Shaking Hands with AI? – AI in Deals and Funding

In a synergistic way, the AI systems may substitute for
their human counterparts for a lot of the underlying
labor that informs deal-making but, for now at least, they cannot be held “accountable” nor “responsible” for their work and outcomes.

Licensing agreements and capital raising are as critical for biopharma and biotech companies as their inventions themselves. These two core business functions enable pipeline expansion, funding R&D, and facilitate commercialization. What can AI offer here? It is increasingly being applied to optimize these processes, offering tools to identify opportunities, support valuations, analyze agreements, target investors, and refine funding narratives. Ultimately, in a synergistic way, the AI systems may substitute for their human counterparts for a lot of the underlying labor that informs deal-making but, for now at least, they cannot be held “accountable” nor “responsible” for their work and outcomes.

Optimizing Licensing Activities with AI

AI can enhance both in-licensing (acquiring rights to external assets) and out-licensing (granting rights to internal assets) activities:

Opportunity Identification

AI platforms can systematically scan vast, diverse datasets – from patent databases, scientific literature, clinical trial registries, conference abstracts, to company pipelines – to identify promising assets for in-licensing or potential partners interested in out-licensing specific technologies or compounds.21
Platforms like Partex AI, used by Fortress Biotech 27, or Vibe Bio’s VibeOne 77 are designed specifically for this purpose, automating the search and initial evaluation process.

Valuation and Forecasting

Accurately valuing licensing deals is complex. AI and machine learning models can support this by incorporating predictive analytics to estimate the probabilities of clinical success, forecast market potential and revenue streams, and project potential royalty payments.28
Poorly executed, the confidence intervals will be so wide as to be worthless! Analyzing comparable deals and market trends using AI can also inform valuation benchmarks.
The high valuation multiples observed in AI-related M&A deals suggest a market premium for assets developed using AI or for AI platform technologies themselves.79
AI can help model different deal structures, considering factors like upfront payments versus milestone payments, a trend noted in recent licensing activity where upfronts have stabilized at lower percentages compared to historical peaks.80

Agreement Analysis

Legal NLP tools can significantly accelerate the review process by automatically analyzing agreements to extract key terms (e.g., scope of license, territory, field of use, exclusivity, payment terms, milestones, termination clauses), identify potential risks or ambiguities, ensure consistency across documents, and facilitate compliance checks.29
This allows legal and business development teams to focus on negotiation and strategic considerations rather than manual document review, representing a significant potential efficiency gain.

Streamlining Capital Raising via AI

Securing funding is a critical challenge, especially for early-stage biotech companies. AI offers potential tools to make this process more efficient & effective:

Investor Targeting

AI models can be created to analyze extensive data on venture capital (VC) firms, corporate venture arms, and other investment entities, including their historical investments, stated areas of interest, portfolio company profiles, fund size, investment stage preference, and even personnel backgrounds, to identify and prioritize the most suitable investors for a specific company’s stage, technology, and therapeutic area.19 This allows fundraising teams to focus their outreach efforts more effectively.

Narrative Refinement

crafting a compelling investment story is key to attracting capital. Generative AI could potentially assist in refining pitch decks, executive summaries, and other investor communications by analyzing successful pitches, tailoring language to specific investor profiles, incorporating relevant market data and trends, and ensuring clarity and consistency in messaging.26 The success of companies like Insilico Medicine in raising substantial funds ($110M Series E) underscores the power of a strong narrative centered around
AI innovation.

Valuation and Financial Projection Support

AI-driven predictive analytics can potentially support the financial models and projections presented to investors by providing data-backed forecasts for market size, adoption rates, clinical trial timelines, or success probabilities.87 However, it is crucial that these AI-generated projections are transparent, auditable, and rigorously validated, as regulators like the SEC are increasingly scrutinizing the reasonableness and disclosure surrounding AI-driven financial reporting and valuations in the
biotech sector.

The application of AI in deal-making and fundraising creates a dual effect. While it equips companies seeking deals or capital with powerful tools for identification, valuation, and targeting, it simultaneously introduces a new layer of scrutiny. Investors and potential partners must now conduct due diligence not only on the science and business plan but also on the AI technologies employed by the target company – assessing the

This necessitates a deeper technical understanding during the evaluation process.

No matter how perfectly crafted the AI model may be, it cannot replace the fundamental need for a compelling strategic vision, strong leadership, and transparent, rigorously prepared financial data. Investor confidence ultimately rests on the credibility of the team and the science, supported, but not solely driven, by AI tools.

Activity	Relevant AI Techniques	Key Benefits	Key Challenges
Licensing Opportunity ID	NLP, ML, knowledge graphs	Efficiency, broader search, identifying hidden gems ²⁷	Data access/completeness, defining search criteria
Licensing Valuation/Forecasting	Predictive Analytics, ML	Data-driven valuation, success probability modeling, revenue forecasting ⁵¹	Model explainability, forecasting uncertainty, market volatility ⁷⁹
Licensing Agreement Analysis	Legal NLP	Speed, accuracy, risk identification, consistency checking ²⁹	Nuance interpretation, legal complexity, model training data
Investor Targeting	ML, Data Analytics	Precision targeting, efficiency, identifying non-obvious investors ⁸⁵	Investor data privacy, dynamic nature of VC focus
Investment Narrative Support	Generative AI, NLP	Message tailoring, market positioning, consistency ²⁶	Authenticity, avoiding hype, demonstrating substance
Financial Projections for Funding	Predictive Analytics, ML	Data-backed forecasts, scenario modeling ⁸⁷	Model validation, transparency, regulatory scrutiny, auditability ⁸⁷