The pharmaceutical and biotech industries are entering a transformative era, exploring the potential of AI in healthcare innovation. AI offers tools to streamline a traditionally slow and costly process, where developing a single drug can take 12–15 years and cost billions of dollars. By partnering with AI-focused innovators, the aim is to address inefficiencies, uncover novel therapeutic opportunities, and accelerate the delivery of life-changing treatments. 

Since 2015, AI-native biotechs and their pharma partners have entered 75 molecules into clinical trials, of which 67 were in ongoing trials as of 2023. From understanding diseases at a molecular level to designing novel compounds using AI-driven molecular design, AI for drug repurposing, and clinical trial optimization with AI, how research is conducted is changing. These advances are not only reducing costs but are also enabling new approaches to previously intractable medical challenges. 

AI is creating new possibilities in drug discovery, providing tools to accelerate and enhance every stage of the process. By analyzing complex datasets and leveraging computational power, AI enables researchers to uncover novel therapeutic targets, predict molecular properties, and design effective treatments with unprecedented precision. Its potential extends beyond improving efficiency and reducing costs to creating entirely new avenues for addressing unmet medical needs. 

For example, AI-driven models like Insilico Medicine’s PandaOmics have significantly advanced Alzheimer’s research, identifying potential therapeutic targets previously difficult to decipher. Institutions such as the Botnar Institute for Immune Engineering (BIIE) are merging computational and biological sciences to develop targeted and effective treatments for diseases once considered intractable, such as childhood diseases in low-to-middle-income countries. 

Generative AI is also revolutionizing molecular design by crafting molecules tailored to specific therapeutic goals, as demonstrated by startups like JURA Bio. An ‘AI-native’ drug discovery and development engine, JURA Bio demonstrates the potential of machine learning in drug discovery by integrating advanced algorithms into every stage of the process, from molecular design to clinical outcome estimation. These advancements streamline workflows by automating and accelerating tasks that traditionally required manual experimentation, such as predicting molecular properties or optimizing compound structures. By improving the accuracy of initial designs, generative AI reduces the need for costly, time-consuming iterations, ultimately improving efficacy and lowering production costs. Shortening feedback loops means researchers can quickly test, learn and refine molecules, enabling faster progression through the drug development pipeline. 

AI provides efficient pathways for identifying new uses for already-approved drugs, reducing the time and costs associated with traditional discovery methods. By analyzing clinical and molecular data, AI has identified novel indications for existing therapies. For example, during the COVID-19 pandemic, BenevolentAI identified Eli Lilly’s baricitinib as a potential COVID-19 treatment using their BenAI engine platform in just 48 hours, showcasing AI’s ability to address urgent challenges and advance healthcare innovation.  

AI’s role extends beyond discovery into clinical development, where it addresses inefficiencies in clinical trials—the most expensive and time-consuming stage of drug development. By enhancing trial design, developing more objective endpoints, advancing the use of AI in precision medicine and streamlining trial operations, AI can simplify workflows, reduce costs and accelerate timelines. 

AI enables real-time optimization of trial protocols, balancing patient burden, cost and scientific objectives 

Tools for protocol digitization, such as Risklick’s Protocol AI,  are redefining clinical trial design by systematically analyzing study elements to clarify requirements, generating optimized scenarios and leveraging customized generative AI to create tailored protocols. This approach enhances trial efficiency, reduces protocol amendments and integrates seamlessly with digital trial systems, ensuring that trials yield robust, meaningful results while minimizing resource expenditure and patient burden. Complementing these advancements, the FDA has mandated the use of the Electronic Common Technical Document (eCTD) format for certain submissions, streamlining regulatory processes and facilitating efficient data exchange.

Recruiting and retaining participants are among the most challenging aspects of clinical trial management. AI-powered clinical trial tools address recruitment challenges, such as ensuring adequate representation across racial, ethnic, gender and socioeconomic groups, by analyzing vast amounts of structured data (e.g., electronic health records) and unstructured data (e.g., physician notes). This enables researchers to identify eligible participants who might otherwise be overlooked by traditional recruitment methods. While this targeted approach accelerates recruitment and ensures diverse and representative trial populations, retention requires additional strategies. 

High study participant dropout rates not only increase costs but also delay results, complicate data analysis, and even jeopardize the success of the trial. AI tools for patient retention offer unique capabilities to monitor patient behavior, detect early signs of disengagement, and tailor outreach efforts to keep participants motivated. By identifying potential barriers—such as transportation challenges or scheduling conflicts—AI enables trial organizers to provide necessary support to help participants remain involved throughout the study. For instance, AI-powered platforms such as AiCure can monitor patient engagement in real time, allowing researchers to address concerns promptly and reduce dropout rates. 

Innovative applications such as AI-generated synthetic control arms are transforming clinical trial methodologies. These virtual cohorts, created using predictive models and historical data, reduce the need for placebo groups, thereby addressing ethical concerns and lowering costs. Synthetic study arms are particularly valuable in rare disease research, where patient population sizes are limited. 

AI also helps investigators, through predictive analytics, to forecast outcomes and identify potential risks. Anticipating challenges early allows clinical researchers to adopt “fail-fast” strategies, which allows for the quick identification of failures early in the process, rather than letting them persist or be discovered later in development, thereby allowing the re-allocation of resources to more promising candidates. 

Moreover, AI-driven diagnostics including speech biomarkers and real-time data monitoring further refine trial methodologies by identifying subtle changes in patient health and predicting outcomes with greater precision. For example, speech biomarkers can detect early cognitive decline in neurodegenerative diseases like Alzheimer’s by analyzing variations in speech patterns, while real-time data monitoring enables continuous tracking of patient vital signs during trials, providing immediate insights into drug efficacy and safety. 

Despite its transformative potential, AI adoption in drug discovery lags behind other industries, as noted in the “Where’s the Value in AI?” report published by the Boston Consulting Group (BCG). While AI has garnered significant hype, only 22% of companies have advanced AI projects beyond the proof-of-concept stage to generate some value, and only 4% are creating a substantial positive outcome. 

While the pharmaceutical sector is actively experimenting with AI, enterprise-wide implementation remains limited. Challenges such as data quality issues, workflow integration and cultural resistance impede broader adoption and implementation. 

AI models rely on large, standardized datasets for training and validation, but the pharmaceutical industry often struggles with fragmented, incomplete and/or proprietary data systems. Unlike in fields such as image recognition where data is abundant, pharma companies face significant challenges in consolidating and curating datasets. Successes like AlphaFold’s ability to predict protein structures in minutes were possible due to the availability of decades’ worth of curated protein structure data—something drug discovery still lacks.  

Addressing these limitations is critical for AI’s effective integration into the field 

The integration of pharmaceutical big data with AI systems presents both opportunities and hurdles for researchers. AI tools frequently operate in silos, disconnected from broader R&D workflows. This lack of integration hampers their impact, as traditional workflows in drug discovery are non-linear and iterative, characterized by feedback loops and overlapping phases that complicate processes, extend timelines and increase costs. Unlike a straightforward step-by-step progression, these workflows often require revisiting earlier stages due to new data or unexpected challenges—such as a molecule failing preclinical testing and requiring redesign, or clinical trial insights prompting reevaluation of earlier hypotheses or methodology adjustments. 

Moreover, large pharmaceutical companies face additional challenges in modernizing legacy systems to accommodate AI-driven processes. Seamless integration requires reimagining workflows and building end-to-end platforms that connect AI tools with other components of the pipeline. 

Cultural resistance presents another significant hurdle 

Concerns about people losing their jobs to AI, and whether AI-generated insights are truly reliable, create skepticism among researchers and clinicians. Many AI models function as “black boxes,” making it difficult for users to interpret their outputs and thus understand how decisions are made. Building trust through transparent and interpretable AI models whose outputs are well understood, as well as positioning AI as a tool to augment human expertise rather than replace it, can alleviate resistance and encourage adoption. 

AI regulatory challenges in drug discovery present another layer of complexity, compounding the challenges of adoption. While agencies such as the FDA and EMA are actively working to establish frameworks for AI validation, these guidelines remain incomplete, leaving drug developers uncertain about compliance requirements. Key issues include the need for robust evidence demonstrating the reliability and reproducibility of AI-generated predictions, particularly given the “black box” nature of many algorithms. 

This lack of transparency complicates efforts to ensure that AI tools meet the rigorous standards demanded by regulators. Pharma companies must also meet stringent validation requirements, such as ensuring the reliability, reproducibility and explainability of AI predictions while addressing data quality standards. 

Compliance with data privacy laws like GDPR and HIPAA, managing patient consent, and addressing ownership and bias in AI models are critical challenges. Prioritizing ethical AI practices in pharmaceutical research ensures fairness and transparency in AI-generated outcomes, highlighting the need for clear regulatory guidelines which are still evolving. Engaging with regulators to shape these frameworks is essential for fostering trust, ensuring compliance, and unlocking AI’s full potential in drug discovery. 

Before AI’s full potential can be unlocked, systemic barriers must be tackled and an ecosystem of collaboration, innovation and ethical implementation must be fostered. 

Collaboration across sectors, combining the expertise of academia, startups, biotech and established pharmaceutical companies, is essential to driving innovation in AI adoption. Initiatives like AION Labs exemplify the power of such collaborations by integrating diverse perspectives and resources to solve complex challenges in drug discovery and development. 

Ensuring that AI systems are transparent, fair, unbiased and justifiable is crucial for building trust among stakeholders, and regulators who are increasingly demanding that AI systems be explainable. Algorithmic transparency, such as explainable AI (XAI) models that clarify decision-making processes, fosters confidence in AI-generated insights. Encouraging the use of open-source tools promotes accountability and collaboration, while governance frameworks that establish industry-wide ethical standards ensure the responsible implementation of AI technologies.  

AI’s transformative potential must extend beyond high-income regions to benefit underrepresented populations and diseases. Strategies for equitable access include public-private partnerships that collaborate with governments and non-governmental organizations to fund AI initiatives addressing neglected diseases. Targeted investments in low- and middle-income countries can also bridge disparities in AI adoption, enabling more inclusive healthcare advancements. 

By focusing on these strategic priorities, the pharmaceutical industry can overcome existing barriers and fully realize the potential of AI in drug discovery and development.