2026-10-14T16:07:55Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33835904 2026-09-16T09:07:22Z category_4 category_15 category_19 category_873 category_734 category_931 portal_63 item_type_3 month_year_09_2026

Machine Learning-Guided Discovery of Natural BACE1 Inhibitors with Enzymatic and Cellular Validation Yiming Bai (3839470) Jun Zhao (59250) Shurong Zhang (11009222) Caiqin Yan (19979521) Yiheng Chen (3079203) Guanhua Du (620091) Ailin Liu (758014) Biochemistry Neuroscience Pharmacology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified potential lead compound natural product libraries molecular docking analysis important therapeutic target hek293t cells demonstrated evaluate predictive performance consensus prediction criteria assisted virtual screening amyloidogenic processing pathway nine machine learning integrating machine learning reducing aβ production amyloid precursor protein potential bace1 inhibitors natural bace1 inhibitors 3 ′- digallate based predictive models 50 </ sub machine learning bace1 inhibitors optimized models bace1 ), widely recognized study demonstrates secretase 1 recursive partitioning present study multiple hydrogen limiting enzyme licoagrochalcone b initial concentration hydrophobic interactions guided discovery future anti external validations excessive accumulation effective strategy chicoric acid cellular activities app ), alzheimer ’ 50 μm 50 μg 171 compounds Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by excessive accumulation of β-amyloid (Aβ) peptides in the brain. Beta-secretase 1 (BACE1), the rate-limiting enzyme in the amyloidogenic processing pathway of amyloid precursor protein (APP), has been widely recognized as an important therapeutic target for reducing Aβ production. In the present study, nine machine learning-based predictive models for BACE1 inhibitors were constructed using Naïve Bayes (NB) and Recursive Partitioning (RP) algorithms. Subsequently, internal and external validations were conducted to evaluate predictive performance. The optimized models were subsequently applied to screen an in-house natural product library containing 452 compounds. Based on consensus prediction criteria, 171 compounds were identified as potential BACE1 inhibitors and subjected to fluorescence resonance energy transfer (FRET)-based enzymatic assays. Among them, 33 compounds exhibited inhibitory rates greater than 50% at an initial concentration of 50 μg/mL. Further dose–response evaluation identified five compounds with IC₅₀ values below 50 μM, including Theaflavin-3,3′-digallate, γ-mangostin, Licoagrochalcone B, Chicoric acid, and Resveratrol. Cell-based validation using APPswe-HEK293T cells demonstrated that Theaflavin-3,3′-digallate significantly reduced extracellular Aβ_1–42 secretion. Molecular docking analysis further revealed that Theaflavin-3,3′-digallate could stably bind to BACE1 through multiple hydrogen-bond and hydrophobic interactions. Collectively, this study demonstrates that integrating machine learning-assisted virtual screening with natural product libraries is an effective strategy for discovering novel BACE1 inhibitors. Theaflavin-3,3′-digallate was identified as a promising natural BACE1 inhibitor with both enzymatic and cellular activities. This research provides new technology and potential lead compound for the development of future anti-Alzheimer’s disease drug. 2026-09-16T00:00:00Z Dataset Dataset 10.1021/acsomega.6c07963.s001 https://figshare.com/articles/dataset/Machine_Learning-Guided_Discovery_of_Natural_BACE1_Inhibitors_with_Enzymatic_and_Cellular_Validation/33835904 CC BY-NC 4.0