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

MISQ Forthcoming

56 Pages Posted: 20 Feb 2020 Last revised: 26 May 2021

See all articles by Bowen Lou

Bowen Lou

University of Connecticut - Operations & Information Management Department

Lynn Wu

University of Pennsylvania - The Wharton School

Date Written: March 15, 2021

Abstract

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. Using a resource-based view, 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 “follow-on” 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. 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.

Keywords: AI, Drug Discovery, Machine Learning, Economics of AI

Suggested Citation

Lou, Bowen and Wu, Lynn, AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms, (March 15, 2021). MISQ Forthcoming, Available at SSRN: https://ssrn.com/abstract=3524985 or http://dx.doi.org/10.2139/ssrn.3524985

Bowen Lou

University of Connecticut - Operations & Information Management Department ( email )

Lynn Wu (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3733 Spruce Street
Philadelphia, PA 19104-6374
United States

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