Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle to meet the unique demands of RDs, often yielding poor returns on investment. However, the advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers groundbreaking solutions. This review explores AI's potential to revolutionize drug discovery for RDs by overcoming these challenges. It discusses AI-driven advancements, such as drug repurposing, biomarker discovery, personalized medicine, genetics, clinical trial optimization, corporate innovations, and novel drug target identification. By synthesizing current knowledge and recent breakthroughs, this review provides crucial insights into how AI can accelerate therapeutic development for RDs, ultimately improving patient outcomes. This comprehensive analysis fills a critical gap in the literature, enhancing understanding of AI's pivotal role in transforming RD research and guiding future research and development efforts in this vital area of medicine.

中文翻译：

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AI 驱动的罕见病药物发现。


罕见病 （RD） 影响着全球 3 亿人，面临着严峻的公共卫生挑战，其特点是复杂性、治疗选择有限和诊断障碍。尽管进行了立法努力，例如 1983 年的美国孤儿药法案，但超过 90% 的 RD 缺乏有效的疗法。传统的药物发现模型以开发周期长、失败率高为特点，难以满足 RD 的独特需求，通常投资回报不佳。然而，人工智能 （AI） 的出现，包括机器学习 （ML） 和深度学习 （DL），提供了开创性的解决方案。这篇综述探讨了人工智能通过克服这些挑战来彻底改变 RD 药物发现的潜力。它讨论了 AI 驱动的进步，例如药物再利用、生物标志物发现、个性化医疗、遗传学、临床试验优化、企业创新和新型药物靶点识别。通过综合当前知识和最新突破，本综述为人工智能如何加速 RD 的治疗开发，最终改善患者预后提供了重要见解。这项全面的分析填补了文献中的关键空白，增强了对人工智能在改变 RD 研究方面的关键作用的理解，并指导了这一重要医学领域的未来研发工作。