# Towards Automated Design of Riboswitches

arXiv: https://arxiv.org/abs/2307.08801 · HTML: https://mlsanity.com/paper/2307.08801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Learning to Design RNA](https://mlsanity.com/paper/1812.11951/) (2018)

## Built on by (most cited first, 1 of 1)

- [Rethinking Performance Measures of RNA Secondary Structure Problems](https://mlsanity.com/paper/2401.05351/) (2023)

## Abstract (arXiv)

Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the number of candidates for the screen could drastically decrease these costs. However, existing computational approaches do not fully satisfy all requirements for the design of such initial screening libraries. In this work, we present a new method, libLEARNA, capable of providing RNA focus libraries of diverse variable-length qualified candidates. Our novel structure-based design approach considers global properties as well as desired sequence and structure features. We demonstrate the benefits of our method by designing theophylline riboswitch libraries, following a previously published protocol, and yielding 30% more unique high-quality candidates.