AI Pharma Goes Global 2.0: From Pipeline Licensing to Capability Export

China’s AI-driven drug development asset BD has officially entered the 2.0 era, with its business logic undergoing a profound qualitative shift: from single-product licensing to capability export, and from point-to-point collaborations to systems thinking and ecosystem synergy.

Recently, Xu Qian, Managing Partner and General Manager of Beijing at Danlu Capital; Du Tao, Founder and Chairman of Aegle Pharmaceuticals; Zhang Ying, Head of Business and Strategic Cooperation at XtalPi; and Zhang Ruiyao, Head of Life Sciences Business at Tencent Health, engaged in an insightful dialogue during the fourth session of the “Deal Roundtable” series under VCBeat’s “China Innovative Medical Assets Living Room.”

The fundamental shifts in AI-driven drug discovery asset business development are reflected across three dimensions: transaction types, collaboration models, and value orientation.

First, the subject of transactions has shifted from molecules to platforms.In the past, AI-driven drug discovery companies played a role similar to that of traditional biotech firms, with the core objective of selling AI-discovered candidate drug molecules or preclinical compounds, treating AI merely as an internal secret weapon and underlying technical support. However, today, counterparties—primarily multinational corporations (MNCs)—are more concerned with how AI-driven drug discovery companies translate their AI capabilities intoReusable R&D Platform for Both Parties, and build capabilities through collaboration.

Zhang Ruiyao, Head of Life Sciences at Tencent Health, added: “The overseas business development (BD) strategies of many AI-driven pharmaceutical companies have evolved from sporadic, single-asset licensing deals to systematic solutions spanning multi-target discovery to clinical candidate compounds, reflecting a trend toward risk-sharing and benefit-sharing.”

Second, the cooperative relationship has evolved from a supplier model into a strategic symbiosis.Zhang Ying, Head of Business and Strategic Partnerships at XtalPi, emphasized that the relationships formed through past one-off project deliveries were too loose, with cooperation ending once the project was completed. In contrast, business development (BD) in the 2.0 era—particularly with multinational corporations (MNCs)—is committed toEstablish deeply integrated, long-term collaborative ecosystem relationships, evolving from “one-off transactions” into a community of shared interests centered on value co-creation. This shift means that AI-driven drug discovery companies are no longer merely vendors in the traditional sense, but have become integral partners in systemic innovation, characterized by complementary capabilities and shared risk.

Drawing on his industry experience, Du Tao, Founder and Chairman of Aegle Pharma, pointed out the underlying reasons for this shift: early-stage business development (BD) often concluded with the delivery of key candidates, leaving subsequent validation and development primarily to the buyer. Today, however, AI companies must possess the capability to “tell their partners what they can do next to support them,” thereby advancing long-term, multi-dimensional value creation.

Xu Qian, founding partner of Danlu Capital, also cautioned that while equity transactions and the Newco model are injecting capital and clinical resources into biotech firms, they also impose stringent requirements on management capabilities and overseas multi-center operational competence. This shift essentially represents a paradigm transition from “early-stage R&D-driven” to “mature, end-to-end operational” models, propelling domestic biotech companies into a prolonged period of capability building.

Third,Valuation assessment is shifting from transaction amounts to the construction of data barriers, with the compounding effect of data gradually becoming the core logic of the valuation system.While short-term economic benefits are certainly important, true value is increasingly reflected in how collaboration generates high-quality data, facilitates algorithm iteration, and strengthens the technological moats of both parties through long-term interactions. Zhang Ying explicitly pointed out, “How to leverage the core barriers of AI-driven drug discovery to generate high-quality, high-dimensional proprietary data and continuously optimize the algorithmic closed loop is, in fact, a more critical aspect of client communication and long-term partnership.”

Furthermore, data generated from each collaboration will serve as fuel for AI models. The more collaborations there are, the stronger the capabilities of the automation platform become, thereby deepening the competitive moat between the two parties. “A diversified business development (BD) model centered on capabilities will bring more sustainable and stable cash flow—by engaging in multi-project collaborations with different partners, we can share the high costs of model R&D and computing power, establishing a business model capable of continuous self-sustaining revenue generation,” added Zhang Ruiyao.Therefore, while business development (BD) efforts in the past focused more on upfront payments and milestone amounts for individual drugs, greater emphasis is now placed on platform efficiency, technology premium, and the potential for long-term collaboration.

Focusing on practical realities, the primary challenge facing Chinese AI-driven drug discovery companies in their global expansion is not technological innovation itself, but ratherBuilding the Bridge of Trust。

“Establishing initial trust with tangible, verifiable data is the most effective way to break through the wait-and-see attitude of multinational corporations (MNCs),” said Du Tao. Whether through pilot projects, historical success stories, or demonstrating the capability of automated platforms to generate real-time data, “letting partners see it with their own eyes” is more persuasive than any business presentation.

Key Strategy 1: Understand and Integrate, Rather Than Directly Disrupt.Zhang Ying specifically noted in her sharing: “XtalPi aims to be a technology enabler, integrating with partners through AI, rather than immediately touting ‘using AI to disrupt any industry.’ The premise of collaboration is to focus on core needs and tangible value, such as cost savings, time efficiency, risk reduction, and improved success rates.” In particular, collaborations with MNCs requireDemonstrate respect for and understanding of industry regulations, including GMP, R&D processes, data standards, and global regulatory requirements.

Key Strategy 2: Partner with Leading Tech Giants to Lower the Trust Barrier.Tencent Health’s practices offer a unique case study—Zhang Ruiyao stated that Chinese AI biotech companies expanding overseas face three major pain points: high computing power costs, challenges in data compliance, and a lack of trust abroad. Tencent Cloud, throughCredit Enhancement, Resource Cost Reduction, Scenario AccelerationThree Key PointsSystematically lower the barriers to business development, striving to become a “partner” for AI companies expanding overseas.

Taking the pain point of high-cost computing power as an example, Tencent Cloud can empower AI enterprises with elastic computing capabilities based on overseas infrastructure certified by international standards such as HIPAA and GDPR. By inviting experts from multinational corporations (MNCs) to log in remotely and monitor model operations in real time, it not only ensures the security of cross-border data and the stability of computing power but also leverages its own team of engineers and scientists to help biotech companies achieve collaborative optimization in algorithms, computing power, and architecture. “With the endorsement of a major tech company and a hands-on operational model, biotech firms can gain the trust of MNCs more quickly, alleviating their concerns regarding data de-identification, cross-border security, and regulatory compliance.”

Key Strategy 3: Prioritize Scientific Research to Establish Discourse Power.Tencent’s AI for Life Sciences Laboratory collaborates with top-tier global research institutions, continuously publishing papers in leading journals. Zhang Ruiyao explained this strategy: “The independent life sciences research division is a heavily invested segment that operates without performance pressure and benchmarks against the global frontier. Through”Research First, continuously updating and iterating to maintain international leadership in AI-driven life sciences. Secondly, we engage in long-term collaborations with leading global research institutions, MNCs, and external consulting experts, fostering mutual understanding, awareness, and brand building through exchange.”

The current logic of AI-driven drug development has shifted from a product-sales model to an “operating system” mindset. Consequently, the structure of deal-making is undergoing a fundamental transformation.

Key Strategy 4: Accelerating Interest Alignment Through a Milestone Model.Compared with the traditional approach where multinational corporations (MNCs) bore sunk costs and made upfront risk-based payments, the milestone-and-reward model is gradually maturing in AI-driven drug discovery business development (BD). Zhang Ying revealed that in a collaboration between XtalPi and an MNC during the first half of the year, a hybrid model comprising “capability subscription + risk sharing + exclusive rights for joint development” was adopted. Essentially, this new model links a portion of the payments to actual progress and establishes incentive mechanisms for “early achievement of milestones.” This designMake the value of AI-driven acceleration explicit and deeply align the interests of both parties.

Key Strategy 5: Upgrading Deliverables from Reports to a Closed-Loop Product.forDispelling Concerns Over the AI “Black Box”: XtalPi Proposes a Computation–Experiment–Feedback Closed-Loop Delivery Model—MNCs Receive Not Only Static Data Reports, but Also Physically Verified Samples and Experimental Data Validated by an Automated Platform; Deliverables Can Directly Enter the Next Stage of Development, Significantly Enhancing Value Density and Creating “Tangible Certainty.” Du Tao noted that, under this model, AI technology and products have developed a symbiotic relationship. Biotech companies must provide continuous, full-lifecycle support, co-evolving with products to deliver long-term empowerment. This companion-style collaborative model represents a novel paradigm never seen before, posing greater challenges to startup teams’ comprehensive capabilities in technology integration, resource allocation, and sustained responsiveness.

As the business development (BD) boom gains momentum, diverse models such as NewCo and Co-Co are emerging, placing higher demands on startup teams’ management capabilities and multi-threaded operational expertise; however, these models are unlikely to become mainstream in the short term. Xu Qian noted that by looking beyond the hype of technology and the complexity of commercial proposals to focus directly on core value, AI drug discovery BD projects share four common evaluation dimensions:

I.Technology and Data Validation:Model Capabilities and Uniqueness: Is the AI model a single-step tool or a cross-target platform? Has a closed-loop system integrating wet-lab and dry-lab experiments been established? Are the training data exclusive, large-scale, high-quality, and iterable?

II.Clinical and Commercial Prospects:Does it address significant unmet clinical needs? Is the target a validated “star target” or a promising yet challenging “undruggable target”? Can AI overcome bottlenecks that traditional methods have failed to resolve?

III.BD Transaction Structure:Is the combination of upfront payment, milestones, and sales royalties reasonable? Does it include equity collaboration? Are the territorial rights and co-development clauses clear?

IV.Team and Execution Capabilities:Does the team possess a multidisciplinary background spanning AI R&D, drug discovery, clinical development, and commercialization? Do the founders have a proven track record of success? Is the strategic focus on proprietary pipelines or platform services?

As AI-driven drug discovery sheds the label of merely selling pipelines and shifts toward delivering continuously evolving capabilities, China’s innovative pharmaceutical industry is crafting a new narrative for global expansion—moving from the 1.0 era of boutique-style product sales to the 2.0 era of licensing foundational capabilities, exporting technical standards, and building an innovation ecosystem. This transformation tests not only technological depth but also the understanding of global pharmaceutical regulations and the ability to provide full-lifecycle empowerment to ecosystem partners.

Amidst the stark contrast between the capital winter and the BD boom, enterprises that can demonstrate value to partners, generate compound returns from data, and ensure traceable trust will ultimately find their place in the global pharmaceutical landscape.

Roundtable Topic: Breaking Through the 10-Billion Mark! Deep Dive into the Logic and Investment Value of Radiopharmaceutical Deals

Roundtable Panelists: Representatives from Guotong New Drug, Jinghe Biotech, Quark Medical, Cold Fir Capital, Yifeng Capital, and Xinghua Dingli

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