QSAR and pharmacophore modeling of anti-tubercular 6-Fluoroquinolone compounds utilizing calculated structural descriptors

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occurrence, persistence, bioaccumulation and toxicity based on the integrated assignment method. A classification model for ADs was constructed using an improved mixup-transformer deep learning method, and its classification accuracy was compared with those of other models. The accuracy of the proposed model improved by up to 23.25 % compared with the random forest model, and the reliability was 80 % more than that of the TOPSIS method. A priority screening candidate list was proposed to screen 33 high-priority ADs. Finally, SHapley Additive explanation (SHAP) visualization, molecular dynamics, and amino acid analysis were performed to analyze the correlation between AD structure and toxic receptor binding characteristics and reveal the differences in AD risk priority. ADs with more intramolecular hydrogen bonds, higher hydrophobicity, and electronegativity had a more significant risk. Van der Waals and electrostatic interactions were the primary influencing factors, and significant differences in the types and proportions of the main amino acids in the interaction between ADs and receptors were observed. The results of the study provide constructive schemes and insights for AD priority screening and risk management.

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

Background

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties.

Results

The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R2train = 0.69, R2adj = 0.63, R2pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol−1, 31 = − 31.85 kcal mol−1, and 33 = − 33.70 kcal mol−1), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol−1).

Conclusions

These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

Discovery of Novel Inhibitors of Bacterial DNA Gyrase Using a QSAR-Based Approach

Type II topoisomerases like DNA gyrase initiate ATP-dependent negative supercoils in bacterial DNA. It is critical in all of the bacteria but is missing from eukaryotes, making it a striking target for antibacterials. Ciprofloxacin is a clinically approved drug, but its clinical effectiveness is affected by the emergence of resistance in both Gram-positive and Gram-negative bacteria. Thus, it is vital to identify novel compounds that can efficiently inhibit DNA gyrase, and quantitative structure-activity relationship (QSAR) modeling is a quick and economical means to do so. A QSAR-based virtual screening approach was applied to identify new gyrase inhibitors using an in-house-generated combinatorial library of 29828 compounds from seven ciprofloxacin scaffold structures. QSAR was built using a data set of 271 compounds, which were identified as positive and negative inhibitors from existing data reported in in vitro studies. The best QSAR model was developed using the 5-fold cross-validation Neural Network in Orange, and it was based on five PaDEL descriptors with an accuracy and sensitivity of 83%. As a result of screening of an in-house-built combinatorial library with the best-developed QSAR model, 675 compounds were identified as potential inhibitors of DNA gyrase. These inhibitors were further docked with DNA gyrase using AutoDock to compare the binding mode and score of the selected/screened compounds, and 615 compounds exhibited a docking score comparable to or lower than that of ciprofloxacin. Out of these, the top five analogues 902b, 9699f, 4419f, 5538f, and 898b reported in our study have binding scores of -13.81, -12.95, -12.52, -12.43, and -12.41 kcal/mol, respectively. The MD simulations of these five analogues for 100 ns supported the interaction stability of analogues with Escherichia coli DNA gyrase. Ninety-one per cent of the analogues screened by the QSAR model displayed better binding energy than ciprofloxacin, demonstrating the efficacy of the generated model. The NN-QSAR model proposed in this manuscript can be downloaded from https://github.com/ritu225/NN-QSAR_model.git.

Quantitative structure-activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents

Quantitative structure–activity relationship (QSAR) and molecular docking approach were carried out to design novel anti-tuberculosis agents based on xanthone derivatives. QSAR designed new compounds were calculated by Austin Model 1 (AM1) methods and analysis of multi-linear regression (MLR). The result showed that the best model as follows: Log IC50 = 3.113 + 11.627 qC1 + 15.955 qC4 + 11.702 qC9, this result has appropriate some statistical parameters (PRESS = 2.11, r2 = 0.730, SEE = 0. 3545, R = 0.6827, FCal/FTab = 4.68), and being used to design a potential anti-tuberculosis drugs with substituted amide, sulfoxide, and carboxylate group xanthone scaffold by a number of their inhibitory concentration (IC50). The mechanism action of sulfonamide substituted on the xanthone scaffold as anti-tuberculosis was carried out using molecular docking. Docking inhibition studies were carried out on MTB C171Q receptor (4C6X.pdb) as KasA inhibitors using by the discovery studio. Based on the binding interaction showed, the sulfonamide substituted xanthone has potential being the anti-tuberculosis drugs by KasA inhibitor for target drug activity.

In silico study directed towards identification of the key structural features of GyrB inhibitors targeting MTB DNA gyrase: HQSAR, CoMSIA and molecular dynamics simulations

Mycobacterium tuberculosis DNA gyrase subunit B (GyrB) has been identified as a promising target for rational drug design against fluoroquinolone drug-resistant tuberculosis. In this study, we attempted to identify the key structural feature for highly potent GyrB inhibitors through 2D-QSAR using HQSAR, 3D-QSAR using CoMSIA and molecular dynamics (MD) simulations approaches on a series of thiazole urea core derivatives. The best HQSAR and CoMSIA models based on IC₅₀ and MIC displayed the structural basis required for good activity against both GyrB enzyme and mycobacterial cell. MD simulations and binding free energy analysis using MM-GBSA and waterswap calculations revealed that the urea core of inhibitors has the strongest interaction with Asp79 via hydrogen bond interactions. In addition, cation-pi interaction and hydrophobic interactions of the R₂ substituent with Arg82 and Arg141 help to enhance the binding affinity in the GyrB ATPase binding site. Thus, the present study provides crucial structural features and a structural concept for rational design of novel DNA gyrase inhibitors with improved biological activities against both enzyme and mycobacterial cell, and with good pharmacokinetic properties and drug safety profiles.