AI-Based Virtual Screening of NLRP3 Inflammasome-Targeting Drug Candidates for Alzheimer’s Disease and Evaluation of Blood–Brain Barrier Permeability

Alzheimer’s disease (AD) is the leading cause of dementia in the elderly. Traditional drugs targeting Aβ and Tau proteins face development bottlenecks. In recent years, studies have shown that NLRP3 inflammasome-mediated neuroinflammation in the brain is a key mechanism in the pathogenesis of AD. However, the number of existing NLRP3-specific inhibitors is limited, and they generally suffer from insufficient blood-brain barrier (BBB) penetration. This study aims to combine artificial intelligence (AI) and machine learning technologies to construct a multi-task screening model that can simultaneously predict the inhibitory activity of small molecule NLRP3 and its ability to penetrate the blood-brain barrier, providing an efficient and low-cost computational screening technology solution for the development of novel anti-inflammatory drugs for Alzheimer's disease. It uses AlphaFold to obtain target protein structures and combines them with the ZINC20/PubChem chemical database to construct a compound dataset. Using Python as the core tool, a random forest multi-task machine learning model was built using the Google Colab cloud platform. Physicochemical parameters such as molecular weight, lipid solubility (LogP), and polar surface area (TPSA) were selected as input features for the model. The drug’s NLRP3 activity and BBB penetration were used as the core prediction labels for model training and cross-validation. The model demonstrated high predictive performance in 5-fold cross-validation, with an average accuracy of 68.89% for predicting NLRP3 activity and an average accuracy of 97.78% for predicting blood-brain barrier penetration. Feature importance analysis confirmed that LogP and TPSA are the most critical physicochemical features that determine whether candidate molecules can penetrate the blood-brain barrier. Several candidate small molecules with both NLRP3 inhibition potential and BBB penetration were screened from a large-scale candidate molecule library. This study successfully constructed a low-cost, easy-to-implement AI-assisted drug screening system for neurological diseases, which can provide data support for the development of lead compounds for novel anti-inflammatory targeted drugs for Alzheimer's disease.