Knowledge-based approaches to drug discovery for rare diseases. Issue 2 (February 2022)

• Record Type: Journal Article
• Authors: Alves, Vinicius M.; Korn, Daniel; Pervitsky, Vera; Thieme, Andrew; Capuzzi, Stephen J.; Baker, Nancy; Chirkova, Rada; Ekins, Sean; Muratov, Eugene N.; Hickey, Anthony; Tropsha, Alexander
• Is Part Of: Drug discovery today. Volume 27:Issue 2(2022)
• Page Start: 490
• Page End: 502
• Publication Date: 2022-02
• Subjects: Informatics -- Rare diseases -- Drug discovery -- Data mining -- Knowledge graphs; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2021.10.014
• Languages: English
• ISSNs: 1359-6446

Abstract

Graphical abstract: Highlights: About 450 million people are suffering from over 7000 rare diseases worldwide. Current drug discovery pipeline is inefficient and unsustainable for rare diseases. Knowledge mining can boost the development of therapeutics for rare diseases. Abstract: 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.