How Big Pharma Adopts AI To Boost Drug Discovery
(Last updated: October 2025)
Since 1950, when Alan Turing introduced the concept of 'thinking machines', artificial intelligence has steadily advanced—from the rise of neural networks in the 1980s to the release of OpenAI’s GPT-3 in 2020, which paved the way for ChatGPT in 2022. Today, AI has become an integral part of daily life for many. According to Forbes, in 2025 78% of organizations worldwide use AI, with the global user base reaching 378 million people. The worldwide AI market is now valued at approximately $244B.
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Traditionally slow to embrace new technologies, the pharmaceutical industry has nonetheless experienced accelerated adoption of AI over the past decade, with interest continuing to grow rapidly. The transformative potential of AI extends not only to healthcare providers and patients facing hard-to-treat conditions, but also to the broader biotech sector. According to Morgan Stanley, AI-driven improvements in drug development success rates could generate up to $600B in healthcare savings by 2050, assuming approval rates increase by 10% to 40% compared to recent trends. As of 2024, GlobalData’s Drugs database records more than 3,000 drugs developed or repurposed with the aid of AI, the majority still in early-stage discovery or preclinical development.
Moreover, AI is already contributing to significant efficiencies in hospital care, with potential savings projected at up to $900B by 2050. Meanwhile, the U.S. Bureau of Labor Statistics reports $9.33B in private AI investment in the healthcare sector in 2024. Against this backdrop of rapid growth and investment, let’s examine specific examples of how AI is being applied across the pharmaceutical industry.
(Since most AI-driven companies use a mix of different approaches and rely on interdisciplinary sources of data for their modeling work, the below classification of AI use cases is illustrative.)
AI for drug target discovery and disease modeling
AI is reshaping drug target discovery and disease modeling by integrating omics and clinical data to uncover therapeutic opportunities. Machine learning and graph neural networks now map complex relationships between genes, proteins, and diseases, potentially improving target identification/validation. At the same time, AI-driven disease models use single-cell data, imaging, and network biology to simulate progression, stratify patients, and reduce clinical trial risks.
AstraZeneca is one example of a pharmaceutical giant actively investing resources and attention into AI technologies. Recently, the company launched a $200M collaboration with Tempus AI and Pathos AI to build a multimodal foundation model for oncology, integrating patient data to uncover insights and accelerate drug development. Following up, AstraZeneca entered a multi-year partnership with Modella AI two months later to embed such models across its R&D, aiming to interpret complex datasets, identify biomarkers, and automate workflows. According to release communications, Modella’s AI will support AstraZeneca’s oncology portfolio, spanning immunotherapies, ADCs, bispecifics, hormone therapies, radiopharmaceuticals, and kinase inhibitors.
AI for target-based and phenotypic drug discovery
AI-driven target-based drug discovery is a computational approach that applies AI methods to identify, validate, and optimize therapeutic candidates that modulate a specific molecular target (e.g., a protein, enzyme, receptor, or nucleic acid) known or hypothesized to play a role in disease.
AbbVie partnered with Neomorph in January 2025, to develop molecular glue degraders for oncology and immunology, targeting proteins once considered “undruggable.” Neomorph will receive an upfront payment and is eligible for up to $1.64 billion in milestones and royalties. That same month, Pfizer expanded its 2020 collaboration with PostEra, focusing on antibody–drug conjugate (ADC) payload optimization using PostEra’s generative AI models—an area critical to balancing potency and safety.
Isomorphic Labs, the DeepMind spinout, broadened its partnership with Novartis, adding three new programs in February 2025. Novartis noted the collaboration is already unlocking new chemical spaces beyond traditional methods, reinforcing AI’s impact in drug design.
By mid-year, activity continued. AstraZeneca and CSPC Pharmaceutical Group launched in June a $5.22B deal to discover novel oral therapies for chronic and immunological diseases, building on their earlier cardiovascular work and AstraZeneca’s expanding R&D footprint in China. A month later, Chugai Pharmaceutical (Roche’s Japanese subsidiary) partnered with Gero on AI-identified age-related disease targets in a deal worth up to $250M.
Closing out the year, Merck expanded the agreement with Variational AI in September to apply its Enki platform to two undisclosed targets, aiming to speed early-stage drug design. The agreement, valued at up to $349M, reflects Merck’s wider strategy of embedding AI across its discovery pipeline.
AI for designing better clinical trials
AI-driven clinical trials leverage AI to enhance the design, execution, monitoring, and analysis of clinical studies. The goal is to improve efficiency, reduce costs, increase patient safety, and generate deeper mechanistic insights. AI can operate at every trial stage — from patient selection and recruitment to endpoint analysis and regulatory submission. According to Dr. ElZarrad from FDA, AI use “is really to help make inferences regarding the safety and effectiveness of the drug being evaluated”.
American big pharma Merck is actively engaging in AI-driven partnerships to boost its clinical trial design capabilities. Merck launched a new generative AI platform in June 2025 designed to streamline the creation of clinical study reports (CSRs), cutting the time to produce a first draft from two to three weeks down to just three or four days. Developed with McKinsey’s QuantumBlack, the system combines data engineering with large language models to handle table mapping, data extraction, and drafting under the oversight of medical writers. Early pilots showed the approach reduced CSR drafting hours from 180 to 80 while halving error rates in areas like data accuracy, citations, and terminology.
Sanofi joined Merck in clinical trial-related initiatives. Sanofi Ventures made a strategic investment in QuantHealth, an AI-driven clinical trial simulation company, bringing its total funding to $30M. QuantHealth’s platform uses digital twin technology to generate patient-level simulations, which leads to pharma companies better predicting trial outcomes, reducing costs and risks. This October 2025 deal aligns with Sanofi’s push to become an AI-first organization, with Chief Digital Officer Emmanuel Frenehard highlighting clinical trial simulation as a key capability for reimagining R&D. As part of the investment, Sanofi Ventures partner Cris De Luca will join QuantHealth’s board as an observer, supporting efforts to scale predictive, AI-driven trial design across the industry.
AI for drug repurposing programs
Drug repurposing (also called drug repositioning or drug reprofiling) is the process of identifying and developing new therapeutic uses for existing drugs, including those already approved for one indication, shelved due to lack of efficacy in their original target disease, or discontinued for strategic reasons. Unlike traditional drug discovery, which starts from de novo molecular design, drug repurposing leverages existing pharmacological, toxicological, and clinical data to accelerate development timelines, reduce costs, and lower risk.
In this regard Sanofi announced a partnership with Healx, a Cambridge-based AI-driven biotech in November 2024. According to the deal the French big pharma will use the Healx Healnet platform to identify new rare disease indications for one of Sanofi’s discontinued compounds. Healx will analyze Sanofi’s proprietary data using its generative AI technology to uncover potential new therapeutic uses.
AI for developing drug formulations
AI-driven drug formulation refers to the application of AI and deep learning algorithms to design, optimize, and predict the performance of pharmaceutical formulations. It accelerates the traditionally experimental and iterative process of formulation development by integrating physicochemical properties, biopharmaceutics, and clinical data into predictive computational models.
XtalPi announced the extension of its collaboration with Pfizer in June 2025. The aim is to develop next-generation molecular modeling tools for small-molecule drug discovery. The work builds on their earlier XtalPi Force Field (XFF), published in 2024, which demonstrated superior accuracy in predicting molecular geometry and binding affinity. The new phase of the effort will adapt XtalPi’s XFEP platform to Pfizer’s chemical space, aiming to deliver faster, more accurate predictive models to support drug design and development.
Connecting the dots with AI
AI’s strength lies in integrating multimodal data—scientific and operational—to generate system-level insights. AbbVie has applied this to clinical data transformation, moving from manual, error-prone standardization to an LLM that predicts trial data mappings, suggests variable alignments, and provides confidence scores. Human oversight ensures accuracy, cutting weeks of work and delivering submission-ready datasets faster.
Merck has taken a broader approach with GPTeal, a generative AI platform giving 50,000 employees secure access to models like ChatGPT, Llama, and Claude. Initially used for routine tasks, it now supports regulatory document preparation, with adoption driven by AI training programs and developer boot camps.
Sanofi, meanwhile, is applying AI to operations. The company launched a Digital Accelerator in Lyon in June to transform its Manufacturing & Supply network, deploying digital twins, AI+IoT monitoring, and scalable platforms to boost supply chain resilience—part of its goal to become the first biopharma powered by AI at scale.
What’s next?
With an increasing interest in AI-driven technologies among the leading pharmaceutical and biotech companies, a strategic focus of pharma and biotech businesses will be further shifting towards AI partnerships, R&D outsourcing, and M&A activity as a means to quickly get access to the required expertise and know-how. Complex nature of AI-based technologies, a need for costly and sophisticated IT infrastructure, a fast pace of progress in the field, and a relative scarcity of highly skilled data science specialists to support specialized machine learning research -- these are some of the key drivers of the ascending outsourcing trend.
Topic: Tech Giants
