2022 review
Knowledge-based approaches to drug discovery for rare diseases
[Review of ]. DRUG DISCOVERY TODAY, 27(2), 490–502.
author keywords: Informatics; Rare diseases; Drug discovery; Data mining; Knowledge graphs
MeSH headings : Artificial Intelligence; Drug Discovery / methods; Humans; Knowledge Bases; Machine Learning; Rare Diseases / drug therapy
TL;DR:
It is expected that a broader application of knowledge graph mining and artificial intelligence approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: February 21, 2022
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.
