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        • 4.2 Surgical AI
        • 4.3 Diagnostic Imaging AI
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        • 4.7 Public & Population Health
        • 4.8 Hospital System Operations
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        • 5.2 Grant & Funding Opportunities
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        • 5.5 Research Methodologies & Tools
        • 5.6 Education & Workforce development
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    Group logo of Drug Discovery and Development
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    • Group photo of AI RESEARCH AND DEVELOPMENT
    AI RESEARCH AND DEVELOPMENT Drug Discovery and Development

    Drug Discovery and Development

    Public Research

    Public Research

    Active 6 months ago

    Purpose: To explore the use of AI in pharmaceutical research and drug development... View more

    Public Research

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    • Profile photo of AIiHC

    Group Description

    Purpose: To explore the use of AI in pharmaceutical research and drug development processes.

    Description: This subgroup is dedicated to discussing AI-driven approaches in various stages of drug discovery and development, from target identification to clinical trials.

    Intended Use:
    – Discussing AI-driven approaches to drug target identification
    – Exploring machine learning in molecular design and optimization
    – Sharing research on AI’s role in accelerating clinical trials

    Limitations:
    – Not for discussing general AI concepts unrelated to drug discovery
    – Not for sharing proprietary or confidential pharmaceutical information

    Key Activities:
    – Analyzing case studies of successful AI applications in drug discovery
    – Discussing challenges in integrating AI into traditional drug development pipelines
    – Exploring the potential of AI in repurposing existing drugs for new indications

    Potential Users:
    – Researchers (in pharmaceutical and biotech fields)
    – Data Scientists
    – Clinicians (involved in clinical trials)
    – Entrepreneurs (in pharma AI startups)

    Possible Discussions:
    – “AI-powered platforms for predicting drug-target interactions: a comparative analysis”
    – “Challenges in validating AI predictions in early-stage drug discovery”

    – “The role of generative models in de novo drug design: current state and future prospects”

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