AI Forge: Trustworthy AI for Drug and Therapeutics

人工智能（AI）已经被广泛应用于包括医疗保健在内的一系列行业。然而，缺乏对这些系统的隐藏内部如何操作（在定义内/外）的理解，引起了我们对每个特定工具可以或应该放置的信任级别的关注。无论系统是为最终用户提供服务（如癌症诊断）还是作为产品创造工具（如药物开发），可靠性都是关键。人工智能的一个重大成功发生在2018年底，当时英国DeepMind开发的工具AlphaFold在很大程度上解决了一个困扰了最优秀的生物化学家几十年的问题——蛋白质折叠。在随后的几年里，人工智能已经证明，它不仅可以预测数千种蛋白质的三维结构，这些蛋白质使生命能够发挥作用。“折叠”它们)，但也创造了新的和独特的蛋白质（“反向折叠”它们）。这应该允许在医疗保健和其他领域有益的新功能。Protein Forge是一家位于牛津的初创公司，专门从事蛋白质工程和设计新型蛋白质疫苗和疗法。免费提供的新人工智能工具为在这一领域工作的中小企业、学者和制药公司提供了巨大的机会。设计新的蛋白质来治疗疾病通常是一个漫长而昂贵的过程。治疗发展使用少量自然产生的蛋白质中的一种，通常是抗体，与细胞或病毒的某些部分结合。疫苗使用死亡的生物体或生物体自身的一种蛋白质。人工智能现在提供了使用全新蛋白质作为治疗病原体、肿瘤和其他疾病的平台的机会。不幸的是，人工智能目前对药物开发管道的启动提供了很大的帮助。现在使用这些新方法可以在几秒钟或几分钟内设计出数百种可能的药物，要对它们进行测试既不容易也负担不起。因此，关键是我们要开发可信的工具来评估人工智能建议的许多药物中，哪些可能像大多数药物一样，在公众批准临床使用之前无法通过严格的测试。该提案旨在进行可行性研究，将整个药物开发过程中的主要利益相关者与领先的人工智能专家聚集在一起。它将创建建立可信度的方法，并形成一个可以评估数据、技能和开发这些工具的需求的联盟。

Artificial Intelligence (AI) is already used extensively by a range of industries, including healthcare. However, a lack of understanding of how the hidden internals of these systems operate (in/out of definition), raises concerns about the level of trust we can or should place in each specific tool. Trustworthiness is key, regardless of whether the system is performing a service for an end user (e.g. cancer diagnostics) or as a tool in product creation (e.g. drug development).One of AI's headline successes came in late 2018 when AlphaFold, a tool created by UK's DeepMind, largely solved a problem that has taxed the best biochemists for decades - that of protein folding. In subsequent years, AI has demonstrated that it can not just predict the three-dimensional structure of the thousands of proteins that allow life to function (i.e. "fold" them) but also create new and unique proteins ("inverse fold" them). This should allow new functions beneficial in healthcare and beyond.The Protein Forge is an Oxford-based start-up specialising in protein engineering, and designing novel protein-based vaccines and therapeutics. The opportunities afforded to SMEs, academics and pharmaceutical companies working in this sector by the new AI tools that have been made freely available are enormous. Designing novel proteins to tackle a disease was typically a lengthy and expensive process. Therapeutic development uses one of a small number of naturally occurring proteins, usually an antibody, to bind to some part of a cell or virus. Vaccines use dead organisms or one of the organism's own proteins. AI now offers the opportunity to use entirely new proteins as platforms for treatments against pathogens, tumours and other conditions.Unfortunately, AI currently assists very much towards the start of the drug development pipeline. It is neither tractable nor affordable to test the many hundreds of possible drugs that can now be designed in seconds or minutes using these new methods. It is, therefore, key that we develop trusted tools to assess which of the many suggested by the AI are likely to fail, like most drugs, to pass the rigorous testing before they are cleared for clinical use by the public.This proposal is for a feasibility study bringing together key stakeholders from throughout the drug development process with leading AI experts. It will create methodologies to establish trustworthiness, and form a consortium in which data, skills and the need to develop these tools can be evaluated.