Published September 2026
Protecting the AI Drug Discovery Ecosystem: Building an IP Stack Around Targets, Molecules and Medicines
Despite the considerable costs and effort in target identification, candidate generation, selection and advancement to clinical trials, 90% of candidates that enter clinical trials during drug discovery fail (Sun D, 2022). The complexity of biological systems remains a major stumbling block in the drug development pipeline. Large datasets such as multiomic datasets and complex analysis methods (for example genome-wide association studies, transcriptomic profiling, proteomic interaction mapping, and metabolic sequencing) have been used to help identify targets with the potential to interact with a given drug and to also identify biological relationships that influence therapeutic activity (Liu Y. Z., 2026) (Pun, 2026) (Rehman AU, 2024). The combination of multiomics with AI, can identify complex relationships across heterogeneous and noisy datasets and can further correlate variables that may be difficult to detect using conventional statistical approaches. This may enable improved prediction of candidate molecules that modulate disease biology (Genentech, 2024).
Traditionally, pharmaceutical patent portfolios have centered on protecting therapeutic compounds and their medical uses. However, ongoing developments in relation to the use of multiomics, biological datasets and AI-enabled analysis, means commercially significant value may be generated long before a candidate molecule enters clinical development and may be created by different organisations, for example TechBio, working alongside traditional pharma. Proprietary datasets, disease models, target identification systems, biomarker strategies and machine-learning workflows may become valuable assets in their own right. The challenge is no longer simply protecting a medicine. It is protecting the ecosystem that created it.
Intellectual Property strategy for TechBio organisations requires a broader and more integrated approach moving beyond patent protected chemistry and instruments to include assets which are typically thought of as challenging or undesirable to patent. This requires the IP team to consider an IP portfolio architecture that considers not only patents in relation to the underlying tools, but also the way data is owned, licensed, and shared. Trade Secrets, contractual and database right strategies can be utilised as tools to capture IP rights and commercial value around multimodal datasets for identification, selection and validation of the targets that subsequently leads to generation of potential candidate molecules (Roche, 2026).
In addition to creating a broader IP portfolio to ensure the IP and commercial strategies are aligned, Freedom to Operate (FTO) risk will have to move beyond considering competitor patent rights to evaluate potential challenges and restrictions arising across a complex omics stack. FTO may increasingly depend on access to data, computational tools and platform integration technologies. A company may own patents covering a therapeutic candidate, yet remain constrained by third-party dataset licences, restrictions on model training, proprietary biomarker rights or contractual obligations arising from collaborative development programmes. Consequently, FTO analyses may need to assess both conventional patent risks and rights associated with the broader discovery ecosystem (Zhavoronkov, 2026), (Liu B. H., 2025).
IP portfolio considerations at different layers of discovery and drug development process
| Layer | Considerations |
| Biology – Biological insight – Disease understanding and identification of where target intervention can take place
May include data and AI sublayers for example multiomic datasets, AI-enabled prediction models and workflows |
|
| Chemistry – screening, generation and synthesis
May include sublayers including target selection, generation of candidate molecules against target, multiple cycles of in silico optimisation, characterisation of candidate molecules and experimental validation
|
|
| Clinical – Medical Use – clinical validation
May include biomarker selection, patient stratification, and clinical feedback to earlier layers |
|
As computational biology continues to leverage data across the omics stack and become embedded across the drug discovery lifecycle, competitive advantage will increasingly derive from interconnected assets spanning biology, chemistry, clinical insight, data and computation. Organisations that view IP strategy solely through the lens of compound protection risk under-protecting key value drivers. In many cases, critical IP decisions may need to be made years before a lead candidate emerges. The most successful innovators are likely to be those that design integrated IP architectures capable of protecting not only the therapeutic itself, but also the datasets, models, workflows and biological insights that enabled its discovery and continue to accelerate its development.
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This article was prepared by Partner Russell Thom and Trainee Patent Attorney Claire Green.