Webinar: Discovery Data to Decisions

The bottleneck in drug discovery is no longer generating data. It's knowing what to do with it.

Over the past few years, the volume of discovery data has exploded: transcriptomics, proteomics, single-cell sequencing, multi-omics, while AI capabilities have advanced at a pace few anticipated. And yet, for most pharma and biotech organizations, the critical gap remains stubbornly in place: the distance between a complex dataset and a defensible scientific decision.

This is the conversation Mithrl's recent webinar set out to tackle. And the insights are worth unpacking for any R&D leader thinking seriously about where AI fits in their organization.

Check-out our webinar, “From Discovery Data to Decisions”:

Today, we’re excited to share that Mithrl has raised $4 million in new funding, led by BonfireAs Vivek, Mithrl's Co-Founder and CEO lead put it during the webinar: "If I run my results today and three years later a different scientist runs it and gets two different results, it's not a scientifically rigorous tool. It's just a fancy AI tool."

That distinction, between a capable AI and a trustworthy one, is where most enterprise R&D teams are hitting a wall. Black-box outputs might be impressive in isolation, but they don't give portfolio managers, computational biologists, or regulators what they need: an auditable, reproducible chain of reasoning that connects raw data to a program decision.

Ventures with participation from our previous investors—including biotech industry insiders.

Based on the discussion, trustworthy AI for R&D comes down to three non-negotiables:

1. Transparency: Show the work. Scientists need to see not just the answer, but how the answer was arrived at. Which analysis steps were taken? Which packages were chosen, and why? What were the decision points along the way? Without this visibility, there's no mechanism to verify correctness or catch drift.

2. Traceability: A frozen, retrievable record. Every parameter, every threshold, every version needs to be captured in a state that can be recalled exactly. This is what makes ELN integration and regulatory filing possible. It's also what transforms AI from a productivity tool into a scientific asset.

3. Reproducibility: Same inputs, same outputs. Every time. This isn't a nice-to-have. Regulators expect it. Scientific credibility depends on it. Any platform that produces probabilistic, model-driven outputs without deterministic guardrails will struggle to meet this bar, particularly as longitudinal studies merge datasets across months or years.