AI-Driven Drug Discovery for Rare Diseases.

Area of Science:

• Medical Research
• Computational Biology
• Drug Discovery

Background:

• Rare diseases (RDs) affect 300 million globally, with over 90% lacking effective treatments.
• Traditional drug discovery faces significant hurdles in RDs due to complexity and low investment returns.
• Existing legislative efforts have not sufficiently addressed the therapeutic gap for most RDs.

Purpose of the Study:

• To explore the revolutionary potential of artificial intelligence (AI) in accelerating drug discovery for rare diseases (RDs).
• To review AI-driven advancements addressing challenges in RD therapeutic development.
• To synthesize current knowledge and guide future R&D in AI for RD research.

Main Methods:

• Review of AI applications in drug discovery, including machine learning (ML) and deep learning (DL).
• Analysis of AI's role in drug repurposing, biomarker discovery, and personalized medicine for RDs.
• Examination of AI in genetics, clinical trial optimization, and novel drug target identification.

Main Results:

• AI demonstrates significant potential to overcome traditional drug discovery limitations for RDs.
• AI facilitates advancements in identifying new drug targets and optimizing clinical trials.
• AI-driven strategies like drug repurposing and personalized medicine show promise for RDs.

Conclusions:

• AI is pivotal in transforming rare disease research and development.
• AI accelerates therapeutic development, offering hope for improved patient outcomes in RDs.
• This review highlights AI's crucial role in addressing the unmet medical needs of rare diseases.