A deeper understanding of preclinical data can give crucial input to make firm decisions at earlier stages of the drug discovery pipeline since it is reflecting trends in research attention that the community is following over the years. It might help researchers to base their research endeavors on evidence-based criteria and direct their interest towards understudied proteins or core scaffolds. In this context, an emerging application of protein target indexing and characterization in drug discovery is the desire to capture the past, current, and future degree of research interest in main drug target families. Up to now, explorative studies on research attention across target families have been carried out mainly from a static point of view with only a few analyses including the time dimension.<br/><br/>In this study we were tracking compound-target associations over time in a target family-wise manner which delivered a picture of trends in research attention that the drug discovery community was following over time. Further, target innovation trends were linked to biological/therapeutic innovation patterns by linking targets to Gene Ontology (GO) annotations and disease annotations from DisGeNET. Inspecting the connectivity of protein targets with GO biological process annotations as well as diseases in network representations for different time periods revealed interesting trends in drug discovery. E.g. in cancer research, interest for some cancer types (e.g. prostate cancer) shifted from primarily research on targets of the EGFR family (mainly ErbB1) to research on Janus kinases, B-RAF and hepatocyte growth factor receptor. In addition, targets being involved in “immune system processes” have experienced a tremendous increasing attention over the last 20 years, both for kinases and GPCRs. Resultant dynamic networks visualizing associated target-disease annotations for different time periods, revealed a shifted focus from targets related to cancer and diabetes research to additional interest in targets associated with behavioral and neurodevelopmental diseases (such as schizophrenia and intellectual disability).

Visualizing relationships between protein targets, GO annotations and diseases via dynamic network representations

2019 Fall National Meeting

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