Advances in AI for predicting pharmacological properties of natural medicines

Abstract

Artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL), has become an important tool in drug development. An increasing number of studies have discovered pharmacologically active compounds in natural products, and AI's high-throughput capabilities have accelerated the drug discovery process. In the development of natural medicines, AI can use existing datasets and experimental data to screen for lead compounds with potential activity and predict disease-associated drug targets. Furthermore, using AI to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of lead compounds early in the drug discovery process can save time and money. This review introduces AI applications for predicting the pharmacological properties of natural drugs by outlining model construction principles and recent advances, summarizing key aspects such as feature selection and evaluation metrics, and discussing natural drug development challenges and opportunities.