Artificial Intelligence and its Influence on Modern Chemical Research: An Analytical Study

Abstract

The impact of artificial intelligence (AI) on modern chemical research is also being studied through secondary data from literature. Research studies have shown that the primary applications of AI in chemistry include molecular property prediction, retrosynthetic planning, reaction optimization, de novo molecular design, catalyst discovery, materials research, and the automation of experimentation. Molecular property prediction and retrosynthesis represent two of the most highly utilized applications of AI in the field of organic synthesis. A key advantage of these methods includes their ability to rapidly screen multiple candidate compounds and predict the properties and potential yields of target products. De novo molecular design is another area where AI is playing a leading role. De novo molecular design involves generating new compound designs based upon desired chemical or physical characteristics. Historically this process was carried out primarily through manual iteration; however, AI has now been applied to perform many of the tasks involved in de novo molecular design. Catalyst discovery represents yet another application of AI in modern organic synthesis. Catalysts are used extensively throughout the chemical industry as they increase efficiency and yield when compared to traditional thermal reactions. Materials research has traditionally represented one of the largest industrial markets for chemicals. New materials often require novel combinations of different chemical functions. Traditional development of such materials requires extensive iterative screening. AI offers a method to efficiently search through vast numbers of possible material configurations. Automated experimentation represents perhaps the greatest potential for innovation offered by AI in modern chemistry. With automated experimentation, researchers can test hundreds if not thousands of different chemical configurations per day.