A Deep and Meaningful Understanding of Data
When the stakes are life and death, as they often, literally, are in the pharmaceutical industry, every data point becomes hugely significant. The pharmaceutical industry might not be dealing with the deluge of data that the finance, telco, or retail industries are but it’s inarguable that the data pharmaceutical companies have to deal with is far more meaningful. After all, what industry can claim it helped save 20 million lives by creating a vaccine in record time when a once-in-a-century pandemic hit in 2019 with COVID. It’s inarguable, the pharma industry needs to have a deep and meaningful understanding of its data.
With research and development (R&D) data, clinical trial data, regulatory and compliance data, manufacturing and supply chain data, commercial and market data, financial and operational data, patient-centric data, competitive intelligence data, as well as a whole host of other types of data, the pharmaceutical industry has long understood its very existence is reliant on — and underpinned by — data.
AI is revolutionizing the industry by accelerating drug discovery, optimizing clinical trials, and improving manufacturing. One could say the industry’s future is AI, but that future is already here. According to Damla Varol’s AI in the Pharmaceutical Industry: Innovations and Challenges
AI in Drug Discovery
According to Precedence Research , “The global artificial intelligence (AI) in drug discovery market size was calculated at USD 6.31 billion in 2024 and is predicted to reach around USD 16.52 billion by 2034, expanding at a CAGR of 10.10% from 2025 to 2034” (see Figure 1). AI is transforming drug discovery and drug design by providing advanced tools and insights that enhance every stage of the discovery and design process. From target identification to compound optimization, AI helps pharmaceutical companies identify and develop new drug candidates more efficiently and effectively. This not only reduces the staggering cost of new drug development but also accelerates the development of new therapies. This, ultimately, benefits both the drug companies and their patients.
Spend on AI in drug discovery To start with, AI analyzes vast amounts of biological data to identify potential drug targets and then predicts the biological relevance of potential targets, helping prioritize the most promising ones. AI accelerates the screening of large chemical libraries to identify compounds that interact with a target of interest. This reduces the need for extensive and costly experimental screenings. Generative AI can create novel chemical structures with sought-after properties. These new molecules can be optimized for specific characteristics, like binding affinity, solubility, and stability.
When looking for potential drug candidates, AI predicts how small molecules bind to a target protein, providing insights about binding modes and affinities. Molecular Dynamics (MD) Simulations predict molecular interactions while providing a deeper understanding of drug-target interactions.
AI can analyze existing drug databases and literature to identify new therapeutic uses for approved drugs. It speeds up the development process as well as uncovers new monetization avenues for old drugs. AI uses network pharmacology to identify potential drug-disease relationships, facilitating the repurposing of old drugs.
Case Study: Insilico Uses AI to Design Novel Molecules
Insilico, a leading global biotech company, uses generative AI, a subset of AI that focuses on creating new content and data, to develop new therapies for debilitating diseases. According to her article, Quicker Cures: How Insilico Medicine Uses Generative AI to Accelerate Drug Discovery
Insilico used generative AI “to identify a molecule that a drug compound could target, generate novel drug candidates, gauge how well these candidates would bind with the target, and even predict the outcome of clinical trials,” says Yao. What would have cost more than $400 million and taken up to six years was done for $40 million in one-third of the time, claims Yao. “To develop its pulmonary fibrosis drug candidate, Insilico used Pharma.AI to design and synthesize about 80 molecules, achieving unprecedented success rates for preclinical drug candidates. The process — from identifying the target to nominating a promising drug candidate for trials — took under 18 months,” says Yao.
“This first drug candidate that’s going to Phase 2 is a true highlight of our end-to-end approach to bridge biology and chemistry with deep learning,” said Alex Zhavoronkov, CEO of Insilico Medicine . “This is a significant milestone not only for us, but for everyone in the field of AI-accelerated drug discovery.”
The plan is to test the drug in several hundred people, a process that will take several months, say Yao. However, the company is working on 30 other programs that target other diseases, including several cancer drugs, contends Yao .
AI speeds up the drug discovery process AI in Clinical Trials
From patient recruiting to trial design to real-time monitoring to data analysis and insight to post-trial analysis, AI is transforming clinical trials by enhancing efficiency, reducing costs, and improving the accuracy and success rates of trials.
According to Grandview research’s Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Therapeutic Space (Oncology, Neurodegenerative Diseases), By Application, By Region, And Segment Forecasts, 2024 – 2030,
GAVI , the vaccine alliance, claims, “Deaths from COVID-19 were two-thirds lower than they otherwise could have been during the first year of the vaccination programme, mathematical modelling suggests.” All of the advances that helped researchers develop the COVID vaccine on an accelerated schedule are meaningful in the most important number of all — the number of human lives saved. These estimates are in the region of 20 million people.
Huge Financial Gamble
Clinical trials can be huge financial gambles. FDA approval means billions of dollars in future drug sales while rejection might mean the pharmaceutical company doing the trials goes bankrupt, literal financial death. The stakes are that high. Pharmaceutical companies must implement data integration and data quality testing procedures to ensure data meets predefined quality standards. Establishing robust data pipelines ensures data accuracy, completeness, and integrity. Data governance ensures the accuracy, reliability, and consistency of clinical trial data throughout the trial lifecycle. Governance protocols govern electronic data capture (EDC) systems, preventing data tampering or manipulation. Pharmaceutical companies must prioritize data quality to maintain regulatory compliance and patient trust.
AI tools can analyze electronic health records (EHRs), genetic data, and other sources to identify suitable candidates for clinical trials. Patients are stratified based on genetic, clinical, and lifestyle factors, enabling more precise and effective clinical trials. Machine learning models predict which patients are most likely to benefit from treatment, which improves recruitment efficiency. Clinical trial outcomes are modeled to optimize trial designs, which raises the chances of trial success.
Using historical data, AI predicts the success of different trial designs, helping researchers choose the most effective protocols. Various trial simulations determine optimal sample sizes, dosages, and endpoints. AI pinpoints potential risks and challenges in trial designs, allowing for proactive mitigation strategies.
AI can also accelerate the analysis of large datasets, identifying trends and insights while predicting trial outcomes. This helps researchers make more informed decisions.
Covid-19 Vaccine
20M
lives saved by the Covid-19 vaccine
Deaths from COVID-19 were two-thirds lower than they otherwise could have been during the first year of the vaccination programme, mathematical modelling suggests.
Manufacturing and the Supply ChAIn
In her Forbes article, Beyond The Algorithm: Why Data Governance Is Key To Pharma’s AI Future
The Covid-19 vaccine was developed in record time, which probably saved millions of lives. AI models optimize manufacturing processes by identifying inefficiencies, reducing waste, and improving yield. For example, AI can adjust parameters in real-time to maintain optimal production conditions. AI-powered visual inspection systems and machine learning algorithms spot defects or anomalies in products during manufacturing, ensuring compliance with quality standards.
AI uses historical data, market trends, and external factors to predict demand for pharmaceutical products, optimizing inventory levels and reducing overproduction. It optimizes inventory levels by predicting supply needs and automating reordering processes, ensuring that critical drugs and materials never run out. AI improves logistics by reducing transportation costs, ensuring timely delivery of products, optimizing routes. This is especially true for temperature-sensitive drugs like vaccines.
Using AI In Pharmaceuticals
From its ability to revolutionize quality assurance and quality control to its ability to enhance efficiency, accuracy, and decision-making across various stages of the product lifecycle to its machine learning algorithms that analyze vast datasets to identifying potential drug targets, AI is proving an indispensable tool for pharmaceutical companies. Examples include:
1
Quality Assurance & Quality Control
AI algorithms can analyze images from production lines in real-time to identify and reject defective products. They analyze batch records to identify deviations or anomalies that could indicate quality issues. Sensors on manufacturing equipment fitted with AI can predict when maintenance is needed, reducing downtime and preventing production errors. Natural Language Processing can review and interpret large volumes of documentation to flag inconsistencies. AI also automates repetitive QC tasks, such as data entry and analysis, reducing the risk of human error.
2
Clinical Trial Design & Monitoring
AI uses historical data to predict the success of trial designs, helping researchers choose the most effective protocols. AI can simulate different trial scenarios to determine the optimal sample size, dosage, and endpoints. It can analyze electronic health records, genetic data, and other sources to identify patients who meet trial criteria. By including diverse populations, AI helps ensure trial results are applicable to a broader patient base. It can also identify unexpected side effects or deviations from expected results.
3
Pharmaceutical Manufacturing
AI systems monitor manufacturing processes in real-time, ensuring optimal conditions are maintained. AI predicts potential bottlenecks or inefficiencies in the production line, enabling proactive adjustments. Computer vision systems utilizing AI inspect products for defects. Machine learning models can detect anomalies in equipment performance and alert technicians before a failure occurs. Demand forecasting models predict demand for pharmaceutical products, enabling better inventory management and reducing overproduction or stockouts.
4
Pharmaceutical Product Management
Machine learning algorithms analyze vast datasets to identify potential drug targets as well as predict the efficacy and safety of compounds, reducing the need for extensive lab experiments. AI optimizes manufacturing processes, improving yield and reducing waste. It can model analyze market trends, competitor activities, and historical sales data to predict future sales. AI models analyze market trends, competitor activities, and historical sales data to predict future sales.
5
Pharmaceutical Product Development
AI models predict the biological activity of potential targets, helping prioritize the most promising ones. AI accelerates the screening of chemical compounds, predicting their interactions with biological targets and identifying potential drug candidates. It generates novel drug-like molecules with desired properties, reducing the time and cost associated with traditional methods. Machine learning models can optimize the chemical structure of compounds to enhance efficacy, reduce toxicity, and improve pharmacokinetic properties.
6
Drug Discovery – Drug Design
AI accelerates the screening of large chemical libraries to identify compounds that interact with the target of interest. This reduces the need for extensive and costly experimental screening. It can design new molecules from scratch, optimizing for specific characteristics such as binding affinity, solubility, and stability. Utilizing descriptive analytics, it analyzes existing drug databases and literature to identify new therapeutic uses for approved drugs, speeding up the development process.
7
Drug Discovery – Drug Screening
AI models can forecast interactions between drugs and biological targets, aiding in the identification of promising candidates for further development. AI algorithms can generate novel drug-like compounds by optimizing chemical structures based on desired properties and biological activity. By predicting potential toxicity and side effects early in the drug discovery process, AI helps to reduce the attrition rates of drug candidates, saving time and resources.
Getting Your Data AI-Ready
In her Forbes article, Beyond The Algorithm: Why Data Governance Is Key To Pharma’s AI Future
For Chakrabarty, AI-ready data isn’t just about volume; it’s about the data’s quality, structure, context, and timeliness. Data needs to be accurate, reliable, consistent, and organized for efficient processing and querying, she contends. “Comprehensive metadata and annotations give AI models understanding,” says Chakrabarty, while adding current and relevant data should reflect the latest trends. Without these data elements, even the most sophisticated algorithms are worthless.
Chakrabarty recommends a five-pronged data approach; build a robust data ecosystem, automate data pipelines, upskill the workforce, incorporate data privacy and regulatory requirements, and optimize clinical trial supply management. Companies must also decide between using a centralized or federated data ecosystem, she says, with the latter more likely to foster innovation.
Automating data pipelines involves streamlining the processes of ingesting, cleaning, transforming and preparing data for AI applications. This automation accelerates the delivery of high-quality, model-ready data while reducing human error and lowering operating costs.
By leveraging advanced technologies like AI, organizations can ensure that data flows seamlessly through the company pipeline. This enables real-time analytics, which leads to faster decision-making. Automated pipelines adapt to changing data requirements. They enhance scalability and efficiency, ultimately empowering pharmaceutical companies to harness the full potential of their data for AI-driven insights.
Happy and HeAlthy
AI is revolutionizing the pharmaceutical industry by transforming drug discovery, optimizing clinical trials, enhancing the manufacturing process, and improving supply chain efficiency. Its ability to analyze vast amounts of data with precision and speed is not only reducing costs and accelerating timelines but also paving the way for groundbreaking therapies and treatments that will probably save lives.
Unfortunately, the sober reality about success with AI is it doesn’t come easy. Money spent on AI is often a wasted expense. However, there are things a pharmaceutical company can do to increase the odds of success. As Sharon Reisner explains in her article, Why most AI implementations fail, and what enterprises can do to beat the odds
As evidenced by companies like Insilico Medicine and Exscientia, AI-driven innovations are already delivering tangible results, from faster drug development to more effective clinical trials. Insilico’s AI methodology should be a harbinger of things to come. These kinds of cost and time savings will quickly be noticed by other players in the industry. Time is money and money runs out quickly in an industry as expensive as the pharmaceutical industry, so anything that saves time and money should be quickly embraced. When it’s a matter of life and death. as if often is in pharma, data mustn’t get in the way of results. With AI as part of the process, it won’t.
