JANUARY 2023LIFE SCIENCES REVIEW 19By Robert J. Prill, Director, Data Science and AI, Computational Biology and Data Sciences, Ferring Research Institute, Inc. and Yong Yue, Senior Director, Computational Biology and Data Sciences, Ferring Research Institute, Inc. f conventional drug discovery is akin feeling around in the dark, artificial intelligence (AI) is portrayed as a high-tech flashlight, an essential tool to light the way. Unbridled enthusiasm for AI drug discovery is driving massive investment in AI-drug startups and enticing established pharmaceutical companies to recruit AI talent and seek partnerships with AI-drug outfits. In this moment of rapid technological change, it is a challenge for an established pharmaceutical company to implement an AI strategy that delivers timely insights. To delay action on AI is to cede leadership in drug discovery and potentially, to relinquish competitiveness in a therapeutic area. While every organization is different, our company is taking a hybrid approach, building core AI capabilities internally while partnering to gain access to specific AI technologies. We draw upon deep expertise in biology and chemistry, especially in computational biology and computational chemistry, to define AI goals and sanity-check AI outputs. From target discovery to clinical trials, AI has the potential to increase hit rates and prevent program-ending surprises. Given the stakes, it is no wonder there is a rush to gear-up with AI.At its essence, AI is about learning from available data to make better decisions. In drug development, better decisions often mean a higher hit rate. If an AI target selection tool could CXO INSIGHTSRobert J. PrillCHALLENGES AND OPPORTUNITIES FORAI IN DRUG DISCOVERY
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