In silico structure-based design of GABAB receptor agonists using a combination of docking and QSAR

Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study

M1 muscarinic acetylcholine receptor (M1-AChR), a member of the G protein-coupled receptors (GPCR) family, plays a crucial role in learning and memory, making it an important drug target for Alzheimer's disease (AD) and schizophrenia. M1-AChR activation and deactivation have shown modifying effects in AD and PD preclinical models, respectively. However, understanding the pharmacology associated with M1-AChR activation or deactivation is complex, because of the low selectivity among muscarinic subtypes, hampering their therapeutic applications. In this regard, we constructed two quantitative structure-activity relationship (QSAR) models, one for M1-AChR agonists (total and partial), and the other for the antagonists. The binding mode of 59 structurally different compounds, including agonists and antagonists with experimental binding affinity values (pKi), were analyzed employing computational molecular docking over different structures of M1-AChR. Furthermore, we considered the interaction energy (Einter), the number of rotatable bonds (NRB), and lipophilicity (ilogP) for the construction of the QSAR model for agonists (R2 = 89.64, QLMO2 = 78, and Qext2 = 79.1). For the QSAR model of antagonists (R2 = 88.44, QLMO2 = 82, and Qext2 = 78.1) we considered the Einter, the fraction of sp3 carbons fCsp3, and lipophilicity (MlogP). Our results suggest that the ligand volume is a determinant to establish its biological activity (agonist or antagonist), causing changes in binding energy, and determining the affinity for M1-AChR.

In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners’ safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics’ data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.

Molecules against Covid-19: An in silico approach for drug development

A large number of deaths have been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide, turning it into a serious and momentous threat to public health. This study tends to contribute to the development of effective treatment strategies through a computational approach, investigating the mechanisms in relation to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). Molecular docking was performed to screen six naturally occurring molecules with antineoplastic properties (Ellipticine, Ecteinascidin, Homoharringtonine, Dolastatin 10, Halichondrin, and Plicamycin). Absorption, distribution, metabolism, and excretion (ADME) investigation was also conducted to analyze the drug-like properties of these compounds. The docked results have clearly shown binding of ligands to the SARS-CoV-2 RdRp protein. Interestingly, all ligands were found to obey Lipinski’s rule of five. These results provide a basis for repurposing and using molecules, derived from plants and animals, as a potential treatment for the coronavirus disease 2019 (COVID-19) infection as they could be effective therapeutics for the same.

Pharmacophore Modeling and 3D-QSAR Study of Indole and Isatin Derivatives as Antiamyloidogenic Agents Targeting Alzheimer's Disease

Thirty-six novel indole-containing compounds, mainly 3-(2-phenylhydrazono) isatins and structurally related 1H-indole-3-carbaldehyde derivatives, were synthesized and assayed as inhibitors of beta amyloid (Aβ) aggregation, a hallmark of pathophysiology of Alzheimer’s disease. The newly synthesized molecules spanned their IC₅₀ values from sub- to two-digit micromolar range, bearing further information into structure-activity relationships. Some of the new compounds showed interesting multitarget activity, by inhibiting monoamine oxidases A and B. A cell-based assay in tau overexpressing bacterial cells disclosed a promising additional activity of some derivatives against tau aggregation. The accumulated data of either about ninety published and thirty-six newly synthesized molecules were used to generate a pharmacophore hypothesis of antiamyloidogenic activity exerted in a wide range of potencies, satisfactorily discriminating the ‘active’ compounds from the ‘inactive’ (poorly active) ones. An atom-based 3D-QSAR model was also derived for about 80% of ‘active’ compounds, i.e., those achieving finite IC₅₀ values lower than 100 μM. The 3D-QSAR model (encompassing 4 PLS factors), featuring acceptable predictive statistics either in the training set (n = 45, q² = 0.596) and in the external test set (n = 14, r²_ext = 0.695), usefully complemented the pharmacophore model by identifying the physicochemical features mainly correlated with the Aβ anti-aggregating potency of the indole and isatin derivatives studied herein.

The GABAB Receptor-Structure, Ligand Binding and Drug Development

The γ-aminobutyric acid (GABA) type B receptor (GABA_B-R) belongs to class C of the G-protein coupled receptors (GPCRs). Together with the GABA_A receptor, the receptor mediates the neurotransmission of GABA, the main inhibitory neurotransmitter in the central nervous system (CNS). In recent decades, the receptor has been extensively studied with the intention being to understand pathophysiological roles, structural mechanisms and develop drugs. The dysfunction of the receptor is linked to a broad variety of disorders, including anxiety, depression, alcohol addiction, memory and cancer. Despite extensive efforts, few compounds are known to target the receptor, and only the agonist baclofen is approved for clinical use. The receptor is a mandatory heterodimer of the GABA_B1 and GABA_B2 subunits, and each subunit is composed of an extracellular Venus Flytrap domain (VFT) and a transmembrane domain of seven α-helices (7TM domain). In this review, we briefly present the existing knowledge about the receptor structure, activation and compounds targeting the receptor, emphasizing the role of the receptor in previous and future drug design and discovery efforts.

Rational Drug Design of Axl Tyrosine Kinase Type I Inhibitors as Promising Candidates Against Cancer

The high level of Axl tyrosine kinase expression in various cancer cell lines makes it an attractive target for the development of anti-cancer drugs. In this study, we carried out several sets of in silico screening for the ATP-competitive Axl kinase inhibitors based on different molecular docking protocols. The best drug-like candidates were identified, after parental structure modifications, by their highest affinity to the target protein. We found that our newly designed compound R5, a derivative of the R428 patented analog, is the most promising inhibitor of the Axl kinase according to the three molecular docking algorithms applied in the study. The molecular docking results are in agreement with the molecular dynamics simulations using the MM-PBSA/GBSA implicit solvation models, which confirm the high affinity of R5 toward the protein receptor. Additionally, the selectivity test against other kinases also reveals a high affinity of R5 toward ABL1 and Tyro3 kinases, emphasizing its promising potential for the treatment of malignant tumors.