LatchBio
Developed early intuition for how frontier models might be used productively in biotech by studying customer workflows at spatial, single-cell and proteomics vendors, and building prototypes with the first reasoning models early 2025. These tools felt like toys, but taught us:
(1) Code is a substrate through which models could play with empirical measurements from assays
(2) Independent progress in coding and statistical reasoning would carry over to many computational biology workflows, including those with low-dimensional readouts like gels and functional assays
(3) Much of the work in modern biology could be accomplished with harnesses already useful for coding, given sufficient post-training and rigorous evals
This led to our first spatial biology benchmark (SpatialBench) published late last December, built on what we'd learned deploying data infrastructure with spatial kit customers like TakaraBio, Vizgen and AtlasXOmics. Much of that structure has since carried over to other measurement types (scRNA, WGS, ATAC, ChiP) and now to concrete activities in drug development.
(1) Code is a substrate through which models could play with empirical measurements from assays
(2) Independent progress in coding and statistical reasoning would carry over to many computational biology workflows, including those with low-dimensional readouts like gels and functional assays
(3) Much of the work in modern biology could be accomplished with harnesses already useful for coding, given sufficient post-training and rigorous evals
This led to our first spatial biology benchmark (SpatialBench) published late last December, built on what we'd learned deploying data infrastructure with spatial kit customers like TakaraBio, Vizgen and AtlasXOmics. Much of that structure has since carried over to other measurement types (scRNA, WGS, ATAC, ChiP) and now to concrete activities in drug development.
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Surag Nair
Yun S. Song
arjun
ClaudeDevs
MTS
Justin Quan