New drug development is a time-consuming, labor-intensive, and high-risk endeavor. The current state of international scientific research and development for new drugs is a major societal concern. However, this field faces significant obstacles: The rate of development has remained constant, and technical challenges continue to intensify. The frequent failures in drug discovery can be attributed to a lack of understanding of disease onset and progression mechanisms, the difficulty of identifying high-quality drug targets, and a shortage of biomarkers that can reflect disease progression and drug efficacy.

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Machine Learning in Drug Discovery

  • Jingli Ren,
  • Yiwen Tao

摘要

New drug development is a time-consuming, labor-intensive, and high-risk endeavor. The current state of international scientific research and development for new drugs is a major societal concern. However, this field faces significant obstacles: The rate of development has remained constant, and technical challenges continue to intensify. The frequent failures in drug discovery can be attributed to a lack of understanding of disease onset and progression mechanisms, the difficulty of identifying high-quality drug targets, and a shortage of biomarkers that can reflect disease progression and drug efficacy.