Insilico Pilots the Automated AI-driven Partnering System for Biotechnology Assets and AI Platforms
Insilico Medicine (“Insilico”, 3696.HK), a clinical-stage biotechnology company driven by generative artificial intelligence (AI), today announced the pilot launch of its Automated AI-Driven Partnering System. This first of its kind business development automation platform is designed to help biotechnology companies partner more efficiently, allowing them to increase engagement, manage due diligence and operate their pipelines on a greater scale. The new system expands Insilico’s applied AI capabilities and introduces an infrastructure that can utilize partnering decks and publications, respond to scientific or diligence questions, support data room management, and streamline standard business development activities end to end.
Solving Business Developments’ Greatest Challenges:
Traditional biotechnology business development is limited and constrained by two key factors: manual team processes and limited pipelines. First, biotechnology companies regularly operate with one or two team members managing outreach, presentations, data rooms, and due diligence cycles, which often makes it difficult to scale. With the revolution of efficient AI transforming these traditional practices, it has become possible to significantly increase throughput and enhance the quality and speed of communication across teams. Second, biotechnology companies have historically managed concise pipelines of two or three assets at a time, making for manual business development processes to be somewhat manageable. However, with the advent of efficient generative AI, this has fundamentally changed the scale at which innovative biotech pipelines can operate. Insilico now advances more than 40 internal programs across multiple therapeutic areas, each supported by experimental data, publications, computational analyses, and development strategies. This level of scale requires a new category of automated business development infrastructure, one capable of organizing and navigating large volumes of information and supporting high quality scientific communication.
Insilico’s Automated Partnering System was created to address these challenges by integrating data from the company’s proprietary therapeutic programs and Pharma.AI platforms with multi-agent architectures that automate the entire business development workflow.
The Platform:
The Automated AI-Driven Partnering System is a digital collaboration platform that streamlines interactions between innovators and potential partners, including pharmaceutical companies, biotech investors, and platform subscribers. It makes proprietary therapeutic assets and AI platforms discoverable, showcases available non-confidential materials, provides context-rich AI-assisted interactions for rapid due diligence, and facilitates meeting and materials requests to accelerate partnerships.
The system serves as an intelligent organizational and reasoning engine that can interpret partnering decks, publications, background documents, and internal technical files. It identifies which materials match specific partner questions, retrieves information quickly, summarizes scientific content, and maintains consistent communication across all assets. By tracking updates across documents and keeping data room materials aligned, it reduces the inconsistencies that often arise when multiple programs advance in parallel.
A core strength of the system is its ability to operate across many assets simultaneously. It maintains a structured understanding of each program's stage, data package, target, modality, and competitive context. This enables navigation between assets without manual lookup, increasing both the throughput and quality of partner support for organizations managing larger pipelines.
Another major advantage is its conversational capability. The system conducts multi turn AI assisted Q&A grounded in internal materials. It can interpret terminology, clarify mechanisms of action, explain target biology, summarize preclinical evidence, and contextualize competitive data, while flagging questions that require human review. Inline citations, images, and feedback loops reinforce transparency and accuracy over time.
Although it does not replace relationship driven business development, it automates a substantial share of the routine informational flow and speeds partner decision making.
Insilico Medicines Generative AI Advancements:
Harnessing state-of-the-art AI and automation technologies, Insilico has significantly improved the efficiency of preclinical drug development. While traditional early-stage drug discovery typically requires 3 to 6 years, from 2021 to 2024 Insilico nominated 20 preclinical candidates, achieving an average turnaround - from project initiation to preclinical candidate (PCC) nomination - of just 12 to 18 months per program, with only 60 to 200 molecules synthesized and tested in each program.
Insilico has published extensively in leading peer reviewed journals on AI driven target discovery, generative chemistry, clinical trial prediction, and disease modeling. These publications include landmark papers describing PandaOmics for target discovery, Chemistry42 for molecule generation, and Generative Biologics design, as well as the InClinico platform for forecasting clinical trial outcomes.
Since its inception, Insilico has published over 200 peer-reviewed papers, including six publications in the Nature portfolios since 2024. These papers highlight advancements in AI-driven programs that have demonstrated success in both preclinical or clinical validation:
This deep library of publications and data allows the Automated Partnering System to ground its reasoning in validated scientific evidence.
Future Platform Applications:
As the industry moves toward an increasing use of AI, Insilico is pioneering a future where early-stage business development dialogue can be more efficient and scalable. The Automated Partnering System supports controlled agent to agent information exchange, enabling non-confidential, structured dialogue between AI systems representing different organizations. While industry adoption is still nascent, this architecture has the potential to significantly speed up early evaluation and opportunity filtering.
The long-term vision for the platform is to support the automation of many operational responsibilities traditionally carried by Business Leaders, including document coordination, preparation of structured summaries, routing of questions, organization of data rooms, maintaining consistency across materials, and portfolio level reasoning. High level negotiations, strategic alignment, and relationship building will continue to require human leadership, but much of the operational and informational workload can be efficiently augmented with AI.
"In the long run, I would like AI to replace me in the position of Chief Business Officer where only the necessary relationship oriented in person interactions remain and most other tasks are automated in the most efficient and frictionless way. At present, the pharmaceutical industry is not utilizing AI in business development outside very mundane tasks such as competitive intelligence and simple reasoning, and we do not expect this to change overnight. When I am offering agent to agent engagements even in non-confidential discussions, most parties still find it surprising and sometimes uncomfortable”, stated Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine, “However, it is important to invest in this area and develop tools that allow business development to scale. Biotechnology companies historically advanced only a few assets and never invested enough in BD infrastructure, while AI enables scale and quality improvements. With more than thirty internal programs, Insilico must operate BD at a scale that traditional approaches simply cannot support. The Automated Partnering System represents an important step in that direction."
Insilico intends to expand the system further. Future development areas may include integration with clinical trial outcome prediction to evaluate partnering readiness or deal probability, automated landscape mapping, multi-language partner engagement, improved scientific narrative alignment across assets, and enhanced reasoning for regulatory and clinical strategy questions. As pipeline sizes continue to increase across the AI driven biotech sector, the company expects automated business development systems to become essential infrastructure for scaling partnerships efficiently.
Insilico has achieved AI-driven drug discovery collaborations with companies including Fosun Pharma, Sanofi, and Eli Lilly, and has delivered R&D milestones across multiple collaborations. By integrating advanced AI and automation technologies, Insilico has significantly improved the efficiency of early-stage drug development in real-world practices, setting a benchmark forAI-driven drug discovery. Whereas traditional early-stage drug discovery typically requires 2.5 to 4 years, more than 20+ of Insilico’s internal programs initiated between 2021 and 2024 achieved PCC nomination in just 12 to 18 months on average, with only about 60–200 molecules synthesized and tested per program.