/PRZWT/ For a long time, the pharmaceutical research and development industry has been trapped in the "double-ten predicament" - a new drug typically takes more than ten years to go from research to market launch, and requires an investment of up to ten billion US dollars. Even so, the overall success rate of new drug research and development worldwide still hovers at a low level of 2% to 15%.
In such an industry context, AI technology should have been the key force to break the deadlock. However, the reality is that AI has not yet fully transformed into a universal advanced productive force in the pharmaceutical field. Through long-term observation of numerous projects and implementation scenarios both at home and abroad, Beidou Digital Intelligence has discovered that it is not the single technical capability of AI that is insufficient, but rather there is a systematic imbalance in the entire R&D system in terms of data, tools, and collaboration mechanisms. These three structural gaps are like three huge stones, hindering the progress of the AI pharmaceutical industry.
A detailed analysis reveals that these structural gaps are specifically manifested in three aspects: Firstly, although biomedicine has entered the PB-level data era, there is a lack of unified standards for data, scarce high-quality data in key areas, and data has formed isolated islands due to governance chaos and compliance concerns, making it impossible to provide sufficient and reliable training support for AI models; Secondly, a large number of AI tools generated by technologies such as CADD and AIDD are fragmented and distributed, only able to solve local problems, and have high usage thresholds, with some models being in a "black box" state, making it difficult for them to work collaboratively; Thirdly, wet experiments are costly and time-consuming, and data cannot be timely structuredly returned to the computing layer, resulting in difficulties in forming a closed loop between AI predictions and experimental verification. Moreover, due to the shortage of interdisciplinary talents and the absence of a collaborative mechanism, the information transmission from "data - mechanism - decision" is not smooth, further restricting the improvement of research efficiency.
The solution: The Beidou Digital Intelligence Multi-Agent Collaborative Drug Research Platform emerges like a phoenix from the ashes.
Facing the industry predicament, NetEase Digital Intelligence has gone beyond the development of a single tool and innovatively proposed a new paradigm for AI drug development - a multi-agent collaborative drug research platform. Its core lies in reconfiguring data application, tool adaptation and collaboration mechanisms, upgrading AI from an "auxiliary tool" to a "chief intelligent agent scientist corps", and building an intelligent research system that can self-improve and evolve sustainably, helping the industry break through the bottleneck.
Complete the "treasure map", restore the "armory": The "calculation module" integrated platform lays the foundation
To address the bottlenecks of data and tools, Beidou Digital Intelligence has built an integrated common technology platform of "data, computing, modeling and application", which revitalizes research and development resources and lowers the usage threshold. The data layer relies on a trusted data space and standardized governance to integrate core data such as multi-omics and clinical data. By leveraging technologies like privacy computing, it breaks down data silos and creates a high-quality data resource pool, providing reliable "fuel" for AI models.
At the computing power layer, high-performance computing resources are integrated, and elastic allocation is achieved through intelligent scheduling to support intensive tasks such as large-scale molecular simulations and model training, thereby strengthening the computing power support. The model layer focuses on the AI4S field, building a specialized vertical model library, integrating general models and developing dedicated models, and optimizing the technology to meet the actual needs of research and development. The application layer encapsulates AI tools into standardized microservice components, supports visual workflow orchestration, forms a complete tool chain for the entire process, and enables the leap from "being available" to "being useful" for AI tools.
From "Signal Relay System" to "Intelligent Chain Collaboration": The Transformation of Research Paradigm through Multi-Agent Architecture
In response to the problem of the breakage in the R&D process loop, the multi-agent drug R&D platform built by Beidou Intelligence has overturned the traditional single-point, linear "fire tower" style collaboration, forming a self-driven, highly efficient R&D "intelligent chain", and establishing an "all-chief intelligent agent scientist corps" driven by AI, achieving parallel emergence. Its operation mechanism and unique advantages are reflected in three aspects.
First, professional collaboration: build specialized intelligent agents such as target discovery and molecular design throughout the entire R&D process, enabling automatic task distribution and real-time information interaction, integrating fragmented tools, and achieving seamless connection of R&D stages. Second, human-machine symbiosis: AI undertakes heavy tasks such as large-scale data processing and repetitive reasoning, freeing up the energy of researchers; researchers focus on core decision-making and innovative hypotheses, forming a new type of research form of "human-machine collaborative innovation". Third, self-evolution capability: through the closed loop of wet and dry experiments to promote model iteration, and carry dynamic knowledge graphs to absorb cutting-edge achievements, promoting R&D to evolve towards higher-order group intelligence, ensuring that the research direction aligns with the frontiers and avoids detours.
Furthermore, the dynamic knowledge graph continuously incorporates the latest scientific research achievements, providing the intelligent agents with reasoning and planning capabilities under knowledge constraints, promoting the development of the research network to evolve towards higher-order swarm intelligence, ensuring that the research direction always keeps up with the scientific frontiers and avoiding detours.
Practical Implementation: Collaborating with Research Institutions to Drive Industry Transformation
Currently, Beidou Digital Intelligence is collaborating with major research institutions to quickly implement the solution of the multi-agent collaborative drug research platform. The two parties jointly build a "computing and tool integration engine" (a standardized packaging suite of core biomedical models and tools), a "multi-agent execution and decision-making engine", and a "knowledge engine and swarm intelligence" (creating knowledge systems in areas such as vaccines and antibodies, integrating multi-source literature, experimental data, public databases and domain knowledge, and constructing a dynamic knowledge system with multi-hop reasoning and correlation) to promote the innovation of drug research methods through an integrated AI pharmaceutical common technology platform and multi-agent workflow. With the launch of the "multi-agent collaborative drug research platform", an AI-driven revolution in the drug research paradigm has already set sail.
