Machine learning has revolutionized many fields, including drug discovery. In this field, AI (artificial intelligence) has been used to create predictive models for ADMET, drug response 10 , toxicity , and anticancer activity . These models allow virtual screening and prediction of compound activity . Several studies have been conducted on building models based on machine learning to predict the activity of compounds against IN, including those by Kurczyk A et al (2015), Li Y et al (2017), and Lucas A et al (2022) . In contrast to traditional QSAR models , which solely focused on linear equations to correlate the molecular descriptor with biological activities, the machine learning approach enables the implementation of non-linear models for QSAR.