Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects - Lardos, Andreas; Aghaebrahimian, Ahmad; Koroleva, Anna; Sidorova, Julia; Wolfram, Evelyn; Anisimova, Maria; Gil, Manuel (Frontiers in Bioinformatics, 2022)
Literature-based discovery (LBD) mines existing literature in order to generate new hypotheses by finding links between previously disconnected pieces of knowledge. Although automated LBD systems are becoming widespread and indispensable in a wide variety of knowledge domains, little has been done to introduce LBD to the field of natural products research. Despite growing knowledge in the natural product domain, most of the accumulated information is found in detached data pools. LBD can facilitate better contextualization and exploitation of this wealth of data, for example by formulating new hypotheses for natural product research, especially in the context of drug discovery and development. Moreover, automated LBD systems promise to accelerate the currently tedious and expensive process of lead identification, optimization, and development. Focusing on natural product research, we briefly review the development of automated LBD and summarize its methods and principal data sources. In a thorough overview of published use cases of LBD in the biomedical domain, we highlight the immense potential of this data mining approach for natural product research, especially in context with drug discovery or repurposing, mode of action, as well as drug or substance interactions. Most of the 91 natural product related discoveries in our sample of reported use cases of LBD had been published in computer science journals and none in a biomedical journal comprising research of natural products. Therefore, it is the wider goal of this review to introduce automated LBD to researchers working with natural products and to facilitate the dialogue between this community and the developers of automated LBD systems.