Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

Abstract

Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive model to identify novel compounds with sertraline-like antidepressant activity. We constructed and validated a customized machine-learning model to predict SSRI activity in new compounds. By applying feature engineering to the chemical structures and bioactivity data of sertraline and its analogs, we trained multiple machine-learning algorithms. Through extensive comparative analysis, we found that the support vector machine (SVM) model demonstrated exceptional performance, achieving an accuracy rate of up to 93%. By further optimizing and integrating the SVM model, we successfully enhanced its accuracy, reaching an impressive 95% capability in predicting more active SSRI compounds. This study successfully developed a targeted, rapid, and efficient machine learning model capable of accurately predicting SSRI activity. The model serves as a valuable tool for rapidly screening novel SSRI drug candidates with superior activity, bringing immense value to the field of drug development.