Integrating Artificial Intelligence in Network Pharmacology: Emerging Paradigm in Drug Discovery

Artificial intelligence (AI) is booming and has applications in almost every domain today. With efficient use of AI techniques, biomedical problems are being solved in a comparatively shorter time frame compared to traditional clinical experiments. This paper reviews and presents a detailed study of applications of AI in Network Pharmacology (NP). This review encompasses a number of studies published recently in the field of Network Pharmacology and Artificial Intelligence. The papers were selected by searching the following keywords on the Google Scholar database: Network Pharmacology, and AI in combination with Chinese medicine, Ayurveda, Drug mechanisms, Drug Prediction, etc. Recent studies related to various methods and approaches for the use of AI in Network Pharmacology were selected. After a comprehensive review, it was observed that Artificial Intelligence, Network Pharmacology, and Machine Learning are widely used in drug development by creating networks of biological entities and inferring connections among them. AI in network pharmacology is used for targeting various problems, including drug discovery, drug-target interaction prediction, personalized medicine, drug safety, natural products and Ayurveda, traditional Chinese medicine, prediction of drug mechanisms, and many others. Artificial Intelligence techniques such as Multiple Linear Regression (MLR), Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Principal Component Analysis (PCA) are widely used for early-stage data analysis and screening. This review comprehensively integrates major methods and datasets used in network pharmacology and summarizes the use of algorithms and methods used in this domain.