AI-based detection of phytochemicals regulating cholesterol level

High cholesterol levels are a significant risk factor for cardiovascular diseases and managing them is crucial for maintaining overall health. Our lifestyle management is contributing to the increased risk of cholesterol. Pharmaceutically various synthetic compounds are available to manage the cholesterol level, but they have certain side effects. Hence, naturally available phytochemicals have always been a choice to replace the synthetic compounds. Artificial Intelligence (AI) proves valuable in screening for cholesterol-lowering phytochemicals, leveraging machine learning algorithms and big data to identify bioactive compounds that may reduce cholesterol levels. This study focuses on in silico screening of bioactive phytochemicals to identify potential candidates for modulating cholesterol metabolism in the liver. For this study, published literature, patents, and databases were surveyed to identify promising hepatoregulatory phytochemicals. Around 46 phytochemicals were selected and queried for Pa and Pi (Pharmaceutically active and Inactive) value in way2drug (https://www.way2drug.com/passonline/) online server for hepatoprotective property, cholesterol synthesis inhibitor, cholesterol absorption inhibitor and cholesterol antagonist. The phytochemicals showing the best activity were analysed for their interaction with the FXR (Farensoid X Receptor), which regulated cholesterol metabolism. Structure-Activity Relationship (SAR) Modelling were done using HADDOCK docking server (https://rascar.science.uu.nl/haddock2.4/) to analyze the interaction between phytochemical and FXR. The phytochemicals favoured thermodynamically were analyzed for ADME (Absorption, Distribution, Metabolism, and Excretion) properties using SwissADME tool (http://www.swissadme.ch/). Quercitin was found as a promising phytochemical to regulate cholesterol metabolism and could be a replacement for chemically synthesized pharmaceuticals.