Efficient Approximate Methods for Design of Experiments for Copolymer Engineering

TL;DR

This paper introduces efficient algorithms for selecting optimal subsets of polymers in experimental design, improving the process of learning polymeric design rules under complex constraints in nucleic acid therapeutics.

Contribution

The paper presents novel algorithms for subset selection in experimental design, tailored for complex constraints in polymer and nucleic acid therapeutics engineering.

Findings

Algorithms effectively solve pragmatic nucleic acid therapeutics scenarios.

Approach outperforms traditional methods like BIBD in constrained settings.

Applicable to various optimal DoE criteria such as D-optimality.

Abstract

We develop a set of algorithms to solve a broad class of Design of Experiment (DoE) problems efficiently. Specifically, we consider problems in which one must choose a subset of polymers to test in experiments such that the learning of the polymeric design rules is optimal. This subset must be selected from a larger set of polymers permissible under arbitrary experimental design constraints. We demonstrate the performance of our algorithms by solving several pragmatic nucleic acid therapeutics engineering scenarios, where limitations in synthesis of chemically diverse nucleic acids or feasibility of measurements in experimental setups appear as constraints. Our approach focuses on identifying optimal experimental designs from a given set of experiments, which is in contrast to traditional, generative DoE methods like BIBD. Finally, we discuss how these algorithms are broadly applicable to well-established optimal DoE criteria like D-optimality.