Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Catalyst for Rare Diseases, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
Drug Discov Today. 2022 Feb;27(2):490-502. doi: 10.1016/j.drudis.2021.10.014. Epub 2021 Oct 27.
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
传统的药物发现管道已被证明不适用于罕见病。在此，我们讨论了最近在生物医学知识挖掘方面的进展，这些进展应用于发现罕见病的治疗方法。我们总结了与罕见病相关的当前化学生物基因组学数据，并就机器学习 (ML) 和生物医学知识图谱挖掘在罕见病药物发现中的有效性提供了一些看法。我们使用 chordoma 案例研究来说明这些方法的强大功能。我们期望更广泛地应用知识图谱挖掘和人工智能 (AI) 方法将加速针对罕见病和常见病的可行药物候选物的发现。
