Investigating the anti-obesity potential of Nelumbo nucifera leaf bioactive compounds through machine learning and computational biology methods

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric disorders. Conventional pharmacological treatments often have significant side effects, highlighting the need for safer alternatives. Traditional Chinese Medicine (TCM) offers potential solutions, with plant extracts like those from <i>Nelumbo nucifera</i> leaves showing promise due to their historical use and minimal side effects. This study employs a comprehensive computational biology approach to explore the anti-obesity effects of <i>Nelumbo nucifera</i> Leaf Bioactive Compounds. Sixteen active compounds from <i>Nelumbo nucifera</i> leaves were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Clustering analysis identified three representative molecules, and network pharmacology pinpointed PPARG as a common target gene. Molecular docking and machine learning models were used for inhibitors screening, and molecular dynamics simulations were futher used to investigate the inhibitory effects and mechanisms of these molecules on PPARG. Subsequent cellular assays confirmed the ability of Sitogluside to reduce lipid accumulation and triglyceride levels in 3T3-L1 cells, underscoring its potential as an effective and safer obesity treatment. Our findings provide a molecular basis for the anti-obesity properties of <i>Nelumbo nucifera</i> Leaf Bioactive Compounds and pave the way for developing new, effective, and safer obesity treatments.