Intelligent Computing for New Drug Discovery: DeepModel Leads the Way | CIAPH Visits DeepModel Technology in Shanghai

At the outset of the event,Li Xiarong, Senior Director of Life Science Solutions at DP TechnologyDelivering the welcome address, he pointed outWithAI for Science, with technological iterations such as multi-agent systems, the pharmaceutical industry is accelerating its transition from experience-driven to intelligence-driven,AIComprehensive penetration across the entire industry chain, including drug R&D, corporate operations, and commercial compliance. DP Technology looks forward to collaborating with industry partners to leverage intelligent computing technologies in solving pharmaceutical R&D challenges and co-creating a new industrial ecosystem.

During the thematic sharing session, multiple industry experts shared insights based on real-world case studies.AIImplementation Achievements and Transformation Strategies in Core Pharmaceutical Business.

Li Xiarong, Senior Director of Life Sciences Solutions at DP Technology

Li Xiarong, Senior Director of Life Sciences Solutions at DP Technology, in “AI for ScienceIn his keynote speech on the theme of “AI-Driven Digital Intelligence Platform for Life Sciences R&D,” he pointed out that DP Technology, as a globalAI for Science As a pioneer and leader, leveraging years of in-depth expertise in the field and a profound understanding of the industry, we have built a capability system centered on the “Bohr Scientific Research Space Station,” encompassing “Reading, Computing, Doing, and Intelligence,” and createdScience as a ServiceIntelligent Research Product and Service Matrix: Bohr Science Navigation, Bohr Lebesgue Intelligent Computing, andHermite®、Piloteye®and other micro-scale R&D software, Bohr Transition Laboratory,SciMasterScientific AI Agents, along with “large-scale scientific infrastructure” and R&D services oriented toward scientific discovery, provide comprehensive and flexible solutions for scientists and R&D organizations in the fields of basic research, life sciences, and physical sciences.The platform integrates products and services such as literature retrieval, intelligent computing, automated experimentation, and scientific AI agents, streamlining the entire drug discovery process. It drives the iterative upgrade of traditional research models and facilitates the industry’s transition toward a standardized, industrialized, and self-evolving intelligent R&D paradigm.

Zhu Feng, General Manager of Digital Intelligence Innovation and Chief Digital Intelligence Officer at Henlius

Zhu Feng, General Manager of Digital Intelligence Innovation and Chief Digital Intelligence Officer at Henlius, shared Henlius’sAIEnd-to-end development experience. He provided an in-depth interpretation of the company’sAIStrategy, and interpreted it from three aspects:Al for ScienceDrug R&D, Algorithm-Driven Core Capability Evolution,Al for ProductivityHierarchical Empowerment System and Exploration of “Digital Employees”Al for ExcellenceAll-Staff Evolution and ConstructionAIFoster an innovation-native ecosystem culture. During the presentation, it was demonstrated thatHAI ClubMolecular Design Platform,HAb VisionDrug-likeness prediction tools,AIThe effectiveness of in-house developed systems such as the Process Brain. Notably, the dual-target affinity maturation project took only a few months from initiation to molecular output, clearly demonstratingAIA significant boost to R&D efficiency.

Zheng Xing, Chief Engineer of Life Sciences R&D at DP Technology

Zheng Xing, Chief Engineer of Life Sciences R&D at DP Technology, gave a live demonstrationPharmMasterDrug DesignAIIntelligent Agent Platform. In traditional R&D models, manually chaining various tools often leads to significant efficiency losses. This platform intelligently transforms self-developed tools and consolidates vast industry expertise into a skill library, enablingAICapable of independent operation. In the hit compound discovery scenario, the platform will originally3-month R&D cycle compressed to2Zhou, the experimental hit rate outperforms manual methods. The platform has currently been builtAIDesign-Simulation Calculation-Automated Wet Lab-A closed-loop system for data feedback, with continuous iterative optimization, empowers pharmaceutical companies to strengthen their original innovation capabilities.

Nona Bioscience CAIO Miao Hongjiang

Nona BiologicsCAIOMiao Hongjiang shared generativeAIPractical Applications in Antibody and Molecular Discovery, Engineering, and Development. The company leverages globally leading fully humanHCAbAntibody Platform, BuildingHu-mAtrixEnd-to-EndAISystem, generative integrationAIIntegrated with automated wet-lab capabilities to achieve de novo antibody design and intelligent optimization of drug developability. Implementation cases demonstrate that,AIThe generated candidate molecules demonstrate significant advantages in affinity, manufacturability, and functionality, effectively accelerating the overall progression of projects from target research to clinical trial application.

PharmaceuticalsAILarge-scale application relies on foundational capabilities such as data empowerment, security and compliance, data governance, and human-machine collaboration. Service providers including Zhongkang Technology, Yige Cloud, Siemens, ShadowBot, and Qincheng Information offer solutions in areas such as office security, technical architecture, and process automation,AIsharing solutions across dimensions such as governance,Safeguarding the intelligent implementation for pharmaceutical companies.

Zhongkang Technology AIService Manager: An Shulan

Zhongkang TechnologyAIFocus with Service Lead An ShulanAIMulti-Agent Systems in New Drug Project Initiation andBDDecisionScenario. She combines5W1HMethodology: Explaining Project Initiation in Pharmaceutical Companies andBDThe core logic of work, and propose pharmaceuticalAIThe product must adhere to three core capabilities: data traceability, workflow integration, and compliance security. Leveraging eighteen years of industry data accumulation, Zhongkang's AI agent constructs a comprehensive pharmaceutical knowledge graph, covering massive amounts of information on targets, clinical trials, patents, transactions, and more. It can perform tasks such as target assessment, competitor analysis, and project valuation, providing pharmaceutical companies withBDSupports scientific decision-making for transactions and new drug project initiation.

Zhang Yan, Solutions Expert at Yige Cloud

Zhang Yan, a solutions expert at Yige Cloud, shared insights tailored to the pharmaceutical industry’s characteristics of high data sensitivity and stringent regulatory requirements. Scenarios such as hybrid work, cross-border operations, and personnel mobility continue to exacerbate the risk of data breaches. Yige Cloud, based onSASEArchitecture and Zero Trust Framework, IntegratedAIBehavioral analysis and data loss prevention technologies to enable "human employees+“Digital Employee” Full Lifecycle Management: Establishing Mature Solutions in R&D Data Protection and Departure Risk Control to Fortify Corporate Data Security Defenses.

Siemens Digital Industries SoftwareIntelligence Center XTechnical Director, Ruan Ming

Siemens Digital Industries SoftwareIntelligence Center XTechnical Director Ruan Ming focused onAIDDProposing solutions to the industry’s “Double-Ten Dilemma.” He pointed out that the current efficiency bottlenecks in the industry are more pronounced, and Siemens is achieving comprehensive acceleration across three key dimensions: throughSLCCompletedSASSystem substitution to reduce data processing costs; leveragingRapidMinerLow-Code Platform, ReduceAIModeling Threshold; LeveragingGraph StudioKnowledge Graph: Bridging Fragmented Data.FDAImplementation cases at such institutions fully demonstrate the practical value of knowledge graphs in the fields of regulation and compliance.

Yingdao After-Sales Director & Partner, Qian Wen

Qian Wen, Head of After-Sales and Partner at Yingdao, pointed out that the pharmaceutical industryAIApplications are reaching a critical watershed: industry development is shifting from mere useAITools, shift to letAIDeeply embedded in the position, undertaking specific tasks. Relying onRPA+AIThe digital employees created can be implemented in high-frequency, repetitive scenarios such as marketing, supply chain, and finance, achieving a combination of process automation and intelligent decision-making to help enterprises build an efficient human-machine collaboration model.

Qincheng Information NextAIProduct Manager Tong Zhenhang

Qincheng InformationNextAIProduct Manager Tong Zhenhang focuses on the core needs of high compliance and high traceability in the pharmaceutical industry. Enterprises throughDockGovernance Platform Achieves Multi-AI Agentaccess control and full-process auditing, while establishing complianceRAGKnowledge Base, targeting drug package inserts,SOPCore materials are subject to the management rule of “search-only, no rewriting, approval allowed,” enabling pharmaceuticalAIUpgrading from “available” to “controllable and trustworthy.”

Following the sharing session, the attendees visited DP Technology’s Shanghai office, touring its exhibition hall and intelligent laboratories. They learned about DP Technology’s rapid development history and conducted on-site visits to facilities such as the intelligent computing platform and automated experimental equipment, gaining an intuitive understandingAIThe Outcome of the Deep Integration of Technology and Medical Research.

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