Kristin Gleitsman

Leadership in Biotech

Tag: ai training sets

Illustration of a desk with a figure from a recent paper about Platinum Pedigree

AI Models Need Better Truth—Platinum Pedigree Shows How

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On November 29, 2025

In AI ∩ Bio

When I was in high school, I was obsessed with genetics. The Human Genome Project was in full swing, and it felt like the future was being written in real time. I told a family friend I wanted to become a geneticist. He smiled and said, “My cousin is at the NIH. They’ll finish the human genome before you finish college, so I wouldn’t bother.”

The project wrapped in 2003. But papers like this remind me how wrong that prediction was. Even after “finishing” the genome, we’re still uncovering what accuracy, completeness, and truth really mean.

The new Platinum Pedigree study pushes that frontier again.

Scientific Insight

This work builds one of the most comprehensive germline variant benchmarks to date, deep long-read sequencing across a 10-member family, combined with Mendelian logic.

By integrating PacBio HiFi, Oxford Nanopore Technologies, and Illumina and testing every variant against inheritance patterns, the authors defined 2.77 Gb of high-confidence genome (~200 Mb beyond prior benchmarks), including repeats, segmental duplications, and low-mappability regions.

The key innovation is biological grounding.

Each child inherits one haplotype from each parent; variants that obey those segregation patterns are kept, and those that don’t are removed. This yielded ~4.7M SNVs, 768k indels, 537k tandem repeats, and 24k structural variants as pedigree-consistent truth.

When DeepVariant was retrained on this truth set, error rates dropped by ~34% across challenging classes, especially indels and tandem repeats.

Better labels → better models.

Leadership Angle

For diagnostics leaders, this signals where the field is heading: stronger evidence standards, clearer definitions of “truth,” and biologically informed benchmarks rather than technology-constrained heuristics.

This strategy of combining multiple sequencing technologies and adjudicating discrepancies with inheritance is exactly how robust systems are built in uncertain environments.

It mirrors what clinical diagnostics now requires: pipelines that perform not just in easy regions, but in messy, clinically meaningful ones.

And it underscores a central lesson in AI-enabled diagnostics: your model is only as good as the ground truth you train it on.

The regions that are currently messy and difficult to map: that’s where new breakthroughs in understanding will occur.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Mentorship Angle

For early-career scientists, the lesson is craftsmanship. This paper doesn’t debut a flashy algorithm; it elevates the foundations. It asks simple but profound questions: Did this variant follow the rules of inheritance? If not, are we sure it’s real?

Your technical tools matter, but your willingness to interrogate assumptions matters more. If you want to build a meaningful career in genetics in this age of AI, stay curious about the scaffolding beneath the science.

Breakthroughs often start there.

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Illustration of a desk with a figure from a recent paper about binding affinity predictions

Beyond Binding: Rethinking Drug Design in the Age of AI and Structural Biology

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On July 23, 2025

In AI ∩ Bio

In molecular design, we often prioritize what’s measurable over what’s meaningful.

For decades, binding affinity has served as a cornerstone of early-stage drug discovery, not because it captures biological function in full, but because it’s one of the few properties we can quantify systematically and optimize across large libraries.

Now, as AI models generate binders faster than we can validate them, we must ask: What exactly are we optimizing for? And what datasets are we training on?

What we know:

  • Strong binding doesn’t guarantee efficacy
  • Residence time and conformational flexibility can matter more than affinity
  • Cellular context — target expression, pathway crosstalk, and off-target interactions — often dictates outcome in clinical applications

Yet much of the public data — and many AI training sets — still orbit around Kd, IC₅₀, and docking scores. These are abundant and easy to label, but they capture only a narrow slice of pharmacological reality (and we’re not even accounting for the fact that these measurements are highly dependent on the specific conditions- buffer, temperature, etc).

If we train models on what’s easy to measure, we shouldn’t be surprised when they generate molecules that impress in silico — and disappoint in vivo.

The problem isn’t that binding doesn’t matter. It does. The problem is that binding isn’t biology.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Toward More Meaningful Models

To do better, we’ll need to:

  • Incorporate multi-parametric data: kinetics, permeability, metabolism, toxicity, immune activation
  • Train models to include mechanism and uncertainty, not just affinity
  • Elevate datasets that link structure to systems, not just structure to scores

The best work ahead won’t just generate molecules; it will surface better models about how they work, and where they fail in the journey from discovery to clinic.

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