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Butyrylcholinesterase (BChE) is a hydrolase involved in the metabolism and detoxification of specific esters in the blood. It is also implicated in the progression of Alzheimer's disease, a type of dementia. As the disease progresses, the level of BChE tends to increase, opting for a major role as an acetylcholine-degrading enzyme and surpassing the role of acetylcholinesterase. Hence, the development of BChE inhibitors could be beneficial for the latter stages of the disease. In the present study, machine learning (ML) models were developed and employed to identify new BChE inhibitors. Further, the identified molecules were subjected to molecular property filters. The filtered ligands were studied through molecular modelling techniques, viz. molecular docking and molecular dynamics (MD). Support vector machine-based ML models resulted in the identification of 3291 compounds that would have predicted IC 50 values less than 200 nM. The docking study showed that compounds ART13069594, ART17350769 and LEG19710163 have mean binding energies of -9.62, -9.26 and -8.93 kcal/mol, respectively. The MD study displayed that all the selected ligands showed stable complexes with BChE. The trajectories of all the ligands were stable similar to the standard BChE inhibitors.Communicated by Ramaswamy H. Sarma.

This study integrates bioinformatics and computer-aided drug discovery to assess suillin's therapeutic potential, particularly its interaction with acetylcholinesterase (AChE). Alzheimer's disease presents profound challenges, necessitating effective treatments to mitigate cognitive decline and improve patients' quality of life. Although current medications offer symptomatic relief, they often entail adverse effects and do not address the underlying disease progression. Natural sources, such as macrofungi mushrooms, hold promise for novel drug discovery due to their bioactive compounds' diverse therapeutic properties. Suillin, derived from Suillus luteus mushrooms, shows promise as a mixed-type AChE inhibitor, crucial for maintaining acetylcholine levels in neurodegenerative disorders like Alzheimer's disease. Computational docking studies reveal suillin's distinctive interactions with AChE, suggesting potential modulation of enzyme function through various bonding mechanisms. The Molinspiration drug-likeness score further supports suillin's efficacy, indicating its suitability for enzyme inhibition. By combining computational and bioinformatics approaches, this study elucidates suillin's molecular interactions and underscores its potential as a therapeutic agent.