Notice of the Conference on 'AI-Empowered Innovative Technology Applications Across the Entire Drug Discovery Process' — Talent Exchange and Development Center, Chinese Academy of Sciences

To relevant organizations: Artificial intelligence (AI) is no longer merely an auxiliary tool in drug discovery—it has become the core engine driving innovation in biomedicine. Traditional drug development models face persistent bottlenecks, rendering new drug development a high-risk, high-investment, and protracted endeavor. The rapid advancement of AI technologies has brought unprecedented, disruptive change to this field, fundamentally reshaping the entire value chain of drug discovery. To accelerate AI’s deep empowerment of drug discovery and enhance comprehensive, real-world application capabilities across all scenarios, our Center will host the second advanced training program titled “AI-Empowered Innovative Technology Applications Across the Entire Drug Discovery Process” in Beijing from September 26–28. Renowned senior experts from institutes of the Chinese Academy of Sciences and prestigious universities will deliver hands-on training on practical AI applications in scientific research. The specific arrangements are as follows: I. Theme and Content (A) The AI-Driven Drug Revolution: AI Reshaping New Paradigms in Biomedical Research 1. Trends and challenges in innovative drug discovery 2. Analysis of the global competitive landscape and China’s market positioning 3. Emerging drug discovery paradigms in the AI era 4. Core AI technologies and scientific breakthroughs empowering drug discovery (B) Breaking Through Bottlenecks: Pathways to Data Assetization Enabled by AI 1. Frameworks for data acquisition, governance, and standardization 2. Analysis of representative domestic and international databases 3. AI-enabled data mining and multi-omics data integration 4. Cross-institutional data collaboration under privacy constraints and federated learning 5. Explainable AI (XAI) addressing the “black-box” challenge in AI-driven drug discovery (C) Key Technical Applications of AI in Target Identification and Validation 1. Application of deep learning in target identification 2. Construction and analysis methods for biological networks 3. Innovations in target druggability assessment and validation systems 4. High-throughput screening (HTS) for identifying small-molecule lead compounds 5. Case studies of AI-powered target discovery (D) Practical Innovations in AI-Driven Molecular Design and Optimization 1. Generative adversarial networks (GANs) for de novo drug molecule generation 2. Reinforcement learning for optimizing molecular structures 3. Multi-dimensional strategies in molecular generation technologies 4. Automated design and optimization of multi-target drug molecules (E) AI-Assisted Indication Selection and Developability Assessment 1. Virtual screening and machine learning–based prediction of compound activity 2. Core parameters for developability assessment and ADMET property prediction

How can the pharmaceutical industry seize opportunities amid AI-accelerated drug development?

Talent Exchange and Development Center, Chinese Academy of Sciences Notice on Hosting the Advanced Training Program on “AI-Empowered End-to-End Applications and Hands-On Tool Practice in Drug Discovery” To relevant organizations: Biopharmaceuticals constitute a strategic industry vital to national economic welfare and security—and serve as a critical pillar for building a Healthy China. It is also a core arena in global science-and-technology competition. To thoroughly implement the state’s major strategic initiative of “AI+”, align closely with the National Medical Products Administration’s “Opinions on ‘AI + Pharmaceutical Regulation’” and the “Pharmaceutical Industry Digital-Intelligent Transformation Implementation Plan (2025–2030)” jointly issued by the Ministry of Industry and Information Technology and six other departments, and to build an integrated platform fostering deep collaboration among academia, research institutes, industry, and application sectors—while cultivating interdisciplinary innovative talent for pharmaceutical R&D—our Center will hold the advanced training program “AI-Empowered End-to-End Applications and Hands-On Tool Practice in Drug Discovery” at the end of June. Invited experts from relevant institutes of the Chinese Academy of Sciences and leading domestic pharmaceutical R&D institutions will deliver specialized training focused on practical AI applications and hands-on skill development in drug research. The specific arrangements are as follows: I. Theme and Content (Three-Day Curriculum, Eight Modules) (A) Cutting-Edge AI Technologies Empowering Biomedicine 1. Foundational logic and technical overview of AI in drug discovery 2. Frontier AI technologies and proven implementation outcomes in drug discovery 3. Core application scenarios of AI in drug discovery 4. Classification and selection criteria for commonly used AI tools in drug discovery (B) Data Requirements and Leading Databases for AI-Driven Drug Discovery 1. Data acquisition, governance, and standardization 2. Analysis of representative domestic and international databases for drug discovery 3. AI-enabled data mining and multi-omics data integration 4. Core data types and preprocessing methods in AI-driven drug discovery 5. AI-assisted data cleaning and visualization for drug discovery (C) AI-Empowered Targeted Drug Design and Virtual Screening Technologies 1. Technical pathways for AI-empowered drug design 2. Hands-on practice of AI-assisted targeted drug design 3. AI applications in phenotypic drug design 4. Practical use of generative AI models in drug design 5. AI applications in antibody design and antibody prediction 6. Cell-image-based gene–compound matching for virtual screening 7. Cross-scale multi-omics integration and spatiotemporal dynamic modeling (D) Practical Innovations in AI-Driven Molecular Design and Optimization 1. High-throughput screening (HTS) for identifying small-molecule lead compounds 2. Hands-on practice of de novo molecular design using AI 3. Technical applications of automated multi-target drug molecule design 4. Generative AI–assisted structural optimization of drug molecules 5. AI-empowered functional prediction of large-molecule therapeutic candidates 6. Molecular dynamics simulation and virtual screening platforms (E) AI-Assisted Indication Selection and Developability Assessment 1. Core parameters for developability assessment and ADMET property prediction 2. Human pharmacodynamic (PD) and pharmacokinetic (PK) modeling–based efficacy prediction 3. Dynamic optimization loops and developability evaluation 4. AI applications in targeted anticancer drug delivery 5. Co-building integrated dry–wet experimental platforms powered by large AI models (F) AI Applications in Preclinical Research

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