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Barbara Zdrazil1, Lars Richter2, Nathan Brown3

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This study uses data mining to identify emerging drug discovery targets. Key findings show increased research interest in immune and circulatory system targets, alongside cancer and neurological disease research.

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Area of Science:

  • Computational Biology and Bioinformatics
  • Pharmacology and Drug Discovery
  • Genomics and Proteomics

Background:

  • The drug discovery process is lengthy, expensive, and has experienced declining productivity over the past two decades.
  • Integrative, data-driven approaches are increasingly crucial for identifying novel therapeutic targets.
  • Understanding research trends in protein families and classes is vital for uncovering new drug intervention opportunities.

Purpose of the Study:

  • To leverage data mining and integration to analyze target innovation trends in drug discovery.
  • To identify protein families and classes receiving significant research attention and emerging areas of interest.
  • To map the evolution of target-driven research by analyzing biological process and disease annotations.

Main Methods:

  • Utilized data mining and integration techniques to analyze scientific literature for drug discovery target trends.
  • Captured trends in biological process annotations from Gene Ontology (GO).
  • Analyzed disease associations using data from DisGeNET, tracking research focus over time.

Main Results:

  • Identified increasing research interest in targets associated with immune system processes.
  • Observed a consistent trend in research focus on targets involved in circulatory system processes.
  • Highlighted growing investigation into targets linked to cancer, intellectual disability, and schizophrenia.

Conclusions:

  • The data-driven methodology effectively captures early-stage research attention trends in the target space.
  • Findings reveal key areas of opportunity for novel drug interventions, particularly in immunology and neurology.
  • Publicly available workflows, data, and an interactive web application facilitate further exploration of these trends.