In silico identification of novel 5-HT2A antagonists supported with ligand- and target-based drug design methodologies

Pharmacophore modeling, molecular docking, and molecular dynamics studies to identify new 5-HT2AR antagonists with the potential for design of new atypical antipsychotics

Some important atypical antipsychotic drugs target the serotonergic receptor 2A (5-HT2AR). Currently, new therapeutic strategies are needed to offer faster onset of action with fewer side effects and, therefore, greater efficacy in a substantial proportion of patients with neuropsychological disorders such as Autism and Parkinson. The main objective of this work was to use SBDD methods to identify new hit compounds potentially useful as precursors of novel and selective 5-HT2AR antagonists. A structure-based pharmacophore screening study based on a selective antagonist was carried out in ten databases. The set obtained was refined using molecular docking, and the five most promising compounds were subjected to molecular dynamics simulations. The most stable and promising hit occupied a side pocket present in the 5-HT2AR, a site that can be explored to obtain selective ligands. Simulations against 5-HT2CR and D2R showed that the best hit could not form stable complexes with these targets, strengthening the hypothesis that the hit presents selective binding by the receptor of interest. The selected hits showed some predicted toxicity risk or violated some drug-likeness property. However, it can be concluded that the identified hits are the most promising for performing in vitro assays. Once the presence of activity is confirmed, they could become precursors of optimized and selective antagonists of 5-HT2AR. An SBDD study was carried out to identify new selective 5-HT2AR ligands potentially useful for designing selective atypical antipsychotics.

Molecular Docking Assessment of Cathinones as 5-HT2AR Ligands: Developing of Predictive Structure-Based Bioactive Conformations and Three-Dimensional Structure-Activity Relationships Models for Future Recognition of Abuse Drugs

Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure-activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Flavanones: A potential natural inhibitor of the ATP binding site of PknG of Mycobacterium tuberculosis

Over the years, Mycobacterium tuberculosis has been one of the major causes of death worldwide. As several clinical isolates of the bacteria have developed drug resistance against the target sites of the current therapeutic agents, the development of a novel drug is the pressing priority. According to recent studies on Mycobacterium tuberculosis, ATP binding sites of Mycobacterium tuberculosis serine/threonine protein kinases (MTPKs) have been identified as the new promising drug target. Among the several other protein kinases (PKs), Protein kinase G (PknG) was selected for the study because of its crucial role in modulating bacterium's metabolism to survive in host macrophages. In this work, we have focused on the H37Rv strain of Mycobacterium tuberculosis. A list of 477 flavanones obtained from the PubChem database was docked one by one against the crystallized and refined structure of PknG by in-silico techniques. Initially, potential inhibitors were narrowed down by preliminary docking. Flavanones were then selected using binding energies ranging from -7.9 kcal.mol^-1 to -10.8 kcal.mol^-1. This was followed by drug-likeness prediction, redocking analysis, and molecular dynamics simulations. Here, we have used experimentally confirmed drug AX20017 as a reference to determine candidate compounds that can act as potential inhibitors for PknG. PubChem165506, PubChem242065, PubChem688859, PubChem101367767, PubChem3534982, and PubChem42607933 were identified as possible target site inhibitors for PknG with a desirable negative binding energy of -8.1, -8.3, -8.4, -8.8, -8.6 and -7.9 kcal.mol^-1 respectively. Communicated by Ramaswamy H. Sarma.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification

Serotonergic psychedelics, substances exerting their effects primarily through the serotonin 2A receptor (5HT2AR), continue to comprise a substantial portion of reported new psychoactive substances (NPS). In this paper five quantitative structure-activity relationship (QSAR) models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models, exploiting the accessibility of the QSAR equations, generate a useful tool for the investigation and identification of unclassified molecules. The models have been built using a set of 375 molecules using Forge software, and the quality was confirmed by statistical analysis, resulting in effective tools with respect to their predictive and descriptive capabilities. The best performing algorithm among the machine learning approaches and the classical field 3D-QSAR model were then combined to produce a consensus model and were exploited, together with a pharmacophorefilter, to explore the 5-HT2AR activity of 523 105 natural products, to classify a set of recently reported 5-HT2AR NPS and to design new potential active molecules. The findings of this study should facilitate the identification and classification of emerging 5-HT2AR ligands including NPS.

Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

Molecular modeling studies of benzothiophene-containing derivatives as promising selective estrogen receptor downregulators: a combination of 3D-QSAR, molecular docking and molecular dynamics simulations

Selective estrogen receptor downregulators (SERDs) for the treatment of positive breast cancer can act both as estrogen alpha receptor (ERα) antagonists and degraders. In this study, the optimal antagonist models (CoMFA-A, q² = 0.660, r² = 0.996; CoMSIA-A, q² = 0.728, r² = 0.992) and degrader models (CoMFA-D, q² = 0.850, r² = 0.996; CoMSIA-D, q² = 0.719, r² = 0.995) of a series of potent benzothiophene-containing SERDs were constructed to explore the three-dimensional quantitative structure-activity relationship. Internal and external validation indicated that all models exhibited good applicability, high predictive ability and robustness. Contour maps revealed the relationships between the essential structural features and antagonistic and degradation activities. Additionally, molecular docking, molecular dynamics and free energy calculation studies were further performed to investigate the detailed binding mode. Results indicated that several key residues, ARG394, GLU353, PHE404 and ILE424, were crucial for the stability of the ligand binding domain. The hydrophobic, electrostatic and Van der Waals interactions played significant effect on the binding affinity. Finally, ten novel compounds were designed based on above findings, where the predicted activity of compound D8 was equivalent to that of the compound LSZ102. 3D-QSAR, ADMET and bioavailability predictions indicated that all designed compounds with good predicted activity, good physicochemical and bioavailability could be potential candidates for SERDs. These results and combinations of computational methods provided guidance for the rational drug design of novel potential SERDs.Communicated by Ramaswamy H. Sarma.