Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods

Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also possess harmful hemolytic toxicity, which can cause the lysis of erythrocytes and thereby hamper their applications in medicine. As such, the organic synthesis of diverse saponins with versatile therapeutic effects and without hemolytic toxicity has gained considerable interests among medicinal/organic chemists. To date, the non-hemolytic saponins of interests have usually been designed by the traditional trial-and-error method or discovered by serendipity. It would be more efficient to develop an in silico method to rationally design promising saponins without hemolytic toxicity prior to the laborious organic synthesis, despite the fact that there is, so far, no computational model to predict the hemolytic toxicity of saponins. To this end, we manually curate 331 hemolytic and 121 non-hemolytic saponins from the literature for the first time and build the first machine-learning-based hemolytic toxicity classification model for the saponins, which provides encouraging performance with 95% confidence intervals for accuracy (0.906 ± 0.009), precision (0.904 ± 0.012), specificity (0.711 ± 0.039), sensitivity (0.978 ± 0.010), F1-score (0.939 ± 0.006), and Matthews correlation coefficient (0.756 ± 0.025) on the test set by averaging over 19 different random data-partitioning schemes. Moreover, we have developed a free program called "e-Hemolytic-Saponin" for the automatic prediction and design of hemolytic/non-hemolytic saponins. To the best of our knowledge, we herein compile the first comprehensive saponin dataset focused on hemolytic toxicity, build the first informative model of hemolytic toxicity for the saponins, and implement the first convenient software that will enable organic/medicinal chemists to automatically predict and design the saponins of interests.