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arXiv · 2512.15984

Lifting Biomolecular Data Acquisition

Eli N. Weinstein·Andrei Slabodkin·Mattia G. Gollub·Kerry Dobbs·Xiao-Bing Cui·Fang Zhang·Kristina Gurung·Elizabeth B. Wood
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Abstract

One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to function space. We measure the activity of multiple different molecules simultaneously, rather than individually. Then, we deconvolute the molecule-activity map during model training. Co-design of wet lab experiments and learning algorithms provably leads to orders-of-magnitude gains in information density. We demonstrate on antibodies and cell therapies.

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Eli N. Weinstein, Andrei Slabodkin, Mattia G. Gollub, Kerry Dobbs, Xiao-Bing Cui, Fang Zhang, Kristina Gurung, Elizabeth B. Wood. 2025-12-17. Lifting Biomolecular Data Acquisition. https://arxiv.org/abs/2512.15984

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