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

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 “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 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.

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

“…Artificial intelligence capability is increasingly a critical and intangible resource for business performance advancement ( Belhadi et al, 2021 ; Lou and Wu, 2021 ; Mikalef and Gupta, 2021 ). It suggests that artificial intelligence may bring a competitive advantage to businesses ( Chaudhuri et al, 2021 ).…”

“…AIC can deliver businesses access to valuable, rare, inimitable, and irreplaceable resources ( Ghasemaghaei, 2021 ). Many studies have deemed “firm capability” as a mediator between resources and firm performance ( Belhadi et al, 2021 ; Lou and Wu, 2021 ; Mikalef and Gupta, 2021 ). Firm capabilities are vital attributes required for business operations ( Yao et al, 2021 ).…”

Putting humans back in the loop: An affordance conceptualization of the 4th industrial revolution

“…(Sun et al, 2019), healthcare (candidate surgical procedures, drug discovery, protein folding, etc.) (Lou & Wu, 2021), images (face creation, enhancing resolution, new video scene creation, face swapping or deep fakes, etc.) (Bem et al, 2019), and the arts (music service playing on‐demand machine‐created music, creation of art works within specific genres or based on simple text descriptions, etc.)…”

Managing artificial intelligence projects: Key insights from an AI consulting firm

“…Managing AI projects is no simple task (e.g., Brethenoux & Karamouzis, 2020;Ransbotham et al, 2017;Reis et al, 2020), with one report indicating that '85% of AI projects ultimately fail to deliver on their intended promises to business' (DeNisco Rayome, 2019). While some research examines the impacts of AI on users (e.g., Fügener et al, 2021;Reis et al, 2020), organisations (e.g., Borges et al, 2021;Li et al, 2021), and industries (e.g., Lou & Wu, 2021), practitioners seeking to better manage their AI projects can only draw on a limited number of studies.…”

“…Managing AI projects is no simple task (e.g., Brethenoux & Karamouzis, 2020; Ransbotham et al, 2017; Reis et al, 2020), with one report indicating that ‘85% of AI projects ultimately fail to deliver on their intended promises to business’ (DeNisco Rayome, 2019). While some research examines the impacts of AI on users (e.g., Fügener et al, 2021; Reis et al, 2020), organisations (e.g., Borges et al, 2021; Li et al, 2021), and industries (e.g., Lou & Wu, 2021), practitioners seeking to better manage their AI projects can only draw on a limited number of studies. For example, research has examined the tensions that arise when domain experts and developers interact during the AI system development process, as well as data accessibility issues that arise during AI projects (e.g., van den Broek et al, 2021; Vial et al, 2021).…”