The research team, led by The-Huan Tran, Thai-Son Tran and Thanh-Dao Tran of the University of Medicine and Pharmacy at Ho Chi Minh City and Hue University in Vietnam, constructed an integrative in silico workflow to mine a molecular library derived from known inhibitors of both enzymes, including the cholinesterase drug rivastigmine and the monoacylglycerol lipase inhibitors JZL-184 and ABX-1431. Acetylcholinesterase breaks down the neurotransmitter acetylcholine, and its inhibition has long underpinned symptomatic Alzheimer’s therapy. Monoacylglycerol lipase, by contrast, degrades 2-arachidonoylglycerol, an endocannabinoid signaling lipid whose preservation has been linked to dampened neuroinflammation and enhanced glial immunity. Hitting both enzymes with a single molecule could, in principle, address cognitive decline and inflammatory damage at the same time.

Starting from 365 candidate compounds, the investigators carried out molecular docking against crystal structures of human acetylcholinesterase and human monoglyceride lipase, using AutoDock Vina to predict binding poses and scores. They then applied interaction-based filtering to retain only those ligands that reproduced the key contacts characteristic of known inhibitors, followed by in silico prediction of absorption, distribution, metabolism and excretion properties and toxicity. This successive narrowing of the chemical space reflects a widely adopted paradigm in early-stage drug discovery, where computational filters are stacked so that only chemically sensible, pharmacokinetically plausible candidates advance to the most expensive and time-consuming analyses.

One compound, designated H34, rose to the top of the ranking. Molecular dynamics simulations run with GROMACS under the CHARMM36 force field examined the structural stability of the H34-enzyme complexes over time, tracking the root-mean-square deviation of the protein backbone, the root-mean-square fluctuations of individual residues, the radius of gyration and the solvent-accessible surface area. Across these metrics, the H34 complexes displayed comparatively favorable stability, suggesting that the ligand does not disrupt the overall fold of either enzyme and remains seated in the binding pocket under physiologically realistic conditions.

To move beyond qualitative stability assessments, the team estimated binding free energies using the MM/GBSA end-state method implemented in the gmx_MMPBSA tool. H34 yielded estimated binding free energies of −30.96 and −37.34 kcal/mol for the two enzyme targets, values that compare favorably within the context of the screened series and support the compound’s predicted affinity for both proteins. These calculations decompose the interaction into electrostatic, van der Waals and solvation contributions, offering a thermodynamic rationale for why the molecule holds on to each active site.

Further dynamical profiling added depth to the picture. ProLIF interaction mapping, which encodes protein-ligand contacts as fingerprints across the simulation trajectory, showed that the interactions anchoring H34 in each active site persisted over time rather than flickering in and out. Free energy landscape analysis, built on dihedral angle principal component analysis, mapped the conformational behavior of the complexes and revealed energetically stable basins, indicating that the ligand-bound enzymes do not wander between widely divergent conformations. Together, these analyses portray a compound whose binding mode is not merely a static docking artifact but a durable, low-energy arrangement.

Drug-likeness and safety filters reinforced the computational case for H34. SwissADME-based pharmacokinetic predictions indicated favorable properties relevant to oral absorption and brain exposure, while ProTox 3.0 predictions pointed to low acute toxicity. Because any candidate intended for Alzheimer’s disease must reach the central nervous system without accumulating liability elsewhere, these early pharmacokinetic and toxicological signals, though still predictive rather than experimental, are a meaningful part of the triage process that decides which molecules justify synthesis and laboratory testing.

The study’s authors are careful to frame H34 as a candidate rather than a drug. All findings derive from computational models, and docking scores, MM/GBSA estimates and simulations can only approximate the thermodynamics and kinetics of real enzyme inhibition. The necessary next steps include chemical synthesis and in vitro enzyme assays to measure actual inhibitory potency against both acetylcholinesterase and monoacylglycerol lipase, followed by cell-based and ultimately animal studies to test whether the dual-target hypothesis translates into cognitive and anti-inflammatory benefit in living systems.

Even so, the work illustrates why integrated computational strategies have become indispensable in multi-target drug discovery. By combining ligand-based library generation, structure-based docking, interaction fingerprinting, molecular dynamics, free energy estimation and ADME and toxicity prediction in a single pipeline, the researchers compressed an enormous search space into one well-characterized hit in silico. If H34 or its analogs survive experimental validation, the compound family could contribute to a new generation of multi-target-directed ligands designed for the complexity of Alzheimer’s disease, where modulating a single pathway has repeatedly fallen short.

Subject of Research: Computational discovery of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer's disease

Article Title: Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer’s disease

Article References: Tran, T.-H., Tran, T.-S., & Tran, T.-D. (2026). Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer’s disease. Molecular Diversity. https://doi.org/10.1007/s11030-026-11725-w

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11725-w

Keywords: Alzheimer's disease, acetylcholinesterase, monoacylglycerol lipase, dual inhibition, molecular docking, molecular dynamics, MM/GBSA, virtual screening, drug discovery, neuroinflammation, multi-target ligands, ADME prediction