AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability

Xi Long, Barbara Steurer, Chun Wai Wong, Ekaterina Kozlova, Vladimir Naumov, F. Pun, Alex Aliper, Fengzhi Ren, Alex Zhavoronkov
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引用次数: 0

Abstract

The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.
人工智能辅助癌症靶点优先排序,在新颖性、可信度和商业可操作性之间取得最佳平衡
确定新的生物靶点对推进癌症治疗至关重要,但破译驱动和维持疾病的极其复杂的过程可能十分繁琐,而且需要大量资源。为了优化和加速药物发现过程,人工智能(AI)平台应运而生,它能够快速、经济高效地识别和优先选择具有最佳靶点特征的新型疾病特异性治疗靶点,同时兼顾可信度、新颖性和商业可操作性。人工智能流式靶点分析有望显著改善传统药物开发的商业负担,并为新型靶点识别提供一种无偏见的方法。在这里,我们将讨论人工智能评估的靶点概况,以及最近被我们的人工智能驱动靶点发现平台确定为最优先癌症靶点的基因的临床相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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