Network Pharmacology and Machine Learning Identify Flavonoids as Potential Senotherapeutics

Background/Objectives: Cellular senescence is characterised by irreversible cell cycle arrest and the secretion of a proinflammatory phenotype. In recent years, senescent cell accumulation and senescence-associated secretory phenotype (SASP) secretion have been linked to the onset of chronic degenerative diseases associated with ageing. In this context, the senotherapeutic compounds have emerged as promising drugs that specifically eliminate senescent cells (senolytics) or diminish the damage caused by SASP (senomorphics). On the other hand, computational approaches, such as network pharmacology and machine learning, have revolutionised the identification of novel drugs. These tools enable the analysis of large volumes of compounds and the optimisation of the search for the most promising ones as potential drugs. Therefore, we employed such approaches in the present study to identify potential senotherapeutic compounds. Methods: First, we constructed drug-protein interaction networks related to cellular senescence. Then, using three machine learning models (Random Forest, Support Vector Machine, and K-Nearest Neighbours), we classified these compounds based on their therapeutic potential against senescence. Results: Our results enabled us to identify 714 compounds with potential senescent therapeutic activity, of which 270 exhibited desirable medicinal chemistry properties, and we developed an interactive web tool freely accessible to the scientific community. Conclusions: we found that flavonoids were the most abundant compound class from which 18 have never been reported as senotherapeutics.