This is the third in a series of blog posts focused on AI and clinical trials/research space, highlighting topics to be discussed at the upcoming BIO International convention on June 22-25, 2026.
AI is transforming drug discovery faster than deal structures can adapt. Traditional licensing frameworks were built for linear innovation: researcher invents, institution owns, licensee commercializes. AI shatters that model by distributing inventive contributions across algorithms, datasets, and human researchers in ways that make ownership genuinely ambiguous. For business development teams, tech transfer offices, early-stage biotech CEOs, and investors, these ambiguities surface during due diligence, valuation negotiations, or post-acquisition integration as deal-killers.
This article examines critical areas where traditional frameworks break down: drug repurposing IP ownership, licensing agreement gaps, spin-out complications, and partnership red flags that signal risk.
The Drug Repurposing IP Quagmire
AI-driven drug repurposing creates ownership chains that defy traditional inventorship analysis. Consider a common scenario: a university researcher uses a commercial AI platform to analyze a health system’s patient data, identifying a new indication for an existing compound. The AI vendor provided the algorithm. The health system contributed the clinical data. The researcher designed the validation experiments. Who owns the repurposed drug indication?
Patent law requires identifying inventors—natural persons who contributed to the conception of the claimed invention.[1] But AI pattern recognition doesn’t fit neatly into “mental conception” frameworks. The algorithm identified the correlation, but algorithms cannot be inventors under current U.S. law.[2] The researcher validated the finding but didn’t conceive the initial insight. The result: joint invention complications where multiple parties have legitimate but overlapping claims.
This fragmentation becomes acute when distinguishing data rights from model rights and output rights. The health system may own the underlying patient data under HIPAA and state privacy laws,[3] but that doesn’t automatically confer ownership of insights the AI derives from that data. The AI vendor owns the model architecture, but licensing agreements often fail to specify whether that ownership extends to the model’s outputs when applied to third-party data. And the university, under Bayh-Dole Act obligations, may claim ownership of discoveries made using federal funding—even if the inventive contribution came primarily from AI analysis.[4]
Traditional IP frameworks assume human inventors with documentable conception dates. AI collaborations produce discoveries where the “aha moment” occurred inside a black-box algorithm, making inventorship determination genuinely uncertain. Investors evaluating these opportunities face fundamental questions: Can the company actually convey clear title? Or will licensees need consent from multiple parties, each with veto power over commercialization?
Licensing Nightmares: Critical Gaps
Most licensing agreements were drafted before AI became integral to drug discovery. Three gaps create risks that surface during financing rounds or acquisition due diligence.
Model vs. Output Ownership. The distinction between owning a tool and owning what the tool produces is foundational, yet frequently blurred. Device-software collaboration structures reveal that while parties negotiate ownership of developed IP, the real value often lies in improvements to background IP and future platform enhancements.[5] In AI contexts, does licensing an AI model for drug discovery include ownership of the discoveries it generates?
Many agreements grant AI vendors royalty-free licenses to model outputs without adequate restrictions. When these licenses are irrevocable, transferable to third parties, or include sublicensing rights, vendors can effectively compete with licensees or enable competitors to do so.[6] Clear specification of usage, modification, sublicensing, and distribution rights is essential, as ambiguous licensing terms lead to disputes and operational constraints.[7] In AI drug discovery contexts, this means explicitly addressing whether the licensee owns discoveries generated by the AI model, whether the vendor can use those discoveries to train improved models for competitors, and whether improvements to the underlying algorithm revert to the vendor or become shared IP.
Training Data Lineage. AI models trained in protected health information (PHI) create HIPAA compliance risks that most licensing agreements don’t address. Was the training data properly de-identified? Do patients’ original consent forms authorize AI model training? Can the model’s outputs inadvertently reveal PHI through algorithmic inference? Research demonstrates that AI can re-identify individuals from supposedly de-identified datasets, particularly when genomic data is involved.[8] Licensing agreements should address training data lineage through chain-of-custody documentation requirements and representations about consent adequacy. Without this documentation, licensees face potential HIPAA violations and state privacy law exposure under frameworks like California’s CPRA and Washington’s My Health My Data Act.[9]
Regulatory Reproducibility. FDA expects sponsors to explain and validate AI-driven discoveries submitted in IND and BLA applications. But many AI models operate as black boxes, producing outputs without transparent reasoning that sponsors can document for regulatory review. FDA’s January 2025 draft guidance on AI in drug development addresses AI use for data analysis and clinical endpoints, but doesn’t specifically cover AI-driven target discovery or repurposing applications.[10] Licensing agreements should include audit rights allowing sponsors to inspect model architecture, version control requirements identifying which model version generated specific outputs, and cooperation obligations requiring AI vendors to participate in FDA meetings and provide validation documentation. Without these provisions, sponsors discover during IND preparation that they cannot adequately explain their AI-driven discovery to FDA, and the AI vendor has no contractual obligation to help.
Spin-Out Complications
When universities commercialize AI-assisted research through spin-out companies, determining how to allocate founder equity presents a significant challenge. Conventional equity frameworks operate on the premise that founders contributed either human intellectual effort or core foundational IP. However, where an AI platform accounts for a substantial portion of the underlying innovation—somewhere in the range of 30 to 40%—the question of how to appropriately recognize and quantify that contribution remains unresolved.
Emerging models treat AI vendors as stakeholders rather than mere service providers. Some agreements grant the AI vendor warrants or equity in spinouts, particularly when the AI platform’s contribution was essential to the core discovery. This creates complications: if the AI vendor holds 10-15% equity, and the university claims its standard 5-10% equity stake, founders may find their ownership diluted before the first institutional investor arrives.
Investors in AI-enhanced drug discovery companies now conduct dual diligence: scientific validation of the discovery itself and legal validation of the AI-related claims and dependencies. The questions investors now ask include: Can the company defend its AI-driven claims if challenged? What validation documentation exists to support the AI model’s accuracy? Does the company have sufficient rights to the AI platform to operate independently? Does the company own its background IP outright, or do partners retain rights that could limit future commercialization? How are improvements to background IP allocated?[11]
Partnership Red Flags and Term Sheet Provisions
Experienced dealmakers recognize warning signs that AI partnerships will create future litigation risk or commercialization barriers.
“Proprietary Algorithm” Opacity. AI vendors often claim their algorithms are proprietary trade secrets that cannot be disclosed. This is legitimate—but only if accompanied by validation documentation proving the model’s accuracy and reproducibility. Transparency doesn’t require disclosing source code. Vendors refusing to provide any validation documentation are asserting “trust us” as a business model, which is untenable when the partnership’s output will be submitted to FDA.
Indemnification Gaps. Standard software indemnification clauses may not adequately cover AI-specific risks. Agreements must explicitly address liability if the AI model was trained on improperly obtained data or PHI without adequate consent; responsibility if model outputs infringe third-party patents; and obligations if data breaches expose training data. Licensing best practices for healthcare startups emphasize the importance of indemnification provisions protecting against third-party IP infringement and data breach claims.[12] In AI contexts, this extends to claims arising from training data provenance issues, algorithmic bias that affects clinical trial outcomes, and regulatory non-compliance discovered during FDA review.
Audit Rights Limitations. Agreements must preserve the licensee’s ability to audit model training data, inspect version control logs, and understand retraining triggers. Data ownership and usage rights require explicit contractual definition, as the quality and provenance of data becomes increasingly critical in AI applications.[13]
Essential Term Sheet Provisions
Successful AI licensing agreements include:
Scope of Rights: Agreements must explicitly specify usage, modification, sublicensing, and distribution permissions.[14] For AI platforms, this includes explicit allocation of ownership for input data, training data, and output data, plus usage restrictions preventing vendors from training models on licensee data without consent.
Model Access and Validation Provisions: Licensees should have inspection rights allowing licensees to audit model architecture and validation documentation; version control requirements documenting which model version generated specific outputs for regulatory traceability.
Performance Warranties: Vendors should be required to warrant that the model meets defined benchmarks specifying model accuracy thresholds; representations that the model is free from known biases; and warranties that training data was lawfully sourced and collected with appropriate consent.
Regulatory Cooperation: Vendors should be obligated to actively support the licensee’s regulatory process—including attending FDA pre-IND meetings and supplying the documentation needed for regulatory submissions. They should also commit to maintaining complete and up-to-date validation records throughout the entire product development lifecycle.
Indemnification and Liability: Agreements should include broad indemnification protections covering the licensee against: third-party intellectual property infringement claims; data breach liabilities arising from the exposure of training data; regulatory enforcement actions resulting from model validation failures; and claims related to algorithmic bias or discriminatory outputs produced by the model.
Conclusion
AI has transformed drug discovery faster than deal structures can adapt. The partnerships, licenses, and spinouts being negotiated today are built on frameworks designed for human inventors, linear innovation pathways, and deterministic software. AI shatters all three assumptions.
The institutions that will succeed are those recognizing that AI licensing agreements negotiated during formation become stress-tested during investor due diligence, FDA review, and acquisition integration. Vague language around ownership, ambiguous improvement rights, and inadequate vendor obligations surface precisely when deal timelines compress and stakes escalate.
For tech transfer offices, business development teams, and early-stage executives, the message is clear: the AI partnerships you structure today determine whether your innovations scale smoothly or become case studies in what not to do. In dealmaking, as in drug discovery, specificity and foresight are competitive advantages.
Footnotes
[1]: 35 U.S.C. § 101; Beech Aircraft Corp. v. EDO Corp., 990 F.2d 1237, 1248 (Fed. Cir. 1993).
[2]: Thaler v. Vidal, 43 F.4th 1207 (Fed. Cir. 2022), cert. denied, 143 S. Ct. 1783 (2023).
[3]: 45 C.F.R. § 164.501; Cal. Civ. Code § 1798.140.
[4]: 35 U.S.C. §§ 200-212; 37 C.F.R. § 401.
[5]: Bryan Stewart & Kris Kappel, Who Owns the Platform? Device and Software Rights and Risks in Collaboration Structures, The Licensing Journal, Apr. 1, 2026.
[6]: Id.
[7]: Kris Kappel, Licensing Software and Intellectual Property: A Guide for Startup Healthcare Companies, The Licensing Journal, Jan. 1, 2026.
[8]: Rocher et al., “Estimating the success of re-identifications in incomplete datasets using generative models,” Nature Communications (2019) (noting that “just 15 demographic attributes can uniquely identify 99.98% of Americans, and genomic data is inherently far more identifying”). See also Kimberly Chew, Before AI Meets Your Biobank: Five Must-Do Steps for Research Institutions (2026), https://www.healthcarelawinsights.com/2026/04/before-ai-meets-your-biobank-five-must-do-steps-for-research-institutions/#_ftnref2
[9]: Cal. Civ. Code §§ 1798.100-199; Wash. Rev. Code §§ 19.373.010-900.
[10]: Kimberly Chew, Protocol Design and Recruitment Risks: AI’s Hidden Compliance Traps in Clinical Trials (2026).
[11]: Stewart & Kappel, supra note 5, at 1.
[12]: Kappel, supra note 7, at 2.
[13]: Stewart & Kappel, supra note 5, at 3.
[14]: Kappel, supra note 7, at 2.