AI becomes core infrastructure for pharmaceutical R&D

Artificial intelligence is moving from experimental use to a core capability in pharmaceutical research and development, helping drugmakers identify targets, design molecules and prioritise candidates for testing.

The shift is expanding AI’s role across the drug development pipeline as pharmaceutical and biotechnology companies seek to improve research productivity, manage large scientific datasets and make decisions faster, according to a report by Prezent Vivo. Recent regulatory and scientific publications also show that AI is increasingly being incorporated across drug development workflows.

AI systems can analyse molecular, genomic, proteomic and clinical information to identify patterns and relationships across large datasets. Researchers are using such systems in areas including target identification, lead optimisation, molecular design and candidate selection, according to recent research published in pharmaceutical and biomedical journals.

The commercial market for AI in drug discovery is also expanding. Grand View Research valued the global market at USD 2.3 billion in 2025 and projected it to reach USD 13.8 billion by 2033, representing a 24.8 per cent compound annual growth rate between 2026 and 2033.

Pharmaceutical and biotechnology companies accounted for the largest end-use segment in the Grand View Research analysis, with a 59.19 per cent revenue share in 2025. The report said these companies are using AI platforms for target identification, lead optimisation and drug-candidate prioritisation.

The technology is also being applied beyond early-stage discovery. A 2026 review in Drug Discovery Today said AI is increasingly being integrated into preclinical and clinical research, including adaptive trial design and drug repurposing. The researchers also cautioned that the value of AI remains dependent on data quality and experimental validation.

Regulators are responding as AI use expands. The US Food and Drug Administration and the European Medicines Agency published 10 guiding principles in January 2026 covering AI use across the drug development cycle, including nonclinical, clinical, post-marketing and manufacturing activities. The principles call for risk-based assessment, data governance, multidisciplinary expertise and lifecycle management.

The FDA has also issued draft guidance on using AI to support regulatory decisions concerning the safety, effectiveness and quality of drugs and biological products. The framework recommends assessing the credibility of an AI model for its specific intended use rather than treating AI performance as universally reliable.

Prezent Vivo, which focuses on AI and human expertise for life sciences communication, has also been promoting AI-related applications across pharmaceutical workflows. Its July 2026 material describes uses spanning target identification, drug design, precision medicine and clinical development.

The company is scheduled to hold Articulate 2026, a biopharma-focused executive event, on October 1 in Philadelphia. Prezent said the event will examine communication in an AI-first biopharma environment and bring together leaders from medical affairs, commercial and market access functions.

As pharmaceutical companies increase their use of AI, regulatory oversight, data governance and validation will remain central to how the technology is incorporated into drug development.