明智押注、唯一选择，抑或二者皆有？生物制药研发转向人工智能

Biopharma R&Dturns to AI. Table of contents Executive summary The pharmaceutical and biotechnology(biopharma) industry faces persistent, long-standing challenges to its R&D efficiency.Top among them are the high cost of drugdevelopment and elevated clinical failure rates.The return on R&D investments has undergonea steady decline to a now flat, but unacceptablelevel, making it increasingly difficult to bringsuccessful new drugs to market. Recognizing the transformative potential of AI,most biopharma organizations (79%) are activelydeveloping strategies to integrate AI across theirR&D value chain. For example: Converging technology including advances inbiology, physics, and computational power areunlocking this breakthrough now. Our global research reveals that biopharmaorganizations recognize this potential. It finds that82% of executives believe AI will fundamentallytransform biopharma R&D, and 60% agree thatcompanies failing to scale AI will fall behind ininnovation and market relevance. Executives alsosee AI as a key enabler in early-stage R&D, wherethe most complex challenges reside, supportingthe design of experimental approaches andrefining models and prototypes. In fact, 63%anticipate that most new molecular entities(NMEs) will originate from AI-driven platformswithin the next decade. •In drug discovery, 74% of executives believegenerative AI (Gen AI) holds significantpotential. Our research reveals that targetidentification is the most widely adopted AIuse case in the drug discovery phase, with 43%of organizations implementing it. Of these,32% report productivity improvements withan average time savings of 28% compared tobefore adopting the technology. AI is now emerging as a powerful catalyst forbreakthroughs in R&D, precisely in the areaswhere the industry has struggled most in recentyears. By streamlining discovery, optimizing trialdesign, and enabling predictive insights, AI ispoised to bring to biopharma R&D more agile,data-driven, and outcome-oriented processes. •In clinical trials, organizations are alreadyusing AI for predicting endpoints and dosing Executive summary regimens, selecting trial sites, and predictingadverse events and treatment responses. Morethan 60% affirm that Gen AI can substantiallyimprove the efficiency and outcomes of clinicaltrials. Organizations are also actively exploring andpiloting AI agents in R&D, with nearly three in 10already piloting initial use cases. Plans for growinglevels of investment indicate that companiesexpect transformation to accelerate. with recommendations for accelerating theadoption and scaling of AI: •Ensure top-down support with senior leadershipbuy-in•Assess organizational risk profile and defineclear goals•Balance building core AI capabilities withstrategic partnerships•Build a data- and digital-savvy workforce•Advocate for organizational data readiness andindustry data standards. It remains unclear, though, whether theindustry will have the capacity to use AI forR&D in the most effective ways. Despite havingestablished foundational data capabilities, manybiopharma organizations remain underpreparedin data readiness. Data quality, externaldata procurement and integration, and datasharing, among other areas, are top challenges.Operational and cultural readiness to scale AI alsoremains a significant hurdle. To unlock the fullpotential of AI in biopharma R&D, organizationsmust address these gaps. We conclude the report •In regulatory submissions, AI acceleratesprocesses by automating data compilation frominternal and external sources, enhancing qualitythrough predictive modeling, and embeddingresponses to anticipated regulator queriesupfront. A strong majority (73%) agree that GenAI has the potential to fundamentally transformregulatory submission and approval workflows.Among organizations using AI to prepare andsubmit regulatory documents, 37% reportproductivity improvements, with an averagetime savings of 19%. Who shouldread thisreportand why? This report is intended for C-suite executivesat global pharmaceutical and biotechnology(biopharma) organizations. It offersrecommendations to help senior biopharmaleaders benchmark their AI maturity andunderstand the benefits that AI can bring tothe drug discovery and development process.Given the potential that the technology has totransform the R&D value chain, this report willalso be of high interest to executives acrossclinical and technological roles and functions.This report offers insights into how largeand mid-sized biopharma organizations canimplement AI and scale their AI use cases. revenue above $500 million and over half (61%)above $1 billion. Half of executives surveyedwork in early-stage R&D (e.g., discovery,preclinical research) and half within late-stageR&D (e.g., clinical development, regulatorysubmission). We also convened an industryadvisory board of two senior executives forthis report. Advisory board members served asa sounding board from research developmentthrou