A computational-based search of natural product derived multi-target ligands for the management of Alzheimer’s and Parkinson’s disease using structure-based pharmacophore modelling, virtual screening, MD docking, free energy analysis, ADMET profiling and DFT studies

Neurodegenerative disorders, including Alzheimer's disease (AD) and Parkinson's disease (PD), represent major global health concerns due to multifactorial pathology and lack of effective therapeutics. A comprehensive multi-target (MT) computational strategy was applied to identify natural product-derived therapeutics against four critical targets of AD and PD: acetylcholinesterase (AChE), dopamine receptor D2, monoamine oxidase B (MAO-B) and cyclooxygenase-2 (COX-2). Structure-based pharmacophore models were constructed for each target, validated and employed to virtually screen 2,54,850 compounds from the COCONUT database, following rigorous ADME-based filtering. Compounds with an average pharmacophore fit score ≥0.6 were shortlisted, yielding 55 promising candidates. These were examined through molecular dynamics (MD) docking using the CDOCKER algorithm. Four hits (N1-N4) displayed superior binding affinities relative to reference drugs donepezil (DNP) and safinamide (SAF). Binding free energy calculations further substantiated their interaction stability. ADMET analysis predicted favourable pharmacokinetic properties, efficient blood-brain barrier penetration and non-toxic profiles, with compound N1 (CNP0388746.0) emerging as the most promising candidate. Additionally, density functional theory (DFT) studies provided insights into the electronic characteristics and reactivity of top hits. Overall, this integrated in silico approach emphasized the potential of natural scaffolds as sustainable multi-target-directed ligands (MTDLs) for AD and PD therapeutics.