10068059AI Forge: Trustworthy AI for Drug and TherapeuticsClosedCollaborative R&DInnovate UKArtificial 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.