论文标题
关于AI如何改变以推进药物发现科学
On How AI Needs to Change to Advance the Science of Drug Discovery
论文作者
论文摘要
自从深度学习模型兴起以来，围绕AI的科学研究已经取得了巨大的成功，即使在诸如蛋白质结构预测之类的长期挑战中也有长期的挑战。但是，这种快速发展不可避免地使它们的缺陷显而易见 - 尤其是在理解因果关系很重要的推理领域。一个这样的领域就是药物发现，其中需要这种理解才能理解虚假相关性困扰的数据。所说的虚假性只会随着生命科学中不断增加的数据量的持续趋势而变得更糟，从而限制了研究人员了解疾病生物学和创造更好的治疗剂的能力。因此，要通过AI提高药物发现的科学，就必须在因果关系语言中提出关键问题，这允许阐明建模假设，以识别真正的因果关系。 在这篇注意论文中，我们将因果药物发现作为创建模型的动力，以基础因果推理中的药物发现过程。
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.
