Reimagining Drug Discovery and Pharma Operations with AI

Reimagining Drug Discovery and Pharma Operations with AI

Organisations are increasingly leveraging Artificial Intelligence (AI), data, and digital engineering to drive innovation and operational excellence. Greg Killian, Senior Vice President (SVP) and Global Head, Life Sciences and Healthcare at EPAM, discusses AI adoption, drug discovery, Global Capability Centers (GCCs), and the future of AI-enabled pharma enterprises with Pharma Industrial India.

Q. Give us an overview of EPAM and your role in driving its life sciences business globally.

Greg Killian: EPAM is global leader in Artificial Intelligence (AI) transformation, engineering, and integrated consulting, serving a broad range of customers from Forbes Global 2,000 companies to emerging startups. With over 30 years of expertise in custom software, product and platform engineering, we empower our clients to become AI-native enterprises, driving measurable value from innovation and digital investments. I lead the global life sciences and health care business wherein we help customers add value to their customers and patients across the health care value chain: from drug/therapeutic discovery through launch, supply chain, and to the provider and patient.

We sit deep inside our customers’ platforms and increasingly act as a co-innovation partner rather than a vendor. India is central to that, making it our largest delivery hub worldwide. The focus is on helping healthcare organisations translate technology investments into measurable business and clinical outcomes, including faster development cycles, stronger AI ready data foundations, better evidence at the point of care and digital experiences that create more meaningful engagement for providers and patients with innovative therapeutics.

Q. How can life sciences companies adopt AI at scale while balancing innovation, regulatory compliance and measurable business value?

Greg Killian: I would start with the clear principle that AI programs need to be closely tied to measurable outcomes such as development cycle reduction, cost efficiencies, market performance or commercial effectiveness. The industry is moving toward AI adoption across the entire enterprise, and organisations are increasingly prioritising use cases that demonstrate proven operational or business impact before scaling further.

Adoption at scale depends on 3 areas progressing together. First, organisations need data platforms designed for interoperability, flexibility and governance, so data can be used consistently across discovery, clinical, regulatory and commercial functions. Second, governance frameworks need to ensure that AI-generated outputs remain traceable, validated and aligned to regulatory expectations, particularly in areas tied to submissions, labelling or patient engagement. Evolving US Food and Drug Administration (FDA) and European Medicines Agency (EMA) frameworks are encouraging AI usage and we should take advantage of that rather than feel constrained. Third, organisations need change management across scientific, clinical and commercial teams, including clarity on where AI-driven outputs complement traditional deterministic systems.

Finally, enterprise data is the key to AI-driven innovation in pharma. Organisations are still working through challenges around data governance, cataloguing, modelling and quality. Without strong data foundations, scaling AI across scientific and operational workflows becomes significantly difficult and can lead to unintended consequences.

Q. Where do you see AI delivering the most tangible impact in drug discovery over the next few years, and what limitations still need to be overcome?

Greg Killian: Chemistry is relatively well mapped and modelled, while biology continues to rely heavily on identifying patterns across complex datasets. As a result, the most immediate impact of AI is emerging in two areas.

The first is ‘-omics’, where large-scale datasets and compute-intensive analysis are essential to identifying biological relationships. AI is helping accelerate the connection between biomarkers, treatment response and patient stratification, thereby supporting more targeted approaches to precision medicine. The second is Real-World Evidence (RWE), where AI can help analyse federated datasets to better understand patient outcomes and inform decisions across discovery, development and care delivery.

Data across the industry remains fragmented. Any AI-driven insight must meet high standards for accuracy, reproducibility and compliance, making strong data governance and interoperability essential. It is also important for organisations to clearly distinguish between probabilistic AI models and the deterministic outputs of traditional enterprise systems.

Read the full article here.

Explore how EPAM helps global pharmaceutical companies accelerate drug discovery and optimize commercial operations across the entire product lifecycle. https://www.epam.com/industries/life-sciences-and-healthcare