Forecasting labels under distribution-shift for machine-guided sequence design

TL;DR

This paper introduces a forecasting method to predict the performance distribution of biological sequence libraries, improving decision-making in machine-guided sequence design under distribution shifts.

Contribution

The authors propose a novel forecasting approach for sequence library performance, addressing the challenge of label distribution shift in machine-guided design.

Findings

Outperforms baseline methods in predicting library performance

Provides a posterior distribution of labels for high-throughput libraries

Enhances decision-making in sequence design processes

Abstract

The ability to design and optimize biological sequences with specific functionalities would unlock enormous value in technology and healthcare. In recent years, machine learning-guided sequence design has progressed this goal significantly, though validating designed sequences in the lab or clinic takes many months and substantial labor. It is therefore valuable to assess the likelihood that a designed set contains sequences of the desired quality (which often lies outside the label distribution in our training data) before committing resources to an experiment. Forecasting, a prominent concept in many domains where feedback can be delayed (e.g. elections), has not been used or studied in the context of sequence design. Here we propose a method to guide decision-making that forecasts the performance of high-throughput libraries (e.g. containing $10^5$ unique variants) based on estimates provided by models, providing a posterior for the distribution of labels in the library. We show that our method outperforms baselines that naively use model scores to estimate library performance, which are the only tool available today for this purpose.