AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

2021 | Management Information Systems Quarterly | Citations: 0

Authors: Lou, Bowen; Wu, Lynn

Abstract: Advances in artificial intelligence (AI) could potentially reduce the complexit ... Expand

Abstract: Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm's ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm's discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental "followon" drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.Lynn Wu is an associate professor of operations, information and decisions at the Wharton School of Business, University of Pennsylvania. She researches and teaches how emerging information technologies, such as artificial intelligence and data analytics, affect innovation, business strategy, and productivity. She has won several best paper awards from top journals and flagship conferences in information systems and received two early career awards in information systems. Collapse

Topics: artificial intelligence innovation management online labor market database system research and development

Methods: machine learning instrumental variables estimation experiment propensity score matching scale validation

Theories: resource based view of the firm