Calcium homeostasis modulator 1 (CALHM1) is a protein responsible for causing Alzheimer’s disease. In the absence of an experimentally designed protein molecule, homology modelling was performed. Through homology modelling, different CALHM1 models were generated and validated through Rampage. To carry out further in silico studies, through molecular docking and molecular dynamics simulation experiments, various flavonoids and alkaloids from Bauhinia variegata were utilised as inhibitors to target the protein (CALHM1). The sequence of CALHM1 was retrieved from UniProt and the secondary structure prediction of CALHM1 was done through CFSSP, GOR4, and SOPMA methods. The structure was identified through LOMETS, MUSTER, and MODELLER and finally, the structures were validated through Rampage. Bauhinia variegata plant was used to check the interaction of alkaloids and flavonoids against CALHM1. The protein and protein–ligand complex were also validated through molecular dynamics simulations studies. The model generated through MODELLER software with 6VAM A was used because this model predicted the best results in the Ramachandran plot. Further molecular docking was performed, quercetin was found to be the most appropriate candidate for the protein molecule with the minimum binding energy of −12.45 kcal/mol and their ADME properties were analysed through Molsoft and Molinspiration. Molecular dynamics simulations showed that CALHM1 and CALHM1–quercetin complex became stable at 2500 ps. It may be seen through the study that quercetin may act as a good inhibitor for treatment. With the help of an in silico study, it was easier to analyse the 3D structure of the protein, which may be scrutinized for the best-predicted model. Quercetin may work as a good inhibitor for treating Alzheimer’s disease, according to in silico research using molecular docking and molecular dynamics simulations, and future in vitro and in vivo analysis may confirm its effectiveness.
The combination of molecular docking with personalised medicine represents a paradigm shift in drug development, providing unmatched accuracy in customising therapeutic approaches for specific patients. This collaborative effort utilises cutting-edge computational methods, including molecular docking, in conjunction with genetic insights to optimise and anticipate drug-receptor interactions. Revolutionary achievements could be further amplified by integrating large-scale omics data, artificial intelligence, and structural biology discoveries. Molecular docking and personalised medicine are developing fields that could lead to treatments that take into account each patient’s unique molecular profile in addition to previously unheard-of levels of accuracy in disease diagnosis. This revolutionary landscape will be further enhanced by future developments in quantum computing, CRISPR-based gene editing, and biomarker discovery. These advances will enable the realisation of a healthcare paradigm in which interventions are not only precise but also proactive, thereby realising the full potential of customised therapeutic strategies for improved patient outcomes.