Taxonomy: From Molecule to Medicine

We're building something special — and we want to do it with you.
This short survey will help us understand where your interests lie and where you feel you can add the most value across our innovation taxonomy. Your responses will inform:

  • How we match you with potential collaborators, startups, or investors

  • Which Dive Team(s) might benefit from your expertise

  • Where we can activate shared energy around high-impact topics

There are no wrong answers — just honest signals to help guide our curation. It should take less than 5 minutes.

Instructions

  1. Review each innovation category listed in the table.

  2. For any category that applies to your work or interest & an area where you think you could add value.

    • Interest Rate

      • A little – A little bit of interest or relevance, lightly on your radar

      • Moderate – Moderate interest or growing importance in your work

      • High – A major area of focus, investment, or strategic priority

    • Value Add

      • A little – You could offer some perspective or light experience

      • Moderate – You have relevant experience or useful connections

      • High – You have deep expertise or are ready to actively contribute, advise, or support

  3. You don’t need to fill out every category.
    If a category doesn’t apply to you, feel free to leave it blank.

  4. Once you're done, click Submit.

Taxonomy Explainers

1. Research Phase

  • Novel approaches in discovery science, preclinical modeling, AI-driven target ID, lab automation, etc.

  • New approaches for target discovery, preclinical model refinement, or predictive validation to accelerate and de-risk early development.

  • Tools that improve decision-making from preclinical to clinical—such as biomarkers, predictive models, and data armonization—to accelerate development and reduce patient requirements in trials.

2. Pre-Clinical to Clinical Phase

  • Solutions that inform go/no-go, trial design inputs, and integrated asset evaluation.

  • Innovations that expand patient access to clinical trials through improved discoverability, matching platforms, digital literacy tools, and community outreach.

    Spans into Clinical phase as well

3. Clinical Phase

  • AI-assisted authoring, structured design tools, and interoperable formats that enhance quality, improve recruitment outcomes, and streamline execution.

  • Master protocols, platform studies, infrastructure to enable flexible study design (including Reduce the Number of Patients Required for Clinical Trials)

  • Reduce patient burden, Modernize informed consent

  • Reduce site burden, optimize site operations, enabling non-traditional sites (e.g. in decentralized clinical trials)

  • Trial management, automation, decentralized trial operations, eConsent, and monitoring tools.

4. Post-Approval Phase

  • Platforms and methods to collect and apply post approval data (safety, label expansion, value demonstration).

  • Approaches that support additional indications, long-term follow-up, or reimbursement-related data generation.

🔄 Cross Phase Enabler

  • AI/ML platforms, infrastructure tools, integrations, and secure data systems that power innovations across phases. Includes adoption of industry-wide standards and interoperable practices that enable scale, trust, and collaboration.

    Also includes technologies that support data sharing and reuse—such as the use of historical clinical trial data to reduce participant burden through external or synthetic control arms. As synthetic data becomes more common, it's also used to train AI models, bridge gaps in real-world data, and meet data privacy requirements.

  • Tools for collaboration between preclinical scientists, clinical teams, regulatory, and leadership.

  • Real-World Data (RWD) and Real-World Evidence (RWE) are increasingly integrated across all phases of clinical development—not just post-approval. From informing regulatory submissions and label expansions to designing simplified or point-of-care trials, RWD supports smarter, more inclusive, and efficient research.
    This category also includes the reuse of historical clinical trial data (e.g. for external comparators), as well as the generation of synthetic data to bridge gaps, train AI models, and meet privacy requirements.