QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity

Investigating the bispecific lead compounds against methicillin-resistant Staphylococcus aureus SarA and CrtM using machine learning and molecular dynamics approach

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucial role in biofilm formation and staphyloxanthin biosynthesis. Thus, the present study used a machine learning-based QSAR model to screen 1261 plant-derived natural organic compounds in order to identify a medication candidate with both biofilm and virulence inhibitory potential. Additionally, the in-silico molecular docking analysis has demonstrated significant binding efficacy of the identified hit compound, that is 85137543, with SarA and CrtM when compared to the control compound, hesperidin. Post-MD simulation analysis of the complexes depicted strong binding of 85137543 to both SarA and CrtM. Moreover, 85137543 showed hydrogen bonding with the key residues of both proteins during docking (ALA138 of SarA and ALA134 of CrtM) and post-MD simulation (LYS273 of CrtM and ASN212 of SarA). The RMSD of 85137543 was stable and consistent when bound to both CrtM and SarA with RMSDs of 1.3 and 1 nm, respectively. In addition, principal component analysis and the free energy landscape showed stable complex formation with both proteins. Low binding free energy (ΔGTotal) was observed by 85137543 for SarA (-47.92 kcal/mol) and CrtM (-36.43 kcal/mol), which showed strong binding. Overall, this study identified 85137543 as a potential inhibitor of both SarA and CrtM in MRSA.Communicated by Ramaswamy H. Sarma.

3D QSAR study on substituted 1, 2, 4 triazole derivatives as anticancer agents by kNN MFA approach

Background and objectives

Researchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinal value. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method.

Methods

Using the molecular design suite, a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation.

Results

The most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047--0.0780--0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities.

Conclusion

The good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

Virtual Screening and Quantitative Structure-Activity Relationship of Moringa oleifera with Melanoma Antigen A (MAGE-A) Genes against the Therapeutics of Non-Small Cell Lung Cancers (NSCLCs)

In the last decade, there have been significant advancements in the treatment of non-small cell lung cancer, including remarkable gains in detection, diagnosis, and therapy. The emergence of molecular targeted therapies, immunotherapeutic inhibitors, and antiangiogenesis medicines has largely fueled improvements in combination therapy and systemic treatments, all of which have dramatically ameliorated patient outcomes. The Moringa oleifera bioactive compounds have been effective in the suppression of cancers, making them the therapeutic agents of choice for the current investigation to treat MAGE-A presented in NSCLC. The ligand entrants were screened for their pharmacological properties, and 2,2-diphenyl-1,3-benzodioxole was stipulated as the lead candidate. 2,2-Diphenyl-1,3-benzodioxole exhibited better pharmacological properties and superior binding with branched-chain amino acids, making it an ideal candidate to address MAGE-A. The study concluded that addressing MAGE-A to impede their activity and antigenicity can be exploited as immunotarget(s).

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved _PEC₅₀ value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Computational investigation of TGF-β receptor inhibitors for treatment of idiopathic pulmonary fibrosis: Field-based QSAR model and molecular dynamics simulation

The discovery of drugs relevant to transforming growth factor β (TGF-β) receptor inhibitors have been considered as a considerable challenge during therapy idiopathic pulmonary fibrosis diseases. For the first time, herein we illustrate a field-based quantitative structure-activity relationship (QSAR) model and molecular dynamics (MD) simulations for novel 7-substituted-pyrazolo [4, 3-b] pyridine derivatives with biological activity for the TGF-β receptor, with an attempt of elucidating the 3D structural features that are essential for the activity. Results demonstrate that the field-based model (Q² = 0.548, R²_training = 0.840, R²_test = 0.750) are acceptable with good predictive capabilities. In addition, MD studies were also carried out on the training set with the aim of exploring their binding modes in the active pocket of TGF-β receptor, resulting in some of the crucial structural fragments which are responsible for inhibitory activity. Therefore, we summarized the following features required for TGF-β receptor inhibition: electronegative in region1, bulky groups in region2 and smaller groups in region3. Based on the model and related information, we hope the above information provides an important insight for understanding the interactions of the inhibitors and TGF-β receptor, which may be useful in discovering novel potent inhibitors.

Trivedi E, Török B, Dembinski R. Regioselective “hydroamination” of alk-3-ynones with non-symmetrical o-phenylenediamines. Synthesis of diversely substituted 3H-1,5-benzodiazepines via (Z)-3-amino-2-alkenones

Reaction of alk-3-yn-1-ones with non-symmetricalo-phenylenediamines provides an effective method with high atom economy for the synthesis of diversely substituted 1,5-benzodiazepines and conjugated enaminones.