Plant Compounds Emerge as Multitarget Leads Against Alzheimer’s Disease in Virtual Screen

Alzheimer’s disease has stubbornly resisted every attempt at a cure, with approved drugs offering only temporary symptomatic relief while the underlying neurodegeneration marches on. Now, a computational study from Bar-Ilan University’s Azrieli Faculty of Medicine in Safed, Israel, has taken aim at this problem from an unusual angle: screening thousands of plant-derived compounds against ten key proteins implicated in the disease simultaneously. The research, led by Samuel Mesfin, Naamah Bloch and Abraham O. Samson and published in Frontiers in Drug Discovery, identified 36 phytochemicals capable of binding three or more Alzheimer’s-related targets at once, several of which have independent experimental evidence of neuroprotective activity.

The rationale for a multitarget approach stems from the sheer complexity of Alzheimer’s pathology. The disease involves protein aggregation of amyloid-beta outside neurons and tau inside neurons, cholinergic neurodegeneration, oxidative stress, neuroinflammation, and synaptic dysfunction, all interconnected. Familial cases, which account for less than 5 percent of the total, are driven by mutations in the amyloid precursor protein or the gamma-secretase subunits presenilin 1 and 2, leading to overproduction of aggregation-prone amyloid-beta fragments. Sporadic cases, representing more than 95 percent, are strongly influenced by the apolipoprotein E4 allele, which impairs clearance of insoluble amyloid-beta and disrupts the blood-brain barrier. Because no single molecular driver dominates, the authors argue that compounds modulating several targets at once may be more effective than the conventional single-target strategy that has repeatedly failed in clinical trials.

To identify the most relevant protein targets, the team employed a literature-mining strategy based on normalized PubMed association scores. They retrieved a list of 20,594 protein-coding human genes from NCBI’s gene resource, counted citations for each gene, and then counted co-citations with Alzheimer’s disease using MeSH terms. Dividing the co-citation count by the total citation count produced a normalized association score, which was then adjusted with a penalty factor to reduce the contribution of genes with very low citation counts. An in-house Python script automated the citation counts for all protein-coding genes. The top ten proteins most strongly associated with Alzheimer’s disease emerged as presenilin 1 and 2, amyloid precursor protein, beta-secretase 1, microtubule-associated protein tau, apolipoprotein E4, acetylcholinesterase, butyrylcholinesterase, cyclin-dependent kinase 5, glycogen synthase kinase 3 beta, and brain-derived neurotrophic factor.

With targets in hand, the researchers turned to structure-based virtual screening. They retrieved a virtual library of 2,847 phytochemical compounds from the PubChem database classification of KEGG phytochemicals, prepared the ligands by adding polar hydrogens and Gasteiger charges, and docked them against representative PDB structures of each target using AutoDock Vina. Crucially, they employed blind docking, with grid boxes covering the entire volume of each protein rather than a predefined binding site, allowing the compounds to explore the full protein surface. The docking protocol was validated by redocking co-crystallized ligands into their native receptors, including donepezil with acetylcholinesterase, verubecestat with BACE1, and osugacestat with presenilin 1. Redocked poses yielded RMSD values of 2.1 angstroms or less, and the correlation between predicted and experimental free-energy values was moderately strong at R = 0.61, attesting to the precision of the setup.

The screening results were striking. Retaining the top 100 ligands for each target produced 362 unique phytochemicals, indicating that only a portion of the library binds to multiple targets simultaneously. Applying a multitarget filter that required binding to three or more Alzheimer’s-related proteins narrowed the selection to 96 compounds. A further filter based on heavy atom count, keeping only ligands with 33 heavy atoms or fewer, reduced the final dataset to 36 compounds. The heavy-atom criterion reflects ligand efficiency metrics, where smaller compounds with good potency are preferred, and correlates with better predicted blood-brain barrier penetration using a cubic polynomial function developed by Weaver and coworkers. Of these 36 top multitarget ligands, 11 fulfill Lipinski’s rule of five with no violations, while 25 have a single logP violation.

The 36 compounds fall into five chemical families. Alkaloids include rutaecarpine, sanguinarine and strychnine. Steroidal alkaloids comprise tomatidine, cyclopamine, jervine, verticine and verticinone. Steroidal sapogenins include diosgenin, hecogenin, yamogenin, tigogenin, smilagenin, sarsasapogenin, neogitogenin and ruscogenin. Triterpenoids and sterols form the largest group, with alpha-spinasterin, fucosterol, stigmasterol, beta-sitosterol, gamma-sitosterol, isofucosterol, germanicol, lupeol, beta-amyrin, alpha-amyrin, taraxerol, friedelin, tetrahymanol, tingenone, parkeol, methylenecycloartanol, ursolic acid, oleanolic acid and obacunone. A single sesquiterpene, lactucopicrin, rounds out the list. Several of these compounds have been independently shown in the literature to inhibit amyloid aggregation, tau phosphorylation, neuroinflammation and cholinergic dysfunction, lending credibility to the computational predictions.

To probe binding mechanisms in detail, the team performed interaction analysis using the Protein-Ligand Interaction Profiler on three representative compounds that commonly target both acetylcholinesterase and GSK3β: lactucopicrin, oleanolic acid and rutaecarpine. Lactucopicrin formed hydrogen bonds with TYR72, TYR124 and SER293 of acetylcholinesterase, residues part of the peripheral anionic site at the entrance of the active site gorge. Oleanolic acid formed a hydrogen bond with ARG247, part of the acyl loop door, suggesting a non-competitive inhibition mechanism. Rutaecarpine formed pi-pi stacking interactions with TRP286 and TYR341, similarly to the native ligand donepezil. On GSK3β, lactucopicrin formed hydrogen bonds with ASP200 and LYS85, while oleanolic acid formed salt bridges with ARG141 and ARG144, all residues within the ATP-binding pocket previously reported as consistent interaction hotspots in small-molecule inhibitors.

Molecular dynamics simulations of 50 nanoseconds for the same three compounds in complex with acetylcholinesterase and GSK3β demonstrated stable protein-ligand complexes. The acetylcholinesterase backbone remained globally stable with maximum RMSD values of approximately 2.5 angstroms, consistent with structurally stable complexes. In GSK3β, ligand binding displayed lower backbone RMSD compared to the unbound form, indicating reduced conformational fluctuations upon ligand binding. Notably, lactucopicrin demonstrated greater structural stability than the co-crystallized control ligand QQA throughout the simulation. RMSF analysis revealed residue-level fluctuations generally below 1.0 angstrom for both complexes, and radius of gyration analysis confirmed preserved global compactness of both protein structures. Rutaecarpine derivatives have been shown to inhibit acetylcholinesterase, oleanolic acid has been found to inhibit the same enzyme, and lactucopicrin has been shown to inhibit it as well, providing experimental corroboration for the computational findings.

The study is not without limitations. Molecular docking is inherently limited by the accuracy of scoring functions, and the beneficial or deleterious effects of these phytochemicals in Alzheimer’s treatment remain to be validated experimentally and clinically. The literature-mining method is subject to the current state of published research and may be biased against new or poorly studied proteins. Multitarget binding does not necessarily indicate a better chance at treatment; it could reflect less discriminate selectivity and greater potential toxicity, as seen with strychnine and sanguinarine, which are primarily associated with cytotoxic rather than therapeutic effects. Molecular dynamics simulations were performed for 50 nanoseconds in a single run for each complex, and replicate or extended simulations could provide additional confidence. Nevertheless, the authors conclude that several of the identified structurally diverse phytochemicals, including rutaecarpine, diosgenin, oleanolic acid, ursolic acid, lactucopicrin, hecogenin, sarsasapogenin, fucosterol, beta-sitosterol and stigmasterol, represent a valuable source of multitarget-directed lead compounds for further development in Alzheimer’s therapeutics, pending experimental validation.

Subject of Research: Virtual screening of phytochemicals against multiple protein targets of Alzheimer's disease

Article Title: Virtual screening of phytochemicals against multiple protein targets of Alzheimer’s disease

Article References: Mesfin, S., Bloch, N., & Samson, A. O. (2026). Virtual screening of phytochemicals against multiple protein targets of Alzheimer’s disease. Frontiers in Drug Discovery, 6, Article 1922953. https://doi.org/10.3389/fddsv.2026.1922953

Image Credits: AI Generated

DOI: 10.3389/fddsv.2026.1922953

Keywords: Alzheimer's disease, phytochemicals, virtual screening, molecular docking, molecular dynamics, polypharmacology, drug discovery, natural products, Virtual, screening, against, multiple