AI-driven interdisciplinary integration from a chemical perspective: opportunities, pathways, and challenges

As the global economy evolves, modernization and urbanization accelerate and various global problems followed. Research in many fields, such as environment, climate and healthcare, is facing increasing challenges. These problems are too complex to be systematically solved by a single discipline. In recent years, artificial intelligence (AI) technology has developed rapidly, as the core discipline of scientific research, when AI technology is applied into chemistry research, great potential will be shown in breaking disciplinary boundaries, promoting multidisciplinary integration and solving various global problems. This paper systematically reviews a number of literatures in the field of AI in the past decade, introduces some AI systems or models that have been applied to various application scenarios in the field of chemistry, such as AlphaFold, SynthReader, dZiner, and AlphaFlow, from reading, deeply learning and analyzing literature, designing synthesis routes, to conducting experiments independently. It predicts and discusses the mechanism of breaking down disciplinary barriers by building an interdisciplinary real-time fusion platform and dynamic knowledge graph, as well as the future path of AI assisted chemical research—"AI+Chem" and other disciplines. It also analyzes and looks forward to the possible development direction of AI and its challenges. At present, whether it is AI+Chem or AI+Chem+multidisciplinary research is still in its infancy, preliminary research has shown that AI technology is very likely to be a powerful tool to break down disciplinary barriers and connect knowledge islands, and also provides great possibilities for solving complex global problems, and the play of tools and problem solving still need to be explored by all scientific researchers.