Drug activity screening and safety evaluation based on artificial intelligence technology

Abstract

This article explores the application of artificial intelligence in drug screening and its integration with traditional scientific research, utilizing bibliometrics and network pharmacology methods to analyze and predict the safety evaluation and activity screening of drugs. Through advanced machine learning algorithms and big data analysis techniques, artificial intelligence has significantly enhanced screening efficiency, processing capabilities, and substantially reduced research and development costs. In terms of bibliometric research, the Web of Science database was employed for comprehensive analysis to uncover hotspots and trends in isoniazid toxicity research. Furthermore, an in-depth investigation into the mechanism of isoniazid hepatotoxicity was conducted to provide future research directions. Network pharmacology was utilized to predict for the first time both the hepatoprotective activity as well as its target action of Dracocephalum moldavuca L., thereby offering novel candidate substances for drug discovery and development. In conclusion, this study presents innovative ideas and methodologies for drug research and development while also suggesting further exploration into the application of artificial intelligence within this field. Such investigations will provide a theoretical foundation along with data support for subsequent studies on isoniazid-induced hepatotoxicity mechanisms as well as hepato-protective activities exhibited by natural compounds like vanilla.