Isolation, Identification and In Silico Evaluation of Novel Cholinesterase Inhibitors from Terminalia triptera Stapf.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Molecules . 2026 Mar 27;31(7):1113. doi: 10.3390/molecules31071113 Search in PMC Search in PubMed View in NLM Catalog Add to search Isolation, Identification and In Silico Evaluation of Novel Cholinesterase Inhibitors from Terminalia triptera Stapf. Tu Quy Phan Tu Quy Phan 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) Find articles by Tu Quy Phan 1 , Hung Tse Huang Hung Tse Huang 2 Division of Chinese Materia Medica Development, National Research Institute of Chinese Medicine, Taipei 11221, Taiwan; [email protected] Find articles by Hung Tse Huang 2 , San-Lang Wang San-Lang Wang 3 Department of Chemistry, Tamkang University, New Taipei City 25137, Taiwan 4 Life Science Development Center, Tamkang University, New Taipei City 25137, Taiwan Find articles by San-Lang Wang 3, 4, * , Dinh Sy Nguyen Dinh Sy Nguyen 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) Find articles by Dinh Sy Nguyen 1 , Manh Dung Doan Manh Dung Doan 5 Institute of Biotechnology and Environment, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] Find articles by Manh Dung Doan 5 , Thi Huyen Thoa Pham Thi Huyen Thoa Pham 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) Find articles by Thi Huyen Thoa Pham 1 , Thi Kim Thu Phan Thi Kim Thu Phan 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) Find articles by Thi Kim Thu Phan 1 , Ba Phong Truong Ba Phong Truong 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) Find articles by Ba Phong Truong 1 , Van Bon Nguyen Van Bon Nguyen 5 Institute of Biotechnology and Environment, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] Find articles by Van Bon Nguyen 5, * Editors: Manuela Leri , Monica Bucciantini Author information Article notes Copyright and License information 1 Department of Science and Technology, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] (T.Q.P.); [email protected] (D.S.N.); [email protected] (T.H.T.P.); [email protected] (T.K.T.P.); [email protected] (B.P.T.) 2 Division of Chinese Materia Medica Development, National Research Institute of Chinese Medicine, Taipei 11221, Taiwan; [email protected] 3 Department of Chemistry, Tamkang University, New Taipei City 25137, Taiwan 4 Life Science Development Center, Tamkang University, New Taipei City 25137, Taiwan 5 Institute of Biotechnology and Environment, Tay Nguyen University, Dak Lak 630000, Vietnam; [email protected] * Correspondence: [email protected] (S.-L.W.); [email protected] (V.B.N.) Roles Manuela Leri : Academic Editor Monica Bucciantini : Academic Editor Received 2026 Feb 19; Revised 2026 Mar 23; Accepted 2026 Mar 25; Collection date 2026 Apr. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license . PMC Copyright notice PMCID: PMC13075089  PMID: 41976156 Abstract Alzheimer’s disease (AD) remains a significant global health challenge, highlighting the need for novel dual inhibitors targeting acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). This study investigated the trunk bark of Terminalia triptera Stapf. as a potential source of bioactive secondary metabolites for AD management. Bioassay-guided isolation led to the identification of two flavan-3-ol derivatives, epicatechin-(4β→8)- ent -catechin ( 1 ) and (−)-catechin ( 2 ), reported here for the first time from this species. In vitro assays demonstrated that the dimeric compound 1 exhibited stronger dual inhibitory activity against AChE and BChE, with IC 50 values of 4.41 × 10 −4 and 4.75 × 10 −4 mol/L, respectively, surpassing the reference compound berberine chloride. Molecular docking analysis revealed that compound 1 formed extensive interactions within both catalytic and peripheral anionic sites of the enzymes. Density Functional Theory (DFT) calculations indicated high kinetic stability, reflected by large HOMO–LUMO energy gaps (6.66–6.97 eV), while global reactivity descriptors suggested lower electrophilicity (ω = 2.19–2.34 eV), supporting a potentially favorable safety profile. Furthermore, 100 ns molecular dynamics simulations confirmed stable ligand–protein complexes stabilized by hydrogen-bond networks and deep binding within catalytic pockets. Overall, these findings highlight T. triptera and its dimeric proanthocyanidins as promising multi-target candidates for anti-Alzheimer drug development. Keywords: Terminalia triptera , cholinesterase inhibitors, Alzheimer’s disease, DFT, molecular docking, molecular dynamics 1. Introduction Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the primary etiology of dementia worldwide, accounting for approximately 60–70% of all cases [ 1 ]. Currently, an estimated 57 million individuals globally live with dementia, with nearly 10 million new diagnoses annually—a figure projected to escalate sharply due to the accelerating aging of the global population [ 1 , 2 ]. Beyond progressive cognitive decline and memory impairment, AD severely compromises activities of daily living (ADL), autonomy, and social engagement, ultimately culminating in profound disability and increased mortality. Indeed, dementia has emerged as a leading cause of disability-adjusted life years (DALYs) among older adults and is ranked among the top global causes of death, posing a critical challenge to public health systems [ 1 ]. The socioeconomic burden of AD is substantial; global costs reached approximately USD 1.3 trillion in 2019 and are anticipated to rise further [ 1 , 3 ]. These expenditures encompass clinical care, long-term institutional support, and informal caregiving, exerting immense pressure on healthcare infrastructures, particularly in low- and middle-income countries [ 1 , 2 , 3 , 4 ]. Pathologically, AD is characterized by complex, multifactorial mechanisms, including the extracellular accumulation of amyloid-beta (Aβ) plaques, intracellular hyperphosphorylation of tau proteins, oxidative stress, neuroinflammation, and progressive synaptic dysfunction [ 5 , 6 , 7 ]. Among the prevailing pathogenic frameworks, the cholinergic hypothesis remains one of the most rigorously validated, asserting that the degeneration of cholinergic neurons and the subsequent depletion of acetylcholine levels are pivotal contributors to cognitive deficits [ 8 ]. Consequently, the pharmacological inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) constitutes a cornerstone therapeutic strategy to enhance cholinergic neurotransmission and mitigate symptomatic decline [ 9 ]. Although several cholinesterase inhibitors (ChEIs), such as donepezil, rivastigmine, and galantamine, are clinically established, their efficacy remains symptomatic rather than disease-modifying. Furthermore, their associated side effects and inability to arrest disease progression underscore the imperative for the discovery of novel, safer, and more effective therapeutic agents [ 10 ]. Natural products derived from medicinal flora remain a reservoir for drug discovery, offering structurally diverse scaffolds with potent neuroprotective and cholinesterase inhibitory properties [ 11 , 12 , 13 ]. Bio-guided isolation coupled with in silico molecular docking has emerged as a robust methodology for identifying novel bioactive secondary metabolites and elucidating their molecular interactions with therapeutic targets. In this context, the genus Terminalia (Combretaceae) has garnered significant interest due to its rich phytochemical diversity and broad spectrum of pharmacological activities, notably its antioxidant and neuroprotective effects [ 14 , 15 ]. Despite this potential, systematic investigations specifically targeting the cholinesterase inhibitory constituents of Terminalia triptera Stapf. remain sparse. In recent years, in silico approaches have become indispensable tools in modern drug discovery, enabling the rapid identification and optimization of potential therapeutic candidates. Computational strategies, including both ligand-based and structure-based virtual screening, facilitate the efficient exploration of chemical space and prioritization of bioactive compounds [ 16 , 17 ]. Molecular docking provides initial insights into binding modes, whereas molecular dynamics (MD) simulations further evaluate the stability and dynamic behavior of protein–ligand complexes under near-physiological conditions [ 18 ]. In addition, ADMET predictions enable early assessment of pharmacokinetic and safety profiles, thereby improving the efficiency of lead selection [ 19 ]. In this context, the present study integrates bioassay-guided isolation with computational approaches to investigate cholinesterase inhibitors from Terminalia triptera . This combined strategy aims to identify structurally characterized compounds with potential neuroprotective activity and to provide a scientific basis for their further development as candidate agents for Alzheimer’s disease management. 2. Results and Discussion 2.1. New Records of Terminalia triptera Extracts as Potential Sources of Cholinesterase Inhibitors The cholinesterase inhibitory activities of Terminalia triptera extract were evaluated and presented in Table 1 . The trunk bark extract of T. triptera exhibited moderate inhibitory effects against both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with IC 50 values of 335.42 ± 8.62 µg/mL and 405.35 ± 9.03 µg/mL, respectively. Notably, the extract demonstrated dual inhibition toward both cholinesterase enzymes, which is considered beneficial for potential anti-Alzheimer’s drug development since BChE activity increases during the later stages of Alzheimer’s disease. Table 1. Cholinesterase Inhibitory effect of Terminalia triptera Stapf. and other reported medicinal plants collected in the Central Higland of Vietnam. Herbal Species Parts Used Cholinesterase Inhibitory Effect IC 50 (µg/mL) Ref. AChE BChE Terminalia triptera Stapf. Trunk bark 335.42 ± 8.62 405.35 ± 9.03 This study Berberine chloride Commercial Compd. 348.28 ± 5.78 368.35 ± 6.43 Tetrastigma erubescens Aerial parts extract 730.6 ND Phan et al. (2025) [ 20 ] Eclipta prostrata L. Aerial parts extract 76.8 ± 0.8 ND Phan et al. (2023) [ 21 ] Dillenia ovata Leaves extract 110 ± 10 ND Doan et al. (2023) [ 22 ] Dillenia ovata Trunk bark extract 710 ± 10 ND Doan et al. (2023) [ 22 ] Dillenia ovata Branch extract 400 ± 10 ND Doan et al. (2023) [ 22 ] Sophora flavescens Root extract 107.7 ± 2.3 ND Thang et al. (2022) [ 23 ] Open in a new tab ND = Not determined, IC 50 values expressed as mean ± SD when available, standard reference inhibitor: berberine chloride. Berberine chloride was used as the positive control due to its reported inhibitory activity against both AChE and BChE and its suitability for comparison with plant-derived compounds. As a natural compound with moderate potency, it provides a more appropriate benchmark than highly potent clinical inhibitors for phytochemical studies. Notably, the IC 50 values of the Terminalia triptera extract were comparable to those of berberine chloride (AChE: 348.28 ± 5.78 µg/mL; BChE: 368.35 ± 6.43 µg/mL), indicating a similar level of moderate inhibitory activity within the same potency range. Several studies have reported AChE inhibitory activities of medicinal plants collected from the Central Highlands of Vietnam, with AChE IC 50 values ranging from approximately 76.8 to 730.6 µg/mL, depending on plant species and parts used ( Table 1 ) [ 20 , 21 , 22 , 23 ]. In these reports, most extracts were primarily evaluated for AChE inhibition, while BChE activity was less frequently assessed. In this context, the present study provides additional evidence by demonstrating that Terminalia triptera trunk bark extract exhibits inhibitory effects against both AChE (IC 50 = 335.42 ± 8.62 µg/mL) and BChE (IC 50 = 405.35 ± 9.03 µg/mL). The simultaneous evaluation of both enzymes is noteworthy, as BChE plays an increasingly important role during the later stages of Alzheimer’s disease. Therefore, these findings suggest that T. triptera represents a promising medicinal plant from the Central Highlands with potential relevance for cholinesterase-targeted research. The genus Terminalia is well known for its rich phytochemical diversity and a wide range of pharmacological activities, including neuroprotective effects [ 14 , 15 ]. Several species within this genus have also been reported to exhibit potential acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activities [ 14 , 15 , 24 , 25 , 26 ]. However, to the best of our knowledge, this study is the first to report the potential cholinesterase inhibitory activity of Terminalia triptera Stapf., highlighting its novelty and suggesting promising prospects for further neuropharmacological investigations. 2.2. Isolation and Chemical Structures Elucidation of Bioactive Compounds from Terminalia triptera The bioactive constituents were isolated from the stem bark of Terminalia triptera through a bioassay-guided fractionation strategy targeting cholinesterase inhibitory activity ( Scheme 1 ). As summarized in Table S1 (Supplementary Material) , the crude extract (TT) exhibited moderate inhibitory effects against both AChE and BChE at the tested concentration, showing inhibition rates of approximately 81% and 78%, respectively, thereby supporting its further fractionation. Scheme 1. Open in a new tab A scheme of purification and identification of bioactive inhibitors from the MeOH extract of Terminalia triptera (TT). The purified compounds were epicatechin-(4β→8)- ent -catechin (compd 1 ) and (−)-catechin (compd 2 ). The crude extract was subsequently fractionated using Diaion HP-20 column chromatography with a MeOH/H 2 O gradient to afford five fractions (TT-1 to TT-5), all of which were evaluated under the same conditions ( Table S1 ). Among them, TT-4 displayed the strongest activity, with inhibition rates of approximately 85% (AChE) and 81% (BChE), indicating enrichment of active constituents. Accordingly, TT-4 was selected for further purification. Further separation of TT-4 using Sephadex LH-20 chromatography yielded three sub-fractions (TT-4.1–TT-4.3). Notably, TT-4.2 exhibited the highest inhibitory activity, with inhibition values of 97% (AChE) and 95% (BChE), suggesting that the major bioactive compounds were concentrated in this fraction. Subsequent purification of TT-4.2 by preparative HPLC led to the isolation of two pure compounds (Compounds 1 and 2 ). Compound 1 showed inhibition values of 98% (AChE) and 93% (BChE), whereas Compound 2 exhibited lower inhibition values of 93% and 94%, respectively ( Table S1 ). The chemical structures of the isolated compounds were elucidated through comprehensive spectroscopic analysis, including nuclear magnetic resonance (NMR) and mass spectrometry (MS), followed by comparison with previously reported spectral data. Based on these analyses, the flavan-3-ol derivatives compounds were identified as epicatechin-(4β→8)- ent -catechin ( 1 ) [ 27 ] and (−)-catechin ( 2 ) [ 28 ]. Epicatechin-(4β→8)- ent -catechin ( 1 ) was obtained as a yellow amorphous powder; HESI-MS m/z : 577.1356 [M − H] − ; 1 H-NMR (600 MHz, pyridine- d 5 ): δ 6.11 (1H, brs, H-2), δ 4.86 (1H, brs, H-3), δ 5.83 (1H, brs, H-4), δ 6.57 (1H, brs, H-7), δ 6.69 (1H, brs, H-9), δ 7.95 (1H, brs, H-2′), δ 7.38 (1H, brs, H-5′), δ 7.10 (1H, brs, H-6′), δ 5.18 (1H, brs, H-2″), δ 4.35 (1H, ddd, J = 6.0, 12.0 Hz, H-3″), δ 3.49 (1H, dd, J = 3.6, 16.0 Hz, H-4″a), δ 3.27 (1H, dd, J = 8.4, 16.0 Hz, H-4″b), δ 6.57 (1H, brs, H-7″), δ 7.95 (1H, brs, H-2‴), δ 7.38 (1H, brs, H-5‴), δ 7.10 (1H, brs, H-6‴); 13 C-NMR (150 MHz, MeOH- d 4 ): δ 77.5 (C-2), 72.8 (C-3), 37.4 (C-4), 102.2 (C-5), 158.3 (C-6), 96.0 (C-7), 158.5 (C-8), 95.4 (C-9), 159.0 (C-10), 132.9 (C-1′), 116.4 (C-2′), 146.4 (C-3′), 146.1 (C-4′), 115.6 (C-5′), 119.1 (C-6′), δ 82.5 (C-2″), 67.8 (C-3″), 29.1 (C-4″), 101.3 (C-5″), 155.3 (C-6″), 97.1 (C-7″), 156.3 (C-8″), 108.5 (C-9″), 154.5 (C-10″), 132.1 (C-1‴), 115.8 (C-2‴), 146.1 (C-3′), 146.0 (C-4′), 115.4 (C-5′), 119.0 (C-6′). The High-resolution ESI-MS, 1 H NMR (600 MHz, pyridine- d 5 ), and 13 C NMR (150 MHz, MeOH- d 4 ) spectra of compound 1 are shown in Figures S1–S3 , respectively. (−)-catechin ( 2 ) was obtained as a white amorphous powder; HESI-MS m/z : 289.0720 [M − H] − ; 1 H-NMR (500 MHz, MeOH- d 4 ): δ 4.84 (1H, brs, H-2), δ 4.20 (1H, m, H-3), δ 2.88 (1H, dd, J = 4.5, 16.0 Hz, H-4a), δ 2.75 (1H, dd, J = 3.0, 16.0 Hz, H-4b), δ 5.96 (1H, d, J = 2.0 Hz, H-7), δ 5.94 (1H, d, J = 2.0 Hz, H-9), δ 6.70 (1H, d, J = 2.0 Hz, H-2′), δ 6.78 (1H, d, J = 8.0 Hz, H-5′), δ 6.82 (1H, ddd, J = 1.0, 2.0, 8.0 Hz, H-6′); 13 C-NMR (125 MHz, MeOH- d 4 ): δ 78.5 (C-2), 66.1 (C-3), 27.9 (C-4), 98.7 (C-5), 156.6 (C-6), 95.0 (C-7), 156.3 (C-8), 94.5 (C-9), 156.0 (C-10), 130.9 (C-1′), 113.9 (C-2′), 144.6 (C-3′), 144.4 (C-4′), 114.5 (C-5′), 118.0 (C-6′). The High-resolution ESI-MS, 1 H NMR (500 MHz, pyridine- d 4 ), and 13 C NMR (125 MHz, MeOH- d 4 ) spectra of compound 2 are shown in Figures S4–S6 , respectively. The HPLC chromatographic profiles of the isolated compounds ( Figure 1 ) showed well-resolved major peaks with distinct retention times (RT), supporting successful purification and high chemical homogeneity. For epicatechin-(4β→8)- ent -catechin ( Figure 1 a), the chromatogram displayed a dominant sharp peak at an RT of approximately ~9 min, characterized by a symmetrical peak shape and a stable baseline, with only negligible minor signals detected, indicating minimal co-eluting impurities. Similarly, (−)-catechin ( Figure 1 b) exhibited a prominent major peak at an RT of approximately ~22.5 min, suggesting efficient chromatographic separation under the applied conditions. The chromatographic results demonstrate that epicatechin-(4β→8)-ent-catechin (a) and (−)-catechin (b) were successfully purified and are suitable for subsequent biological activity evaluation and further natural product studies. Figure 1. Open in a new tab The high-performance liquid chromatography finger printings of epicatechin-(4β→8)- ent -catechin ( a ) and ( − )-catechin ( b ) purified from the MeOH extract of Terminalia triptera Stapf. The isolation and structural elucidation of the flavan-3-ol derivatives epicatechin-(4β→8)- ent -catechin ( 1 ) and (−)-catechin ( 2 ) from Terminalia triptera represent a noteworthy addition to the phytochemical profile of this species. To the best of our knowledge, these compounds have not previously been reported as isolated constituents of T. triptera , as earlier studies mainly focused on extract-level bioactivity rather than detailed compound characterization. Flavan-3-ols such as catechin and epicatechin are well documented in several Terminalia species, including T. chebula , T. arjuna , and related taxa, where they contribute to antioxidant and pharmacological activities [ 15 , 29 , 30 ]. The occurrence of these metabolites in T. triptera is therefore consistent with the chemotaxonomic features of the genus, which is rich in tannins and proanthocyanidin-type polyphenols [ 15 , 31 ]. Nevertheless, the present work provides the first structural evidence supporting the presence of these flavan-3-ol derivatives in this species, thereby expanding its known chemical diversity and offering a plausible basis for its reported biological activities. 2.3. Evaluation of the Cholinesterase Inhibitory Effect of Purified Compounds from Terminalia triptera Stapf. The cholinesterase inhibitory activities of the isolated compounds from Terminalia triptera were evaluated against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the results are summarized in Table 2 . Both compounds exhibited measurable inhibitory effects toward the two enzymes, although differences in potency were observed. Table 2. Cholinesterase inhibition of purified compounds and berberine chloride. Sample/Compound Cholinesterase Inhibition (mol/L) AChE BChE Epicatechin-(4β→8)- ent -catechin ( 1 ) 4.41 × 10 −4 4.75 × 10 −4 (−)-Catechin ( 2 ) 11.9 × 10 −4 12.1 × 10 −4 Berberine chloride (Positive control) 8.97 × 10 −4 9.32 × 10 −4 Open in a new tab All experiments were performed in triplicate ( n = 3). Data are presented as mean ± SD. AChE: acetylcholinesterase; BChE: butyrylcholinesterase. (−)-Catechin ( 2 ) exhibited slightly lower cholinesterase inhibitory activity than the positive control, berberine chloride, with IC 50 values of 11.9 × 10 −4 mol/L against AChE and 12.1 × 10 −4 mol/L against BChE, compared to 8.97 × 10 −4 mol/L and 9.32 × 10 −4 mol/L, respectively, for berberine chloride. These results are consistent with previous studies indicating that (−)-catechin possesses cholinesterase inhibitory activity and may contribute to the overall bioactivity of flavan-3-ol-rich extracts. Furthermore, earlier reports suggest that catechin derivatives may exhibit enhanced inhibitory effects through synergistic interactions with other flavan-3-ols [ 32 ]. Epicatechin-(4β→8)- ent -catechin ( 1 ) demonstrated inhibitory activity against both AChE and BChE, with IC 50 values of 4.41 × 10 −4 and 4.75 × 10 −4 mol/L, respectively, indicating a higher potency than that of compound 2 . The improved activity of compound 1 relative to the monomeric catechin may be attributed to its dimeric structure, which could facilitate stronger interactions with the enzyme binding sites through increased molecular size and additional hydroxyl functionalities. Both compounds displayed similar inhibition toward AChE and BChE, suggesting a relatively non-selective cholinesterase inhibition profile. The inhibitory activity of isolated compounds was observed in the high micromolar range, which is consistent with previous reports for flavan-3-ol derivatives that generally exhibit weak AChE and BChE inhibition due to their low binding affinity [ 32 , 33 ]. However, given the relatively high IC 50 values, the potential contribution of non-specific effects such as colloidal aggregation cannot be excluded [ 34 ]. Therefore, the observed inhibition may reflect low-affinity on-target interactions, although further studies (e.g., detergent-based assays or DLS analysis) are required to clarify the underlying mechanism. To the best of our knowledge, this study represents the first report of dual AChE and BChE inhibitory activity for epicatechin-(4β→8)- ent -catechin. These findings highlight the potential importance of flavan-3-ol dimerization in enhancing cholinesterase inhibition and provide new insights into the bioactive constituents of Terminalia triptera . 2.4. In Silico Evaluation of Cholinesterase Inhibitors Isolated from Terminalia triptera 2.4.1. Molecular Docking Molecular docking studies were conducted to evaluate the binding interactions between phytochemical constituents isolated from Terminalia triptera and two cholinesterase enzymes relevant to Alzheimer’s disease, namely acetylcholinesterase (AChE, PDB ID: 1C2B) and butyrylcholinesterase (BuChE, PDB ID: 1P0I). The binding pockets were carefully defined to encompass the key catalytic regions ( Figure 2 ). For AChE, the grid box was centered at (x = 33.22, y = 84.30, z = 28.29) with dimensions of 29.96 × 30.00 × 31.77 Å. For BuChE, the grid box center was set at (x = 131.91, y = 113.09, z = 40.36) with dimensions of 40.00 × 24.40 × 31.76 Å. This configuration ensured coverage of both the Catalytic Anionic Site (CAS) and the Peripheral Anionic Site (PAS), facilitating the identification of potential dual-site inhibitors. Figure 2. Open in a new tab 3D visualization of the docking grid box setup covering the active sites of AChE and BChE. Molecular docking is widely employed as a computational screening approach to predict the binding affinity of ligands toward target enzymes, with docking scores lower than −3.20 kcal/mol generally indicating potential inhibitory activity [ 35 ]. As summarized in Table 3 , all tested ligands, including epicatechin-(4β→8)- ent -catechin ( 1 ), (−)-catechin ( 2 ), and berberine chloride ( 3 ), displayed favorable binding energies against both acetylcholinesterase (AChE; −9.8 to −8.3 kcal/mol) and butyrylcholinesterase (BuChE; −11.9 to −8.7 kcal/mol). Among them, the dimeric compound epicatechin-(4β→8)- ent -catechin ( 1 ) exhibited the strongest predicted binding affinity toward both targets, with docking scores of −9.8 kcal/mol for AChE and −11.9 kcal/mol for BuChE, outperforming its monomeric counterpart (−)-catechin ( 2 ) (−8.3 and −9.1 kcal/mol, respectively) as well as the reference drug berberine chloride ( 3 ) (−8.5 and −8.7 kcal/mol). Thermodynamically, the markedly lower binding energy of compound 1 , particularly against BuChE, suggests that its bulky dimeric scaffold is structurally suited to occupy the large hydrophobic binding pocket, thereby forming a more stable ligand–protein complex [ 36 ]. These in silico results are consistent with the experimental findings presented in Table 2 . Detailed ligand–enzyme interaction mechanisms are illustrated in Figure 3 and summarized in Table 3 . Table 3. Binding affinities and interacting residues of the purified compounds with AChE and BChE. Compound AChE (1C2B) BChE (1P0I) Binding Energy (kcal/mol) Key Interacting Residues Binding Energy (kcal/mol) Key Interacting Residues Epicatechin-(4β→8)- ent -catechin ( 1 ) −9.8 Leu72, Thr71, Tyr68, Tyr120, Ser289, Glu288 −11.9 Trp79, Leu283, Ser284, Gly113, Ser195 (−)-Catechin ( 2 ) −8.3 Tyr120, Trp282, Ser289, Arg292 −9.1 His433, Tyr329, Asp67, Asn65 Berberine chloride (Positive control) −8.5 Phe291 −8.7 Tyr435, Met432, Trp425, Pro282, Thr281, Ser284, Gly113, Gln116, Gly114 Open in a new tab Figure 3. Open in a new tab 2D interaction diagrams of compounds including epicatechin-(4β→8)- ent -catechin ( 1 ), (−)-catechin ( 2 ), and berberine chloride ( 3 ) with Acetylcholinesterase (1C2B) and Butyrylcholinesterase (1P0I). As illustrated in Figure 2 , compound 1 adopts a conformation that blocks the entrance of the AChE gorge and establishes a strong interaction network with peripheral anionic site (PAS) residues, including Tyr120, Leu72, and Thr71. The interaction with Tyr120 is particularly important, as this residue acts as a gatekeeper controlling substrate entry [ 37 ]. By anchoring at the PAS region, compound 1 likely prevents acetylcholine from accessing the catalytic site. In contrast, the reference compound berberine chloride forms only a single major interaction with Phe291 and fails to generate an effective steric blockade, consistent with its weaker binding affinity (−8.5 kcal/mol). Compound 2 shows a distinct binding pattern, interacting mainly with peripheral and mid-gorge residues (Tyr120, Trp282, Ser289, and Arg292) through hydrogen bonds and hydrophobic contacts. However, its orientation does not form a strong steric blockade or penetrate deeply into the catalytic center, suggesting weaker inhibitory potential. Within BChE (PDB ID: 1P0I), compound 1 exhibits a distinct binding mode compared with C3. While C3 interacts mainly with the peripheral hydrophobic pocket (Tyr435, Trp425), compound 1 penetrates deeper into the catalytic region and forms hydrogen bonds with Ser195 (a key residue of the catalytic triad) and Gly113 in the oxyanion hole [ 38 ]. This binding pattern indicates interaction with key catalytic residues. However, it should be noted that this interpretation is based solely on molecular docking and does not provide definitive evidence of the inhibition mechanism. Experimental enzymatic kinetics would be required to confirm this. In contrast, compound 2 (monomer) and berberine chloride do not show interactions with Ser195, suggesting that the dimeric structure of compound 1 may facilitate deeper access to the catalytic pocket of BChE. 2.4.2. Frontier Molecular Orbitals (FMO) and Global Reactivity Descriptors The frontier molecular orbital (FMO) analysis, particularly the energy gap (ΔEgap) between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), is a fundamental approach for predicting the kinetic stability and chemical reactivity of bioactive compounds [ 39 ]. According to FMO theory, a molecule with a larger energy gap is considered “hard”, exhibiting higher kinetic stability and lower polarizability. Conversely, a smaller energy gap indicates a “soft” molecule, which is typically more reactive and more prone to electronic excitation [ 40 ]. The HOMO–LUMO distributions and energy levels of the purified compounds ( 1 and 2 ) and berberine chloride ( 3 ) are visualized in Figure 4 , with specific energy values summarized in Table 4 . Figure 4. Open in a new tab HOMO-LUMO energy diagrams and interfacial plots of ( 1 ) Compound 1 , ( 2 ) Compound 2 , and ( 3 ) Compound 3 (berberine chloride). The red and green isosurfaces represent the negative and positive phases of the molecular orbital wavefunction, respectively. Table 4. Frontier molecular orbital energies and energy gap values of the studied compounds. Compound E HOMO (eV) E LUMO (eV) ΔE gap (eV) 1 −7.28 −0.62 6.66 2 −7.39 −0.43 6.97 3 −10.01 −4.93 5.09 Open in a new tab Epicatechin-(4β→8)-ent-catechin ( 1 ), (−)-catechin ( 2 ), and berberine chloride ( 3 , positive control). EHOMO and ELUMO correspond to the highest occupied molecular orbital and lowest unoccupied molecular orbital energies (eV), respectively; ΔEgap = ELUMO − EHOMO. As observed in Table 4 , the reference compound 3 exhibits the narrowest energy gap ΔE gap = 5.09 eV), whereas the purified compounds 1 and 2 display significantly larger gaps of 6.66 eV and 6.97 eV, respectively. This substantial increase in the energy gap suggests that compounds 1 and 2 possess higher kinetic stability compared to the reference. To quantitatively assess the chemical behavior, global reactivity descriptors including ionization potential ( I ), electron affinity ( A ), chemical hardness (η), softness ( S ), electronegativity (χ), and electrophilicity index (ω) were calculated based on Koopmans’ theorem [ 41 ] and standard DFT-based equations [ 42 , 43 ]. The results are presented in Table 5 . Table 5. Global chemical reactivity descriptors of the studied compounds. Compound I (eV) A (eV) χ (eV) η (eV) S (eV −1 ) ω (eV) 1 7.28 0.62 3.95 3.33 0.15 2.34 2 7.39 0.43 3.91 3.48 0.14 2.19 3 10.01 4.93 7.47 2.54 0.20 10.98 Open in a new tab Epicatechin-(4β→8)- ent -catechin ( 1 ), (−)-catechin ( 2 ), and berberine chloride ( 3 , positive control). I : ionization potential; A : electron affinity; χ: electronegativity; η: chemical hardness; S : chemical softness; ω: electrophilicity index. All values are expressed in eV (except S in eV −1 ). The data in Table 5 further corroborates these findings. Chemical hardness (η) follows the order 2 (3.48 eV) > 1 (3.33 eV) > 3 (2.54 eV). Since hardness is directly related to the resistance against electron cloud deformation, the higher η values of 1 and 2 indicate that they are chemically harder and less prone to spontaneous decomposition or non-specific reactions [ 44 , 45 ]. Conversely, compound 3 shows the highest softness ( S = 0.20 eV −1 ), aligning with its higher reactivity but also implying a potential for lower selectivity. Notably, the electrophilicity index (ω), which reflects the propensity of a molecule to accept electrons, shows a marked difference among the compounds. The reference compound 3 exhibits a relatively high ω value (10.98 eV), indicating a stronger electrophilic character, whereas the purified compounds 1 (ω = 2.34 eV) and 2 (ω = 2.19 eV) display lower electrophilicity. This variation may influence their chemical reactivity and interactions with biological targets. 2.4.3. Molecular Dynamics Simulation and Complex Stability To validate the stability of the docking predictions and elucidate the dynamic behavior of Compound 1 within the enzyme pockets, 100 ns molecular dynamics (MD) simulations were performed. The structural stability and conformational evolution of the 1–1C2B (AChE) and 1–1P0I (BChE) complexes were assessed using RMSD, RMSF, radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen-bond analyses. The molecular dynamics (MD) simulations were performed in triplicate. The averaged results are shown in Figure 5 and Figure 6 , while the results from individual runs are provided in the Supplementary Information (Figures S7–S10). Figure 5. Open in a new tab RMSD vs. time plot ( a ), RMS function plot of amino acid residues ( b ), Time-dependent Radius of Gyration (Rg) plot ( c ), and Time-dependent Solvent Accessible Surface Area (SASA) plot ( d ) for the complexes of compound 1 with 1C2B and 1P0I and Time-dependent number of hydrogen bonds plot for the 1–1C2B and 1–1P0I complexes ( e ). Figure 6. Open in a new tab Time-dependent Total Energy plot for the 1–1C2B and 1–1P0I complexes. The root mean square deviation (RMSD) of the protein backbone ( Figure 5 a) was used as a primary indicator of system equilibration. The 1–1C2B complex (blue) exhibited initial fluctuations up to 0.31 nm during the equilibration phase (0–10 ns), followed by stabilization with RMSD values in the range of 0.21–0.40 nm. All subsequent parameters were calculated based on the equilibrated trajectory after 10 ns. This behavior suggests an induced-fit mechanism, in which conformational adjustments of the enzyme accommodate ligand binding [ 46 ]. In contrast, the 1–1P0I complex (green) showed greater rigidity, maintaining a stable plateau between 0.07 and 0.21 nm throughout the simulation. Both systems remained below the commonly accepted stability threshold of 0.3 nm for protein–ligand complexes [ 47 ]. Local flexibility was evaluated using root mean square fluctuation (RMSF) analysis ( Figure 5 b). In both complexes, active-site residues displayed minimal fluctuations (<0.29 nm), indicating strong ligand engagement that restricts residue mobility. Higher fluctuation peaks were confined to loop regions (e.g., residues 70–80 and 280–290 in AChE), which are intrinsically flexible and located away from the binding pocket. This profile is consistent with stable inhibition, where the ligand acts as a “molecular glue” that stabilizes the catalytic domain [ 48 ]. To further assess structural integrity and compactness, Rg and SASA were analyzed simultaneously ( Figure 5 c,d). Both parameters remained stable, showing no significant increasing trends throughout the simulation. The Rg values averaged approximately 2.34 nm for 1–1C2B and 2.32 nm for 1–1P0I, confirming maintenance of compact tertiary structures without unfolding or expansion [ 49 ]. This observation is supported by the SASA profiles, which fluctuated around ~222 nm 2 and ~221 nm 2 for 1–1C2B and 1–1P0I, respectively. The consistent SASA values indicate that the hydrophobic cores remained shielded from solvent exposure, suggesting that ligand binding did not disrupt overall protein folding. Hydrogen-bond analysis ( Figure 5 e) provided additional insight into the stability mechanisms. The 1–1C2B complex (blue) maintained a dense interaction network, with an average of 4.29 hydrogen bonds over the 100 ns trajectory. These directional polar interactions may explain the relatively higher RMSD values, reflecting adaptive backbone motions that optimize bonding interactions. In contrast, the 1–1P0I complex (green) formed fewer hydrogen bonds (average of 3.72); however, its exceptionally low RMSD (0.07–0.21 nm) suggests that stability is primarily governed by hydrophobic packing and steric complementarity (lock-and-key fit) rather than electrostatic interactions alone [ 50 ]. Finally, thermodynamic stability was confirmed by total energy trajectories ( Figure 6 ). The energy profiles remained stable throughout the simulation, oscillating around −593,136 kJ/mol and −589,856 kJ/mol for 1–1C2B and 1–1P0I, respectively. The absence of significant energy drift indicates that both systems reached equilibrium and that ligand–protein interactions were energetically favorable over time. 2.4.4. In Silico Pharmacokinetic and Toxicity Profiling (ADMET) Evaluation of physicochemical properties and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters is essential for predicting drug-likeness and minimizing late-stage failure in drug discovery [ 51 ]. Compounds 1 and 2 , together with the reference berberine chloride ( 3 ), were assessed using SwissADME [ 19 ] and pkCSM [ 51 ]. Lipinski’s Rule of Five (Ro5) analysis ( Table 6 ) shows that the reference compound ( 3 ) fully satisfies Ro5 criteria, while compound 2 exhibits acceptable drug-likeness with only one violation and favorable lipophilicity (LogP = 1.55). In contrast, compound 1 presents four violations, mainly due to its higher molecular weight (578 Da) and elevated TPSA (220.76 Å 2 ), which may reduce passive oral absorption. However, such deviations are often associated with structurally complex high-affinity ligands, and formulation strategies may help mitigate pharmacokinetic limitations [ 52 , 53 ]. Table 6. Physicochemical properties and drug-likeness analysis based on Lipinski’s Rule of Five. Parameter 1 2 3 Ideal Limit [ 49 ] MW (g/mol) 578.00 290.00 337.0 <500 Da H-bond Donors 10 5 0 <5 H-bond Acceptors 12 6 4 <10 LogP 3.00 1.55 2.73 <5 Molar Refractivity 143.39 72.62 93.03 Between 40–130 Rotatable Bonds 3 1 2 <10 TPSA (Å 2 ) 220.76 110.38 40.80 <90 Lipinski Violations 4 1 0 0 Open in a new tab ADMET predictions ( Table 7 ) indicate clear differences in safety profiles. Compound 1 demonstrates the most favorable safety characteristics, being predicted as non-mutagenic and non-hepatotoxic, whereas compound 2 shows potential mutagenicity and the reference compound is predicted to exhibit both mutagenic and hepatotoxic risks. Considering that toxicity is a major cause of drug attrition, this profile highlights compound 1 as a safer candidate [ 54 ]. Table 7. In silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling. Category Parameter 1 2 3 Unit Absorption Intestinal Absorption 49.768 72.619 79.298 % Absorbed Distribution BBB Permeability −2.092 −1.069 0.241 log BB CNS Permeability −4.231 −3.403 −1.529 log PS Metabolism CYP3A4 Inhibitor No No Yes Yes/No CYP2D6 Inhibitor No No No Yes/No CYP1A2 Inhibitor No No No Yes/No Excretion Total Clearance 0.228 0.29 1.618 log mL/min/kg Renal OCT2 Substrate No No No Yes/No Toxicity AMES Toxicity No Yes Yes Yes/No Hepatotoxicity No No Yes Yes/No hERG II Inhibitor No No No Yes/No Open in a new tab In terms of metabolism, compound 1 does not inhibit key CYP enzymes, including CYP3A4, suggesting lower risk of drug–drug interactions compared with the reference compound [ 39 ]. Moreover, its low BBB permeability (logBB = −2.09) implies limited CNS exposure and potentially reduced neurological side effects. Despite deviations from Ro5 that may influence oral absorption, the superior safety profile and metabolic stability of compound 1 identify it as the most promising lead scaffold. Future optimization may focus on nanocarrier-based formulations to enhance bioavailability while maintaining its safety advantages [ 40 ]. 3. Materials and Methods 3.1. In Silico Evaluation of Cholinesterase Terminalia triptera Stapf. was collected from Yok Don National Park, Dak Lak Province, Vietnam. The species was identified by Thi Huong Tran, a botanist at Tay Nguyen University. A voucher specimen (Code/Registration No.: Nguyen04052O24.IBE.TNU) has been deposited at the Institute of Biotechnology and Environment, Tay Nguyen University, Dak Lak 630000, Vietnam. After collection, the plant material was air-dried, stored in polyethylene (PE) bags, and maintained at −30 °C prior to further analysis. Acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), and berberine chloride were obtained from Sigma-Aldrich (St. Louis, MO, USA). All solvents, reagents, and supplementary chemicals used throughout the experiments were of analytical grade to ensure reliability and reproducibility of the results. 3.2. Cholinesterase Inhibitory Assay Cholinesterase inhibition (AChE and BChE) was evaluated using a modified Ellman’s colorimetric method [ 55 ]. The reaction mixture consisted of enzyme solution (40 µL), phosphate buffer (80 µL), and test sample or reference inhibitor (berberine chloride, 40 µL) at different concentrations, followed by pre-incubation at room temperature for 15 min. Subsequently, substrate solution (40 µL) and DTNB reagent (5,5′-dithiobis(2-nitrobenzoic acid), 0.005 M in distilled water, 40 µL) were added, and the reaction was further incubated for 15 min. Absorbance was measured at 412 nm using an iMark™ Microplate Absorbance Reader (Bio-Rad Laboratories, Hercules, CA, USA). Blank controls were prepared under identical conditions, replacing the sample with buffer. Acetylcholinesterase (0.77 U/mL, electric eel) with acetylthiocholine iodide (0.003 M) was prepared in 0.05 M phosphate buffer (pH 8.0), while butyrylcholinesterase (0.13 U/mL, equine serum) with butyrylthiocholine iodide (0.003 M) was prepared in 0.05 M phosphate buffer (pH 7.5). PG was dissolved in dimethyl sulfoxide, and berberine chloride was prepared in methanol. The inhibition percentage was calculated as [(Abs_control − Abs_sample)/Abs_control] × 100. IC 50 values were determined from dose–response curves, where lower IC 50 values indicate higher inhibitory potency. 3.3. Preparation of Medicinal Plant Extract, Extraction, Purification of Active Compounds Bioassay-guided fractionation based on cholinesterase inhibitory activity was employed to isolate active constituents from the methanolic trunk bark extract of Terminalia triptera . The crude extract (TT, 27.75 g) was subjected to Diaion HP-20 column chromatography and eluted with a stepwise MeOH/H 2 O gradient (15%, 30%, 45%, 60%, and 100% MeOH), affording five fractions: TT-1 (10.05 g), TT-2 (3.95 g), TT-3 (8.1 g), TT-4 (3.85 g), and TT-5 (1.45 g). Among these, fraction TT-4 showed the strongest inhibitory activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) and was therefore selected for further purification. Fraction TT-4 was subsequently separated using Sephadex LH-20 column chromatography with MeOH/H 2 O to yield three sub-fractions (TT-4.1–TT-4.3). The most active sub-fraction, TT-4.2 (0.45 g), was further purified by preparative HPLC (35% MeOH), resulting in the isolation of two pure compounds, Compd 1 (62.85 mg) and Compd 2 (3.3 mg). These compounds’ chemical structures were identified using NMR, HESI-MS, HPLC analysis and comparison with previous reports. 3.4. Computation Molecular Docking: The crystal structures of AChE (PDB ID: 1C2B) and BChE (PDB ID: 1P0I) were retrieved from the RCSB Protein Data Bank. The docking process was executed using AutoDock Vina version 1.2.5 [ 36 ] integrated within UCSF Chimera 1.18 [ 56 ]. For each protein, a specific grid box was defined to cover the active site residues as follows: For AChE (1C2B) The grid center was set at (x = 33.2163, y = 84.3, z = 28.2875) with a size of (x = 29.957, y = 30, z = 31.7651) Å [ 57 ]. For BChE (1P0I) The grid center was set at (x = 131.906, y = 113.09, z = 40.3581) with a size of (x = 40, y = 24.397, z = 31.7617) Å [ 58 ]. The best-scored binding poses were selected for further analysis. The 2D ligand-protein interaction diagrams were generated using LigPlot+ v2.3 [ 59 ] to visualize hydrogen bonding and hydrophobic contacts. DFT Calculations: The ground-state geometries of all compounds were optimized using Gaussian 09 [ 60 ] with Density Functional Theory (DFT) at the M06-2X level [ 61 ] and the 6-311++G(d,p) basis set. The M06-2X functional was chosen for its reliable description of non-covalent interactions and thermochemical properties. Harmonic frequency analyses at the same level confirmed true minima (no imaginary frequencies) and provided thermodynamic parameters. Molecular Dynamics Simulations: Molecular dynamics (MD) simulations were performed using GROMACS 2025.02 [ 62 ] to evaluate ligand–protein stability. The CHARMM36 force field [ 63 ] was applied for protein topology, while ligand parameters were generated compatible with CHARMM36 (e.g., via CGenFF). Systems were solvated in a cubic TIP3P water box and neutralized with 0.15 M NaCl. After energy minimization (steepest descent), equilibration was conducted under NVT and NPT ensembles (100 ps each). Production simulations ran for 100 ns at 300 K and 1 bar. Trajectories were analyzed using RMSD and RMSF via GROMACS tools. ADMET and Toxicity Prediction: The physicochemical and pharmacokinetic properties (Absorption, Distribution, Metabolism, and Excretion; ADME) were predicted using the SwissADME web server [ 19 ]. Drug-likeness was evaluated according to Lipinski’s Rule of Five, while toxicity profiles were assessed using the pkCSM database [ 51 ]. 4. Conclusions This study provides the first comprehensive evidence of the cholinesterase inhibitory potential of Terminalia triptera Stapf., emphasizing its value as a source of bioactive metabolites for Alzheimer’s disease research. Bioassay-guided isolation led to the identification of two flavan-3-ol derivatives, epicatechin-(4β→8)- ent -catechin ( 1 ) and (−)-catechin ( 2 ), reported for the first time from this species. The dimeric compound 1 demonstrated notable dual inhibition against AChE and BChE, surpassing berberine chloride. DFT analysis indicated high kinetic stability and low electrophilicity, suggesting a favorable safety profile. Molecular docking and 100 ns molecular dynamics simulations further confirmed stable binding within enzyme catalytic sites. Overall, these results highlight T. triptera dimeric proanthocyanidins as promising multi-target anti-Alzheimer candidates, supporting further pharmacological evaluation, including in vivo studies and clinical investigations. Abbreviations The following abbreviations are used in this manuscript: AD Alzheimer’s Disease AChE Acetylcholinesterase BChE/BuChE Butyrylcholinesterase ChEIs Cholinesterase Inhibitors Aβ Amyloid-beta DFT Density Functional Theory HOMO Highest Occupied Molecular Orbital LUMO Lowest Unoccupied Molecular Orbital MD Molecular Dynamics ADL Activities of Daily Living DALYs Disability-Adjusted Life Years CAS Catalytic Anionic Site PAS Peripheral Anionic Site IC 50 Half-maximal Inhibitory Concentration NMR Nuclear Magnetic Resonance MS Mass Spectrometry HESI-MS Heated Electrospray Ionization Mass Spectrometry HPLC High-Performance Liquid Chromatography PDB Protein Data Bank RMSD Root Mean Square Deviation RMSF Root Mean Square Fluctuation Rg Radius of Gyration SASA Solvent-Accessible Surface Area FMO Frontier Molecular Orbitals TTS Terminalia Triptera Stem Bark Extract TT Terminalia Triptera Extract Open in a new tab Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules31071113/s1 , Figure S1: High-resolution ESI-MS spectrum of compound 1 (epicatechin-(4β→8)- ent -catechin) recorded in negative ion mode; Figure S2: 1 H NMR (600 MHz, pyridine-d5) spectrum of compound 1 (epicatechin-(4β→8)- ent -catechin); Figure S3: 13C NMR (150 MHz, MeOH- d4) spectrum of compound 1 (epicatechin-(4β→8)- ent -catechin); Figure S4: High-resolution ESI-MS spectrum of compound 2 [(−)-catechin] recorded in negative ion mode; Figure S5: 1 H NMR (500 MHz, MeOH-d4) spectrum of compound 2 [(−)-catechin]; Figure S6: 13C NMR (125 MHz, MeOH- d4) spectrum of compound 2 [(−)-catechin]; Figure S7: RMSD vs. time (a), RMSF of amino acid residues (b), and time-dependent radius of gyration (Rg) (c) for the complexes of compound 1 with 1C2B from three independent MD simulations (replicates); Figure S8: Solvent-accessible surface area (SASA) (a), number of hydrogen bonds (b), and total energy (c) for the complexes of compound 1 with 1C2B from three independent MD simulations (replicates); Figure S9: RMSD vs. time (a), RMSF of amino acid residues (b), and time-dependent radius of gyration (Rg) (c) for the complexes of compound 1 with 1-1P0I from three independent MD simulations (replicates); Figure S10: Solvent-accessible surface area (SASA) (a), number of hydrogen bonds (b), and total energy (c) for the complexes of compound 1 with 1-1P0I from three independent MD simulations (replicates); Table S1: Bio-guided Iolation of Cholinesterase Inhibitors from Terminalia triptera Stapf. molecules-31-01113-s001.zip (2.2MB, zip) Author Contributions Conceptualization, methodology, V.B.N. and S.-L.W.; software, T.Q.P.; validation, S.-L.W. and H.T.H.; formal analysis, V.B.N., M.D.D., T.H.T.P., T.K.T.P., T.Q.P., D.S.N., B.P.T., and S.-L.W.; investigation, T.Q.P., H.T.H.; resources, V.B.N.; data curation, V.B.N. and S.-L.W.; writing—original draft preparation, V.B.N. and T.Q.P.; writing—review and editing, visualization, supervision, V.B.N. and S.-L.W.; project administration, V.B.N. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement All relevant data are contained within the paper. The data analyzed in this study are available from the corresponding authors upon reasonable request. Conflicts of Interest The authors declare no conflicts of interest. Funding Statement This research was funded by the grant from Tay Nguyen University, Vietnam, grant number T2025-46CBTĐ. Footnotes Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. References 1. World Health Organization Dementia: Key Facts. [(accessed on 10 February 2026)]. Available online: https://www.who.int/news-room/fact-sheets/detail/dementia . 2. Alzheimer’s Disease International . World Alzheimer Report 2022: Life After Diagnosis: Navigating Treatment, Care and Support. Alzheimer’s Disease International; London, UK: 2022. [(accessed on 5 January 2026)]. Available online: https://www.alzint.org/resource/world-alzheimer-report-2022/ [ Google Scholar ] 3. Wimo A., Seeher K., Cataldi R., Cyhlarova E., Dielemann J.L., Frisell O., Guerchet M., Jönsson L., Malaha A.K., Nichols E., et al. The worldwide costs of dementia in 2019. Alzheimer’s Dement. 2023;19:2865–2873. doi: 10.1002/alz.12901. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Brodaty H., Donkin M. Family caregivers of people with dementia. Dialogues Clin. Neurosci. 2009;11:217–228. doi: 10.31887/DCNS.2009.11.2/hbrodaty. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Long J.M., Holtzman D.M. Alzheimer disease: An update on pathobiology and treatment strategies. Cell. 2019;179:312–339. doi: 10.1016/j.cell.2019.09.001. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. DeTure M.A., Dickson D.W. The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegener. 2019;14:32. doi: 10.1186/s13024-019-0333-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Hampel H., Mesulam M.M., Cuello A.C., Farlow M.R., Giacobini E., Grossberg G.T., Khachaturian A.S., Vergallo A., Cavedo E., Snyder P.J., et al. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain. 2018;141:1917–1933. doi: 10.1093/brain/awy132. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Ferreira-Vieira T.H., Guimaraes I.M., Silva F.R., Ribeiro F.M. Alzheimer’s disease: Targeting the cholinergic system. Curr. Neuropharmacol. 2016;14:101–115. doi: 10.2174/1570159X13666150716165726. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Colović M.B., Krstić D.Z., Lazarević-Pašti T.D., Bondžić A.M., Vasić V.M. Acetylcholinesterase inhibitors: Pharmacology and toxicology. Curr. Neuropharmacol. 2013;11:315–335. doi: 10.2174/1570159X11311030006. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Anand P., Singh B. A review on cholinesterase inhibitors for Alzheimer’s disease. Arch. Pharmacal Res. 2013;36:375–399. doi: 10.1007/s12272-013-0036-3. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Howes M.J., Perry N.S., Houghton P.J. Plants with traditional uses and activities, relevant to the management of Alzheimer’s disease and other cognitive disorders. Phytother. Res. 2003;17:1–18. doi: 10.1002/ptr.1280. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Orhan I.E. Nature: A substantial source of auspicious substances with acetylcholinesterase inhibitory action. Curr. Neuropharmacol. 2013;11:379–387. doi: 10.2174/1570159X11311040003. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. ALNasser M.N., Alboraiy G.M., Alsowig E.M., Alqattan F.M. Cholinesterase inhibitors from plants and their potential in Alzheimer’s treatment: Systematic review. Brain Sci. 2025;15:215. doi: 10.3390/brainsci15020215. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Zhang X.R., Kaunda J.S., Zhu H.T., Wang D., Yang C.R., Zhang Y.J. The genus Terminalia (Combretaceae): An ethnopharmacological, phytochemical and pharmacological review. Nat. Prod. Bioprospect. 2019;9:357–392. doi: 10.1007/s13659-019-00222-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Cock I.E. The medicinal properties and phytochemistry of plants of the genus Terminalia (Combretaceae) Inflammopharmacology. 2015;23:203–229. doi: 10.1007/s10787-015-0246-z. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Sliwoski G., Kothiwale S., Meiler J., Lowe E.W., Jr. Computational Methods in Drug Discovery. Pharmacol. Rev. 2014;66:334–395. doi: 10.1124/pr.112.007336. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Lionta E., Spyrou G., Vassilatis D.K., Cournia Z. Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Curr. Top. Med. Chem. 2014;14:1923–1938. doi: 10.2174/1568026614666140929124445. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Hollingsworth S.A., Dror R.O. Molecular Dynamics Simulation for All. Neuron. 2018;99:1129–1143. doi: 10.1016/j.neuron.2018.08.011. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Daina A., Michielin O., Zoete V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Sci. Rep. 2017;7:42717. doi: 10.1038/srep42717. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Phan T.Q., Wang S.-L., Vu T.T.L., Do T.L., Pham P.T.T., Phan T.K.P., Phan T.K.T., Nguyen T.H., Nguyen A.D., Nguyen V.B. Inside into diverse pharmaceutical properties and phytochemical profiles of Tetrastigma erubescens: A drug discovery via experiments, docking, DFT, ADMET and Lipinski’s rules of five performance. Res. Chem. Intermed. 2025;51:3351–3381. doi: 10.1007/s11164-025-05591-8. [ DOI ] [ Google Scholar ] 21. Phan T.K.P., Wang S.-L., Nguyen Q.V., Phan T.Q., Nguyen T.T., Tran T.T.T., Nguyen A.D., Nguyen V.B., Doan M.D. Assessment of the Chemical Profile and Potential Medical Effects of a Flavonoid-Rich Extract of Eclipta prostrata L. Collected in the Central Highlands of Vietnam. Pharmaceuticals. 2023;16:1476. doi: 10.3390/ph16101476. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Doan M.D., Wang S.-L., Nguyen V.B., Phan T.K.P., Phan T.Q., Nguyen T.T., Nguyen T.H., Nguyen Q.V., Nguyen A.D. Phytochemical profiles and novel biofunctions of Dillenia ovata Wall. ex Hook.f. et Thomson: A Vietnamese indigenous medicinal plant. Res. Chem. Intermed. 2023;49:5567–5593. doi: 10.1007/s11164-023-05126-z. [ DOI ] [ Google Scholar ] 23. Thang P.N.T., Tran V.-H., Vu T.A., Vinh N.N., Huynh D.T.M., Pham D.T. Determination of Antioxidant, Cytotoxicity, and Acetylcholinesterase Inhibitory Activities of Alkaloids Isolated from Sophora flavescens Ait. Grown in Dak Nong, Vietnam. Pharmaceuticals. 2022;15:1384. doi: 10.3390/ph15111384. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Khan I., Ullah Z., Shad A.A., Fahim M., Öztürk M. In vitro antioxidant, anticholinesterase inhibitory, and antimicrobial activity studies of Terminalia chebula (Retz) and Terminalia arjuna (Roxb) S. Afr. J. Bot. 2022;146:395–400. doi: 10.1016/j.sajb.2021.11.016. [ DOI ] [ Google Scholar ] 25. Li Y.J., Liang C.C., Jin L., Chen J. Inhibition mechanisms of four ellagitannins from Terminalia chebula fruits on acetylcholinesterase by inhibition kinetics, spectroscopy and molecular docking analyses. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2023;302:123115. doi: 10.1016/j.saa.2023.123115. [ DOI ] [ PubMed ] [ Google Scholar ] 26. de Torre M.P., Cavero R.Y., Calvo M.I. Anticholinesterase Activity of Selected Medicinal Plants from Navarra Region of Spain and a Detailed Phytochemical Investigation of Origanum vulgare L. ssp. vulgare. Molecules. 2022;27:7100. doi: 10.3390/molecules27207100. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Phansalkar R.S., Nam J.-W., Leme-Kraus A.A., Gan L.-S., Zhou B., McAlpine J.B., Chen S.-N., Bedran-Russo A.K., Pauli G.F. Proanthocyanidin Dimers and Trimers from Vitis vinifera Provide Diverse Structural Motifs for the Evaluation of Dentin Biomodification. J. Nat. Prod. 2019;82:2387–2399. doi: 10.1021/acs.jnatprod.8b00953. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Hori K., Wada M., Yahara S., Watanabe T., Devkota H.P. Antioxidant phenolic compounds from the rhizomes of Astilbe rivularis. Nat. Prod. Res. 2018;32:453–456. doi: 10.1080/14786419.2017.1309536. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Swain R.K., Tripathy S.N., Rana R., Patro S.K., Behera S. A study on phytochemical and pharmacological activity of Terminalia arjuna: A review. Res. J. Pharmacogn. Phytochem. 2023;15:111–117. doi: 10.52711/0975-4385.2023.00017. [ DOI ] [ Google Scholar ] 30. Pfundstein B., El Desouky S.K., Hull W.E., Haubner R., Erben G., Owen R.W. Polyphenolic compounds in the fruits of Egyptian medicinal plants (Terminalia bellerica, Terminalia chebula and Terminalia horrida): Characterization, quantitation and determination of antioxidant capacities. Phytochemistry. 2010;71:1132–1148. doi: 10.1016/j.phytochem.2010.03.018. [ DOI ] [ PubMed ] [ Google Scholar ] 31. Pellati F., Bruni R., Righi D., Grandini A., Tognolini M., Prencipe F.P., Poli F., Benvenuti S., Del Rio D., Rossi D. Metabolite Profiling of Polyphenols in Terminalia spp. J. Ethnopharmacol. 2013;147:277–285. doi: 10.1016/j.jep.2013.02.025. [ DOI ] [ PubMed ] [ Google Scholar ] 32. Okello E.J., Mather J. Comparative Kinetics of Acetyl- and Butyryl-Cholinesterase Inhibition by Green Tea Catechins|Relevance to the Symptomatic Treatment of Alzheimer’s Disease. Nutrients. 2020;12:1090. doi: 10.3390/nu12041090. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Cichon N., Grabowska W., Gorniak L., Stela M., Harmata P., Ceremuga M., Bijak M. Mechanistic and Therapeutic Insights into Flavonoid-Based Inhibition of Acetylcholinesterase: Implications for Neurodegenerative Diseases. Nutrients. 2025;17:78. doi: 10.3390/nu17010078. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. McGovern S.L., Helfand B.T., Feng B., Shoichet B.K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 2003;46:4265–4272. doi: 10.1021/jm030266r. [ DOI ] [ PubMed ] [ Google Scholar ] 35. Chandra Babu T.M., Rajesh S.S., Bhaskar B.V., Devi S., Rammohan A., Sivaraman T., Rajendra W. Molecular Docking, Molecular Dynamics Simulation, Biological Evaluation and 2D QSAR Analysis of Flavonoids from Syzygium Alternifolium as Potent Anti-Helicobacter Pylori Agents. RSC Adv. 2017;7:18277–18292. doi: 10.1039/C6RA27872H. [ DOI ] [ Google Scholar ] 36. Trott O., Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010;31:455–461. doi: 10.1002/jcc.21334. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Sussman J.L., Harel M., Frolow F., Oefner C., Goldman A., Toker L., Silman I. Atomic structure of acetylcholinesterase from Torpedo californica: A prototypic acetylcholine-binding protein. Science. 1991;253:872–879. doi: 10.1126/science.1678899. [ DOI ] [ PubMed ] [ Google Scholar ] 38. Nicolet Y., Lockridge O., Masson P., Fontecilla-Camps J.C., Nachon F. Crystal structure of human butyrylcholinesterase and of its complexes with substrate and products. J. Biol. Chem. 2003;278:41141–41147. doi: 10.1074/jbc.M210241200. [ DOI ] [ PubMed ] [ Google Scholar ] 39. Wienkers L.C., Heath T.G. Predicting in vivo drug interactions from in vitro drug discovery data. Nat. Rev. Drug Discov. 2005;4:825–833. doi: 10.1038/nrd1851. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Patra J.K., Das G., Fraceto L.F., Campos E.V.R., Rodriguez-Torres M.D.P., Acosta-Torres L.S., Diaz-Torres L.A., Grillo R., Swamy M.K., Sharma S., et al. Nano based drug delivery systems: Recent developments and future prospects. J. Nanobiotechnol. 2018;16:71. doi: 10.1186/s12951-018-0392-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Koopmans T. Über die Zuordnung von Wellenfunktionen und Eigenwerten zu den einzelnen Elektronen eines Atoms. Physica. 1934;1:104–113. doi: 10.1016/S0031-8914(34)90011-2. [ DOI ] [ Google Scholar ] 42. Parr R.G., Pearson R.G. Absolute hardness: Companion parameter to absolute electronegativity. J. Am. Chem. Soc. 1983;105:7512–7516. doi: 10.1021/ja00364a005. [ DOI ] [ Google Scholar ] 43. Parr R.G., Szentpály L.V., Liu S. Electrophilicity index. J. Am. Chem. Soc. 1999;121:1922–1924. doi: 10.1021/ja983494x. [ DOI ] [ Google Scholar ] 44. Chattaraj P.K., Maiti B. HSAB Principle Applied to the Time Evolution of Chemical Reactions. J. Am. Chem. Soc. 2003;125:2705–2710. doi: 10.1021/ja0276063. [ DOI ] [ PubMed ] [ Google Scholar ] 45. Vu T.T.L., Wang S.-L., Luc Q.T., Do T.L., Dam T.B.H., Phan T.K.P., Nguyen T.T., Tran P.H., Nguyen A.D., Nguyen V.B. New Record of Potential Tyrosinase Inhibitory, Antioxidant, and Photoprotective Effects of Vietnamese Castanea mollissima Extracts. Chem. Biodivers. 2025;22:e202500994. doi: 10.1002/cbdv.202500994. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Koshland D.E. Application of a Theory of Enzyme Specificity to Protein Synthesis. Proc. Natl. Acad. Sci. USA. 1958;44:98–104. doi: 10.1073/pnas.44.2.98. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Kuzmanic A., Zagrovic B. Determination of Ensemble-Average Pairwise Root Mean Square Deviation from Experimental Serial Crystallography Data. Biophys. J. 2010;98:861–871. doi: 10.1016/j.bpj.2009.11.011. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Salo-Ahen O.M.H., Alanko I., Bhadane R., Bonvin A.M.J.J., Honorato R.V., Hossain S., Juffer A.H., Kabedev A., Lahtela-Kakkonen M., Larsen A.S., et al. Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes. 2021;9:71. doi: 10.3390/pr9010071. [ DOI ] [ Google Scholar ] 49. Lobanov M.I., Bogatyreva N.S., Galzitskaia O.V. Radius of Gyration as an Indicator of Protein Structure Compactness. Mol. Biol. 2008;42:623–628. doi: 10.1134/S0026893308040195. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Patil R., Das S., Stanley A., Yadav L., Sudhakar A., Varma A.K. Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target–Ligand Interface Leads the Pathways of Drug Designing. PLoS ONE. 2010;5:e12029. doi: 10.1371/journal.pone.0012029. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Pires D.E., Blundell T.L., Ascher D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 2015;58:4066–4072. doi: 10.1021/acs.jmedchem.5b00104. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Veber D.F., Johnson S.R., Cheng H.-Y., Smith B.R., Ward K.W., Kopple K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002;45:2615–2623. doi: 10.1021/jm020017n. [ DOI ] [ PubMed ] [ Google Scholar ] 53. Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2001;46:3–26. doi: 10.1016/S0169-409X(00)00129-0. [ DOI ] [ PubMed ] [ Google Scholar ] 54. Enoch S.J., Cronin M.T.D. A review of the electrophilic reaction chemistry involved in covalent DNA binding. Crit. Rev. Toxicol. 2010;40:728–748. doi: 10.3109/10408444.2010.494175. [ DOI ] [ PubMed ] [ Google Scholar ] 55. Ellman G.L., Courtney K.D., Andres V., Featherstone R.M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 1961;7:88–95. doi: 10.1016/0006-2952(61)90145-9. [ DOI ] [ PubMed ] [ Google Scholar ] 56. Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004;25:1605–1612. doi: 10.1002/jcc.20084. [ DOI ] [ PubMed ] [ Google Scholar ] 57. Duong T.-L.T., Liu T.-W., Huynh Q.-D.T., Nguyen D.-K., Wang Y.-H., Chu M.-H., Vo T.-H., Hsu S.-J., Lee C.-K. Rapid Identification of Natural Acetylcholinesterase Inhibitors from Glycosmis parviflora Stem Utilizing Dereplication, In Vitro and In Silico Approach. Arab. J. Chem. 2024;17:105811. doi: 10.1016/j.arabjc.2024.105811. [ DOI ] [ Google Scholar ] 58. Artunç T., Çetinkaya Y., Taslimi P., Menzek A. Investigation of Cholinesterase and α-Glucosidase Enzyme Activities, and Molecular Docking and DFT Studies for 1,2-Disubstituted Cyclopentane Derivatives with Phenyl and Benzyl Units. Mol. Divers. 2025;29:1305–1321. doi: 10.1007/s11030-024-10911-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. Laskowski R.A., Swindells M.B. LigPlot+: Multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011;51:2778–2786. doi: 10.1021/ci200227u. [ DOI ] [ PubMed ] [ Google Scholar ] 60. Frisch M.J., Trucks G.W., Schlegel H.B., Scuseria G.E., Robb M.A., Cheeseman J.R., Scalmani G., Barone V., Mennucci B., Petersson G.A., et al. Gaussian 09, Revision D.01. Gaussian, Inc.; Wallingford, CT, USA: 2009. [ Google Scholar ] 61. Zhao Y., Truhlar D.G. The M06 suite of density functionals for main-group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: Two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Acc. 2008;120:215–241. doi: 10.1007/s00214-007-0310-x. [ DOI ] [ Google Scholar ] 62. Abraham M.J., Murtola T., Schulz R., Páll S., Smith J.C., Hess B., Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1:19–25. doi: 10.1016/j.softx.2015.06.001. [ DOI ] [ Google Scholar ] 63. Best R.B., Zhu X., Shim J., Lopes P.E.M., Mittal J., Feig M., MacKerell A.D., Jr. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 2012;8:3257–3273. doi: 10.1021/ct300400x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials molecules-31-01113-s001.zip (2.2MB, zip) Data Availability Statement All relevant data are contained within the paper. The data analyzed in this study are available from the corresponding authors upon reasonable request. Articles from Molecules are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI) ACTIONS View on publisher site PDF (3.6 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top