DECEMBER 2022LIFE SCIENCES REVIEW 19machine learning community: a publicly available training set (the protein data bank crystal structures), a neutral-party assessment on blinded predictions (the critical assessment of protein structure prediction, CASP), and a forum for open exchange of ideas and methods (competitions, conferences, and papers). Could such an open dialog around protein-ligand binding or therapeutic antibodies be possible?"From target discovery to clinical trials, AI has the potential to increase hit rates and prevent program-ending surprises"Given the challenges of AI adoption in the pharmaceutical industry, we decided that a combination of internal AI development and external partnerships is the best way to bring AI into our drug discovery process. As an example, we focus on target identification here. AI methods for target identification can use large-scale data integration to reveal hidden connections between targets, diseases, drugs, etc. A myriad of data types can be represented in a knowledge graph, a data structure amenable to exploring connections "by hand" and by machine learning. Knowledge graphs for target identification can be populated with transcriptomic measurements, canonical pathways, data-driven modules, protein-protein interactions, protein-ligand interactions, genetic interactions, gene sets, ontologies, literature-derived relationships, and other data. In short, there are a lot of options for how to represent the problem. Some potential partners specialize in this mode of target identification and can deliver hits that can be experimentally validated. The "pay for hits" arrangement has the advantage of instant gratification, but as discussed, it can be totally opaque, a black box algorithm wrapped in a black box business agreement. To most effectively engage with an AI partner requires people on the receiving end who can cut through marketing hype, are trained in machine learning, and can rigorously assess hits using quantifiable metrics. Internal AI activity does not need to replicate partners' AI systems, nor would it necessarily be possible given the degrees of freedom in how to represent the problems. Rather, internal AI development within the pharmaceutical enterprise can be focused on developing our own target feature spaces. Internally, we can measure machine learning accuracy on various supervised and semi-supervised tasks to know how much confidence to put in our target representations. Through this lens we can assess the relevance of externally generated hits to our disease biology. How surprising is this hit? If your computational tools cannot shed light on that question you need to start building a new flashlight. From target discovery to clinical trials, AI has the potential to increase hit rates and prevent program-ending surprisesYong Yue
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