Artificial Intelligence

Digital tools, such as artificial intelligence, are improving pharma drug discovery

Vikas Jain at Tata Consultancy Services

The pharmaceutical industry is undergoing a profound transformation driven by artificial intelligence (AI). From accelerating drug discovery to streamlining operations, safeguarding patient data and enhancing patient-centric care, AI is reshaping every stage of the healthcare value chain. As AI capabilities mature, its integration is not just an advantage but a necessity for companies looking to stay competitive.

Global spending on medicine is forecasted to reach over $1.9trn by 2027, with the consumption of doses taken by patients on the rise.1 An ageing global population is leading to a growing demand for new and better treatment for chronic diseases. Also, advancements in personalised medicine and geneand biologics-based therapies for non-curable diseases are expected to drive sustained growth in the pharmaceutical industry.

Tasked with catering to this increasing demand, the industry is under growing pressure to ensure its output keeps pace with the global need for new therapies and treatments. To combat such demand, pharma professionals must look to the game-changing solutions empowered by AI to design and discover new drugs, and speed up operations across R&D, manufacturing, regulatory submission and delivery of drugs to patients.

In 2025, AI will transform the pharmaceutical industry. AI will impact every aspect, from enhancing innovation at breakthrough stages and streamlining drug production processes, to enabling targeted care solutions and bolstering patient privacy. Here are four ways it is expected to play out:

Accelerating drug discovery: the dawn of in silico development

As the demand for medicine grows across the next few years, the production of both new and existing drugs and therapies will become increasingly essential. AI is poised to transform drug discovery and production, enhancing early-stage innovation and accelerating the development process. By harnessing AI-powered platforms, researchers can accelerate the discovery of new drug molecules.

These advance in silico methods, greatly speeding up the discovery and development process compared to more traditional approaches. AI can help in significant reduction in trial cost and time across in vitro and in vivo studies, clinical trial to regulatory submissions, accelerating clinical study design, site monitoring, patient engagement and clinical study report authoring. The use of AI in regulatory intelligence is helping the smalland medium-sized enterprises (SMEs) to process regulatory changes, update relevant standard operating procedures (SoPs) and be compliant. This also helps in answering health authority queries in lesser time and with enhanced quality.

Using AI, pharma scientists can design new molecules and virtually test new molecules for drug-like properties. With generative AI (genAI) at our disposal, there is great potential to reshape the drug discovery process, transforming the way scientists discover new therapeutics. The future of drug discovery will be centred around data, where this part of the sector will transform into an engineering and data design activity with human scientists in the loop.

GenAI brings a number of advantages over traditional drug design, such as being able to sample from a larger chemical space where traditional approaches can only screen molecules from a chemical library with a limited number of molecules. A drug molecule should be selectively active only against targets of interest, non-toxic and administered through a delivery mechanism across certain barriers in the body. The optimisation of these drug-like properties is a sequential process in the traditional approach and often leads to late-stage attrition. The generative approach can incorporate various drug-like properties during design stage and perform on-the-fly, multi-property optimisation so that it can search in a specific chemical space, which satisfies all the drug-like properties.

The explainable AI approach provides reasoning behind model predictions, which is helpful for refining a drug molecule during lead optimisation. With recent progress, genAI approaches can also include the synthesis route of the designed molecule through forward synthesis-based design. In addition, AI-enabled knowledge synthesis brings new and relevant knowledge faster to support discovery, development and operations.

Streamlining operations: efficiency and speed from lab to market

R&D are integral to the pharma industry, but the time and resources used to produce and deliver treatments are equally important to maintaining pace with growing demands. Business processes tuned with AI tools provide more predictable and efficient results, reducing costs and accelerating the time to market.

AI can also accelerate and improve the efficiency of the clinical trials, by selecting the most appropriate sites, improving patient recruitment, accelerating protocol and study design. What’s more, AI-powered multi-modal intelligent assistants can provide valuable support to stakeholders across the pharmaceutical industry, augmenting their capabilities. Examples include AI-enabled research assistants, sales rep assistants, batch disposition digital assistants and regulatory intelligence assistants, enhancing the work conducted by various pharma professionals.

Furthermore, AI-generated reporting or query assistants can be deployed to assist medical image analytics, audio pattern diagnostics and audit intelligence. AI tools are able to understand, condense and extract specific information, and can be used to automate the documentation and regulatory submission packages needed to bring drugs to market, such as clinical data management.

As regulatory enforcement on drug development – cited as a key challenge facing the pharma sector in 2024 – becomes stringent, aligning with regulation at all stages of drug design and development is essential.2 AI tools can be used to optimise pharmacovigilance case processing and regulatory submissions, accelerating administrative procedures whilst instilling compliance at each stage of delivery.

The use of AI for knowledge work in the pharma industry has matured over the years. Initial adoption was on automating low-end repetitive tasks and thus driving productivity. The second level of adoption was to augment knowledge workers on high-end tasks by presenting data insights. The aim was to drive work quality and not just productivity – here AI helped in assimilating formal and contextual knowledge, but was still dependent on skills and experience of the person on the job. The third and current adoption is to transform knowledge work by presenting decisions and not data insights. Here, the aim is to assimilate tacit, contextual and formal knowledge, thus driving consistency in work quality irrespective of the skills and experience variation across knowledge workers. Such reasoning systems can be built using agentic AI, models and tools, which can present choices as possible solutions to the task in hand.

Safeguarding patient data privacy

AI can be used to generate synthetic data that has multiple use-cases across pharmaceutical operations, including the testing of sensor-based algorithms in medical devices and persona-specific multi-modal training simulators. Crucially, this synthetic data replicates real data but without including any personally identifiable information and, if compromised, would pose no harm to existing patients.

An AI-enabled patient-first approach

By unlocking AI’s potential, healthcare systems can put the patient first, not only in terms of their privacy, but also in tailoring treatments to individual patient needs. Within the healthcare industry, such vast quantities of patient and medical data can be challenging to sift through by individuals alone. However, by applying AI algorithms, valuable insights can be brought to the surface that help professionals target treatments to patient needs, developing targeted therapies and predicting treatment responses based on multiple parameters, including individual genetic profiles. AI-based solutions can bring patient-centric approaches to drug development and delivery. Besides enabling personalised medicine, AI can help in predicting potential adverse events and disease progression for individual patients. AI-enabled patient engagement tools can provide patients with personalised health information, reminders for medications, and facilitate better communication between patients and providers. Finally, AI-based solutions can help collect and analyse patient report outcomes from wearables to enable a deeper understanding of the patient’s experience and quality of life.

Conclusion: AI as the driving force of pharma’s future

The pharmaceutical industry faces escalating global demand and evolving healthcare challenges, necessitating the adoption of transformative solutions like AI that empower the industry to not only meet this burgeoning demand, but also enhance patient care and ensure data privacy. From accelerating drug discovery to streamlining operations and improving patient care, AI offers a powerful toolkit to meet the ever-growing need for medicine.

AI is not just an incremental improvement – it is a transformational force driving the pharmaceutical industry forward. From revolutionising drug discovery to optimising operations, fortifying cybersecurity and personalising patient care, AI is unlocking new possibilities at every stage of the healthcare ecosystem. Companies that embrace AI-driven innovation will not only improve efficiency and compliance but also deliver better patient outcomes, reduce costs and accelerate time to market. As AI continues to evolve, its integration into pharma will redefine what’s possible, making the future of medicine smarter, faster and more patient-focused.

Ultimately, by leveraging AI’s capabilities, the pharmaceutical industry can transition to a more efficient, patient-centric model, ensuring that life-saving treatments are developed and delivered with greater speed, precision, safety and efficacy in the years to come.

References:
1. Visit: statista.com/statistics/280572/medicine-spendingworldwide/

2. Visit: pwc.com/us/en/industries/pharma-life-sciences/pharmaceutical-industry-trends.html

Vikas Jain, vice president and business head of Life Sciences at Tata Consultancy Services (TCS), has over three decades of industry experience and currently heads TCS’ life sciences industry unit. In his previous roles, he was the global head for TCS’ Life Sciences and Healthcare business within Enterprise Growth Group, managed large client relationships, headed TCS operations for a large region in US and incubated technology centres of excellence.