based descriptors for the purpose of modeling the SARS-CoV-3CLpro enzyme inhibition. The main purpose was developing a reproducible model involving easy interpretation, utilized for a quick prediction of the inhibitory activity of SAR-CoV-3CLpro. The following statistical parameters were present in the best-developed QSAR model: (training set) R 2 = 0.9314, Q 2 = 0.9271; (test set) R 2 = 0.9243, Q 2 = 0.8986. Molecular fragments, defined as SMILES notation descriptors, that have a positive and negative impact on 3CLpro inhibition were identified on the basis of the results obtained for structural indicators, and were applied to the computer-aided design of five new compounds with (4-methoxyphenyl)[2-(methylsulfanyl)-6,7-dihydro-1H-[1,4]dioxino[2,3-f]benzimidazol-1-yl]methanone as a template molecule. Molecular docking studies were used to examine the potential inhibition effect of designed molecules on SARS-CoV-3CLpro enzyme inhibition and obtained results have high correlation with the QSAR modeling results. In addition, the interactions between the designed molecules and amino acids from the 3CLpro active site were determined, and the energies they yield were calculated.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02170-8.

In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7

The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results.

Development of potential therapeutics for pain treatment by inducing Sigma 1 receptor antagonism – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for pain treatment.

Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for glaucoma treatment.

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

Phospho-mTOR expression in human glioblastoma microglia-macrophage cells

The glioblastoma (GBM) immune microenvironment is highly heterogeneous, and microglia may represent 30-70% of the entire tumor. However, the role of microglia and other specific immune populations is poorly characterized. Activation of mTOR signaling occurs in numerous human cancers and has roles in microglia-glioma cell interactions. We now show in human tumor specimens (42 patients), that 39% of tumor-associated microglial (TAM) cells express mTOR phosphorylated at Ser-2448; and similar mTOR activation is observed using a human microglia-glioma interaction paradigm. In addition, we confirm previous studies that microglia express urea and ARG1 (taken as M2 marker) in the presence of glioma cells, and this phenotype is down-regulated in the presence of a mTOR inhibitor. These results suggest that mTOR suppression in GBM patients might induce a reduction of the M2 phenotype expression in up to 40% of all TAMs. Since the M2 profile of microglial activation is believed to be associated with tumor progression, reductions in that phenotype may represent an additional anti-tumor mechanism of action of mTOR inhibitors, along with direct anti-proliferative activities.

Development of novel therapeutics for glaucoma filtration surgery based on transforming growth factor-β receptor 1 inhibition

QSAR modeling with computer-aided drug design was used for the in silico development of novel therapeutics for glaucoma filtration surgery.