How AI is Accelerating Pharmaceutical Development

For pharmaceutical scientists, the integration of artificial intelligence (AI) and machine learning (ML) into workflows represents a fundamental paradigm shift in how the industry discovers, develops, and manufactures medicines (1). AI is no longer a futuristic concept but a present-day reality, reshaping everything from early-stage drug discovery and clinical trial optimization to real-time process control on the factory floor (1). The synergy between groundbreaking science and innovative, AI-driven manufacturing may well define the next decade of bio/pharma, creating more personalized, effective, and accessible therapies.

How is AI accelerating discovery and streamlining development?

AI is looking to be invaluable during the early stages of drug development as it can rapidly screen vast compound libraries and predict a drug candidate's properties from just its chemical structure (2). ML algorithms can identify complex patterns in high-dimensional data, allowing scientists to de-risk projects by predicting absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties in silico before committing to resources (2, 3). This capability is particularly crucial for complex molecules with poor solubility or narrow therapeutic windows, where traditional modeling may fall short (2).

Beyond initial screening, digital tools are creating a bridge between R&D and manufacturing (3). AI-powered analysis of preclinical data can help optimize formulation conditions, while virtual models of a process or product (i.e., digital twins) offer extensive in silico testing, scenario elaboration, and process optimization (1, 3, 4, 5). These tools have the potential to save time and resources by reducing the need for physical experiments. They also have the potential to accelerate tech transfer by capturing and standardizing process knowledge, ensuring consistency from a small lab batch to a large manufacturing run (3, 5).

How is AI revolutionizing manufacturing?

The impact of AI extends into manufacturing, where Industry 4.0 is driving the adoption of smart machinery, interconnected sensors, and AI-driven control systems (4). In both small-molecule and large-molecule manufacturing, the goal is to leverage data to achieve high levels of process understanding and control (4, 5). By combining AI/ML models with process analytical technology, manufacturers can monitor critical quality attributes in real time, moving toward real-time release (4, 5).

This integration enables a move from reactive to predictive—and eventually prescriptive—manufacturing (4). An AI/ML model can analyze real-time sensor data from a bioreactor, predict the final batch outcome, and drive a closed-loop optimization to adjust parameters before a deviation occurs (4,5). This can lead to improved yields, reduced variability, and more consistent product quality, when compared with standard, non-AI/ML approaches (4,5).

How can one navigate the data and regulatory landscape?

Realizing the full potential of AI hinges on high-quality, contextualized data (1,2). Process data from the factory floor is often unstructured, making it difficult for algorithms to use effectively (5). Adopting findable, accessible, interoperable, and reusable (FAIR) data principles and creating centralized data platforms are essential first steps to building a scalable, AI-ready environment (1,5). Without context, the value of data diminishes, hindering their ability to drive meaningful action (5).

Simultaneously, the regulatory landscape is rapidly evolving to keep pace with technology (6). While regulators may be open to AI-driven approaches, they emphasize the need for transparency, validation, and managing bias (2, 5, 6). The "black box" nature of some algorithms is a significant concern, pushing the industry toward hybrid models that combine the predictive power of AI with the interpretability of mechanistic models (2, 3). Establishing "good machine learning practice" will be crucial for ensuring these tools are fit-for-purpose and gaining regulatory confidence (2).

As the industry moves forward, AI will continue to augment human expertise, requiring a workforce upskilled in data analysis and robotics (1, 4). The journey ahead involves navigating these data and regulatory challenges, but the destination may be a more efficient, intelligent, and patient-centric pharmaceutical industry.

What are experts saying?

Visit PharmTech.com to watch industry experts discuss the use of AI/ML in pharma. Below is a selection of video interviews.

• PDA Regulatory Conference 2025: Wrap Up
  Susan J. Schniepp, Distinguished Fellow, Regulatory Compliance Associates, joins PDA CEO Glenn Wright and Melissa Seymour, Executive Vice President, Global Quality, Eli Lilly and Company, to talk about the regulatory and quality topics discussed at the 2025 PDA Regulatory Conference.
  https://www.pharmtech.com/view/pda-regulatory-conference-2025-wrap-up
• Industry Outlook 2025: Technology as a Supporting Element
  Pharmaceutical Technology® spoke with Alexander Seyf, CEO and co-founder of Autolomous, about the use of artificial intelligence and machine learning in pharmaceutical development and manufacturing.
  https://www.pharmtech.com/view/industry-outlook-2025-technology-as-a-supporting-element
• Drug Digest: Keeping Pace with Progressive Technologies in Biopharma Development
  This episode of Drug Digest will discuss where the significant bottlenecks are in the development and manufacturing of new molecules resulting from progressive technologies, such as AI and ML, and how best to approach these challenges.
  https://www.pharmtech.com/view/drug-digest-keeping-pace-with-progressive-technologies-in-biopharma-development
• Quality Considerations for Using AI in Bio/Pharma
  In this episode of the Ask the Expert video series, Susan J. Schniepp, distinguished fellow at Regulatory Compliance Associates, and Siegfried Schmitt, vice-president, Technical, at Parexel, discuss the quality requirements needed when incorporating AI in bio/pharmaceutical development and manufacturing.
  https://www.pharmtech.com/view/quality-considerations-for-using-ai-in-bio-pharma

References

1. Thomas, F. Expanding Pharma’s Horizons Through AI and Acquisitions." Pharmaceutical Technology 2025 49 (1), 10–13.
2. Challener, C. A. Overcoming PK/PD Modeling Challenges. Pharmaceutical Technology 2025 49 (6).
3. Haigney, S. Advancements in Biopharma Spur Innovation. Pharmaceutical Technology 2025 49 (5) 12–15.
4. Mirasol, F. Accelerating Adoption of Smart Tools to Advance Manufacturing." Pharmaceutical Technology 2025 49 (2), 10–13.
5. Markarian, J. Optimizing Data Use for Digital Transformation." Pharmaceutical Technology, Sept. 23, 2025.
6. Schmitt, S. Regulations in AI and Other Digital Technologies. Pharmaceutical Technology 2025 49 (1).