Artificial Intelligence in Phycochemicals Recognition

Given the exponential growth of data in biotechnological processes, artificial intelligence (AI) and machine learning applications are getting much attention in both academic and industrial research, where the rapid product development in a constantly changing environment is challenging, as quality, speed, and efficiency are crucial. Since the commercial value of microalgae and seaweeds has been recognized due to their unique chemical spectrum of phytochemicals, such as pigments, fatty acids, phenolic compounds, polysaccharides, and proteins, the systematic search for new innovative and sustainable inputs is increasingly ongoing. However, overcoming laborious and cost-intensive research is still a bottleneck. Extracting relevant data and solving complex problems due to recent advances in computing power, e.g., high-performance computing and improvements in technologies such as Deep Learning and Random Forest, have the potential to face these challenges. In chemodiversity research, AI has been widely applied in drug development, omics data analysis, as well as system design and optimization, so far. In this context, this chapter introduces the basic concept of computational methods, with a focus on the recent achievements and research trends proposed for the discovery of bioactive algal compounds and their pathways, including the elucidation of their structural information.