Discovery to Decision: Virtual Assays for Drug Target Ranking and Derisking

Augmenting Drug Discovery Pipelines with Mechanistic Insights Unlocked by Simulating Millions of Experiments in Turbine’s Virtual Lab

9

April

  • 3PM London / 10AM New York
  • Free
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Why attend?

While foundational resources like DepMap and Open Targets have accelerated novel target discovery, they often lack the interpretability and mechanistic insight required to rank ideas with the greatest clinical translatability. It is critical to enable drug developers to systematically link complex tumor biology to drug response at scale by harmonizing carefully selected and curated data modalities.

This webinar demonstrates how Turbine’s Virtual Cell platform bridges these gaps by simulating millions of in silico experiments daily to uncover hidden vulnerabilities. We will review real-world case studies, including the discovery of novel biomarkers for PARP inhibitors and the identification of NEK1 as a viable target where CRISPR screens failed to find drivers of disease progression.

Key learnings:

  • Close Interpretability Gaps: Move beyond static data by simulating the specific protein interacions that drive drug response.
  •  Identify Hidden Vulnerabilities: Uncover synthetic lethal pairs and dependencies that only emerge in complex molecular contexts.
  • Increase Chance or Translation to the Clinic: Learn how virtual experiments already identified more than a dozen biomarkers, with two now used in clinical panels.
  • Discover “Invisible” Targets: See how virtual screening identified NEK1 as a promising target where traditional in vitro screens failed.
  • Accelerate Discovery Intelligence: Explore how investigating millions ideas can deliver a ranked target validation plan in a few months.

Speakers

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Krishna Bulusu
VP of Product Innovation & Strategy

I am an established Healthcare AI leader with over 15 years of experience in academia and industry, specializing in the integration of data science into the drug discovery process to enhance patient outcomes through precision medicine. My leadership philosophy centers on empathy, which I consider essential for cultivating high-performing teams.

As VP of Product Innovation & Strategy at Turbine, I lead a team driving product vision and roadmap at the intersection of science, technology, and strategy to transform drug discovery and development. My role bridges long-term innovation with near-term delivery, shaping priorities, guiding products from concept to launch, and building partnerships across pharma, biotech, and academia. By combining Turbine’s Virtual Biology platform with lab-in-the-loop innovation, we aim to deliver solutions with meaningful scientific and commercial impact for patients.

In my previous role at AZ, I led a global data science team delivering innovation in multi-modal data integration and deep insights into disease and drug mechanisms, informing critical portfolio milestones and reducing data-to-decision timelines. We leveraged the power of GenerativeAI to amplify insights from structured data representations across the drug discovery and development journey. My team’s efforts ensured the back-translation of clinical insights into early discovery, informing target discovery and patient-selection hypotheses.

My work is supported by a robust record of publications and presentations in the field of Healthcare AI.I am an established Healthcare AI leader with over 15 years of experience in academia and industry, specializing in the integration of data science into the drug discovery process to enhance patient outcomes through precision medicine. My leadership philosophy centers on empathy, which I consider essential for cultivating high-performing teams. As VP of Product Innovation & Strategy at Turbine, I lead a team driving product vision and roadmap at the intersection of science, technology, and strategy to transform drug discovery and development. My role bridges long-term innovation with near-term delivery, shaping priorities, guiding products from concept to launch, and building partnerships across pharma, biotech, and academia. By combining Turbine’s Virtual Biology platform with lab-in-the-loop innovation, we aim to deliver solutions with meaningful scientific and commercial impact for patients. In my previous role at AZ, I led a global data science team delivering innovation in multi-modal data integration and deep insights into disease and drug mechanisms, informing critical portfolio milestones and reducing data-to-decision timelines. We leveraged the power of GenerativeAI to amplify insights from structured data representations across the drug discovery and development journey. My team’s efforts ensured the back-translation of clinical insights into early discovery, informing target discovery and patient-selection hypotheses. My work is supported by a robust record of publications and presentations in the field of Healthcare AI.

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Csilla Hegedűs
Senior Product Manager

Translational oncology scientist and scientific strategy leader with over a decade of experience integrating cancer genomics, mechanistic cell modeling, and biological insight to support target discovery, biomarker development, and precision oncology decision frameworks. Proven track record in translating complex multi-omics and computational outputs into biologically actionable insights and guiding cross-disciplinary teams across discovery and translational research initiatives. Experienced in bridging computational, experimental, and clinical perspectives to advance oncology research and therapeutic innovation.

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