神经科学领域长期面临药物研发困境，Alzheimer's 与 Parkinson's 等疾病的病因至今尚未完全阐明，致使制药业者反复针对有限的已知生物靶点进行开发，而绝大多数候选药物最终均以失败告终。新一代人工智慧公司正试图从根本上改变这一局面：不再仅仅利用 AI 针对已知靶点设计更优药物，而是透过 AI 发现全新的生物学机制，从而揭示大量未经探索的靶点。Recursion Pharmaceuticals 执行长 Najat Khan 指出：「建立新颖的数据集并从中发现靶点，这一路径确实可行。」该公司正利用其专有的大规模数据集来挖掘先前未被探索的神经科学靶点。
瑞士制药巨头 Roche 及其美国生物技术子公司 Genentech 已率先对这一理念投下业界最早的重大赌注。双方与总部位于 Utah 的 Recursion 合作推进了首个项目——该生物技术公司利用 AI 为一种未公开的神经科学疾病识别出一个此前从未被探索的生物靶点。这一新靶点的发现，源于 AI 对超过一万亿个（over 1 trillion）实验室培养的人类神经元数据的分析，并经过了实验室的广泛验证。Khan 表示：「这只是众多成果中的第一个。」
更大的价值并非来自单一靶点，而是其背后的数据集本身。Recursion 认为，与传统「实验完成即弃置数据」的模式不同，该公司已构建了一个可持续挖掘的资源，无需每次从零开始收集与重建数据，从而显著加速研发进程。公司神经科学副总裁 Christopher Winrow 将其比喻为一张「地图」，研究人员可以「不断回到这张地图上」搜索更多药物靶点。尽管这一新靶点仍有可能失败——大多数实验性药物确实如此——但关键问题在于此流程能否被重复验证。若 AI 能持续揭示科学家此前未曾关注的生物学机制，将有望从根本上改变药物发现的起点。
The neuroscience field has long struggled with drug development, as the causes of diseases such as Alzheimer's and Parkinson's remain incompletely understood, forcing drugmakers to repeatedly target a limited shortlist of known biological targets—most of which ultimately fail. A new generation of AI companies is attempting to fundamentally change this paradigm: rather than using AI solely to design better drugs against known targets, they aim to discover entirely new biology and uncover a wealth of unexplored targets. Recursion Pharmaceuticals CEO Najat Khan states that "creating novel data sets and then finding targets can actually work," as her company leverages proprietary large-scale datasets to identify previously unexplored neuroscience targets.
Swiss pharmaceutical giant Roche and its US biotechnology arm Genentech have placed one of the industry's first major bets on this approach. They advanced the first project from their partnership with Utah-based Recursion, after the biotech used AI to identify a previously unexplored biological target for an undisclosed neuroscience disease. This new target emerged from AI analysis of data derived from over one trillion laboratory-grown human neurons and was subsequently validated extensively in the lab. "This is the first of many," Khan says. (Key numbers: 1)
The greater promise lies not in any single target but in the dataset itself. Recursion believes it has built a continuously mineable resource rather than following the traditional model where data is discarded after each experiment, thereby significantly accelerating the speed of development. Christopher Winrow, the company's vice president of neuroscience, likens it to a "map" that researchers can "continue to go back to" in search of additional drug targets. While this new target may still fail—as most experimental medicines do—the critical question is whether this process can be reliably repeated. If AI can consistently uncover biology that scientists were not previously examining, it could fundamentally change where drug discovery begins.