Pharmacophore-guided Virtual Screening to Identify New β3-Adrenergic Receptor Agonists

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

A fruitful century for the scalable synthesis and reactions of biphenyl derivatives: applications and biological aspects

This review provides recent developments in the current status and latest synthetic methodologies of biphenyl derivatives. Furthermore, this review investigates detailed discussions of several metalated chemical reactions related to biphenyl scaffolds such as Wurtz-Fittig, Ullmann, Bennett-Turner, Negishi, Kumada, Stille, Suzuki-Miyaura, Friedel-Crafts, cyanation, amination, and various electrophilic substitution reactions supported by their mechanistic pathways. Furthermore, the preconditions required for the existence of axial chirality in biaryl compounds are discussed. Furthermore, atropisomerism as a type of axial chirality in biphenyl molecules is discussed. Additionally, this review covers a wide range of biological and medicinal applications of the synthesized compounds involving patented approaches in the last decade corresponding to investigating the crucial role of the biphenyl structures in APIs.

Discovery of the new alpha-glucosidase inhibitor with therapeutic potential in type 2 diabetes mellitus by a novel high-throughput virtual screening and free energy evaluation

Type 2 diabetes can cause a variety of complications, significantly affecting people's health. Given their ability to suppress carbohydrate digestion, alpha-glucosidase inhibitors are effective treatments for diabetes. However, the current approved glucosidase inhibitors' side effects of abdominal discomfort limit their use. We used the compound Pg3R from the natural fruit berry as a reference, screening against a large database of 22 million compounds to identify potential health-friendly alpha-glucosidase inhibitors. Ligand-based screening enables us to identify 3968 ligands that exhibit structural similarity compared to the natural compound. These lead hits were used for LeDock, and their binding free energies were evaluated by MM/GBSA. Among the top-scoring candidates, ZINC263584304 exhibited the strongest binding affinity to alpha-glucosidase, with a "low-fat" structural characteristic. Its recognition mechanism was further investigated by microsecond MD simulations and free energy landscapes, exhibiting novel conformational changes during the binding process. Our study provided a novel alpha-glucosidase inhibitor with the potential to treat type 2 diabetes.

Insights into β3-adrenoceptor agonism through comprehensive in silico investigation

Research onβ₃-AR, the new member of the adrenoceptor family, is in its infancy and few β₃-AR agonists have been approved for marketing to date. Meanwhile, β₃-AR exhibited obvious species differences in pharmacological properties, such as between human and animals, however, the 3D structure of human β₃-AR has not been published, which makes it difficult to understand the interaction between human β₃-AR and its agonists. Herein, binding patterns of β₃-AR agonists are explored starting from the Alphafold predicted structural model, and the obtained model was optimized by using molecular dynamics simulations. Moreover, the human β₃-AR and its agonists were subjected to molecular docking, dynamics simulations, binding free energy calculations and pharmacophore modeling to elucidate the characteristics of human β₃-AR activity pockets and agonist conformational relationships, including a hydrophobic group, a positively charged group as well as two hydrogen-bonded donors, which provide comprehensive insights into the interactions between human β₃-AR and its agonists.

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.