Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs.

The accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the incomplete understanding of their mechanisms. This study presents a powerful senolytic predictor built using phenotypic data and machine learning techniques to identify compounds with potential senolytic activity. A comprehensive training dataset consisting of 111 positive and 3951 negative compounds was curated from the literature. The dataset was used to train machine learning models, incorporating traditional molecular fingerprints, molecular descriptors, and MoLFormer molecular embeddings. By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. This senolytic predictor was then used to perform virtual screening of compounds from the DrugBank and TCMbank databases. In the DrugBank database, 98 structurally novel candidate compounds with potential senolytic activity were identified. For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. Moreover, pathway enrichment analysis revealed key targets and potential mechanisms underlying senolytic activity. In an experimental screening of predicted compounds, panaxatriol was found to exhibit senolytic activity on the etoposide-induced senescence of the IMR-90 cell line. Additionally, voclosporin was found to extend the lifespan of