AIMS
To find for lead compounds, efficiently defeating diabetes mellitus, prompted us to the synthesis of a new library of adamantane‑appended 1,2,3-triazole hybrid conjugates (AT1-AT18).
MATERIAL AND METHODS
The current synthesis of adamantane-appended 1,2,3-triazoles was performed by using Cu(I)-catalyzed azide-alkyne cycloaddition reaction. The hybrids were structurally characterized via 1H, 13C NMR, FTIR, and mass spectrometry. The hybrids were evaluated for their inhibitory potential for α-glucosidase enzyme, along with in silico activity, i.e., molecular docking, molecular dynamics simulations, and ADME (absorption, distribution, metabolism, and excretion).
RESULTS
Hybrid AT5 (IC50 5.5 ± 0.87 µM) showed maximum inhibition potential against α-glucosidase enzyme, which may be risen from the methyl substitution on phenyl ring. The results were compared with the reference, acarbose (IC50 13.5 ± 0.32 µM). The docking studies using PDB:3L4U revealed that the hybrid AT5 demonstrated a docking score -7.656, which was better than alkyne, i.e., -3.894. Ligand stability analysis explored by using molecular dynamics demonstrated the useful findings such as RMSD <2 Å, 0-2 H-bonds, for hybrid AT5, highlighting the stabilization contribution of triazole unit.
CONCLUSION
The hybrid AT5 may be used as potential lead compound after necessary structural modifications.
Aman Ragshaniya, V. Asati, Jayant Sindhu et al.· Future Medicinal Chemistry· 0 citations
Drug discovery is a time-consuming and resource-intensive process with a development period of more than ten years and a clinical attrition rate of more than 90%. Despite its contributions to rational drug design, computer-aided drug design has been constrained by limited scalability, overreliance on molecular descriptors, and incomplete modeling of complex biological systems. The emergence of artificial intelligence (AI) has transformed this landscape. AI-based drug discovery platforms have shifted the paradigm from a narrow focus to a comprehensive platform that covers target identification using graph-based drug-target interaction models. Additionally, deep-learning-based docking techniques, such as GNINA and AtomNet, de novo design approaches, such as REINVENT and RANC, and multi-task ADMET predictors, such as ADMETlab 2.0, are all parts of the AI-based drug discovery platform. In addition, AlphaFold has recently predicted more than 200 million protein structures, substantially expanding the pool of accessible drug targets. This review focuses on the AI-based approaches for target discovery, virtual screening, molecular generation, lead optimization, retro-synthesis, and structural modeling while also addressing issues of dataset bias, reproducibility, and real-world applicability. In this review, we discuss the emerging trends of AI-based drug discovery computational tools, which might change the face of medicinal chemistry. This review provides a balanced overview of AI-based drug discovery, including the limitations and challenges, to provide a framework to move this emerging field of science to a more robust, reproducible and clinically applicable platform.
Ryena Dhir, Pitam Ghosh, D. Sharma et al.· RSC Advances· 0 citations