Towards Automated Design of Riboswitches

• URL: http://arxiv.org/abs/2307.08801v1
• Date: Mon, 17 Jul 2023 19:34:59 GMT
• Title: Towards Automated Design of Riboswitches
• Abstract summary: Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. We present a new method, libLEARNA, capable of providing RNA focus libraries of diverse variable-length qualified candidates.
• Score: 38.46798525594529
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation [59.19858972597063]
  LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom?<n>We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution.<n>A skill bank distills recurring operations into compact procedural rules reusable across projects.<n>A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history.<n>A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination.
  arXiv  Detail & Related papers  (2026-05-11T16:33:47Z)
• Designing RNAs with Language Models [3.332772772624738]
  RNA design is a challenge due to the exponentially large sequence space and exponentially many competing folds.<n>We introduce a reusable neural approxorimator, instantiated as an autoregressive language model (LM), that maps target structures directly to sequences.<n>Across four datasets, our approach outperforms state-of-the-art systems on key metrics such as Boltzmann probability while being 1.7x faster.
  arXiv  Detail & Related papers  (2026-02-12T22:57:09Z)
• Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
  LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
  arXiv  Detail & Related papers  (2026-01-10T09:49:45Z)
• Extensions of regret-minimization algorithm for optimal design [3.4245017707416148]
  We explore extensions and applications of the regret minimization framework introduced bycitedesign for solving optimal experimental design problems. We incorporate the entropy regularizer into this framework, leading to a novel sample selection objective and a provable sample bound complexity. As an application, we use our algorithm to select a small set of representative samples from image classification datasets without relying on label information.
  arXiv  Detail & Related papers  (2025-03-25T17:37:09Z)
• Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text Generation [60.493180081319785]
  We propose a systematic way to estimate the intrinsic capacity of a truncation sampling method by considering the trade-off between diversity and risk at each decoding step. Our work provides a comprehensive comparison between existing truncation sampling methods, as well as their recommended parameters as a guideline for users.
  arXiv  Detail & Related papers  (2024-08-24T14:14:32Z)
• An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models [55.01592097059969]
  Supervised finetuning on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities. Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool. We propose using experimental design to circumvent the computational bottlenecks of active learning.
  arXiv  Detail & Related papers  (2024-01-12T16:56:54Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization [3.460496851517031]
  PLiNIO is an open-source library implementing a comprehensive set of state-of-the-art DNN design automation techniques. We show that PLiNIO achieves up to 94.34% memory reduction for a 1% accuracy drop compared to a baseline architecture.
  arXiv  Detail & Related papers  (2023-07-18T07:11:14Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Forecasting labels under distribution-shift for machine-guided sequence design [0.0]
  We propose a method to guide decision-making that forecasts the performance of high- throughput libraries. We show that our method outperforms baselines that naively use model scores to estimate library performance.
  arXiv  Detail & Related papers  (2022-11-18T18:35:50Z)
• An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries [1.5495593104596397]
  Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. We propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE) to overcome these challenges.
  arXiv  Detail & Related papers  (2022-10-19T15:43:13Z)
• A Framework and Benchmark for Deep Batch Active Learning for Regression [2.093287944284448]
  We study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations, and selection methods. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code.
  arXiv  Detail & Related papers  (2022-03-17T16:11:36Z)
• Biological Sequence Design with GFlowNets [75.1642973538266]
  Design of de novo biological sequences with desired properties often involves an active loop with several rounds of molecule ideation and expensive wet-lab evaluations. This makes the diversity of proposed candidates a key consideration in the ideation phase. We propose an active learning algorithm leveraging uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions.
  arXiv  Detail & Related papers  (2022-03-02T15:53:38Z)
• On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game [140.19656665344917]
  We study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. We tackle this problem under the context of function approximation, leveraging powerful function approximators. We establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
  arXiv  Detail & Related papers  (2021-10-19T07:26:33Z)
• Optimal Decision Making in High-Throughput Virtual Screening Pipelines [12.366455276434513]
  We propose two optimization frameworks, applying to most (if not all) screening campaigns involving experimental or/and computational evaluations. In particular, we consider the optimal computational campaign for the long non-coding RNA (lncRNA) classification as a practical example. The simulation results demonstrate that the proposed frameworks significantly reduce the effective selection cost per potential candidate.
  arXiv  Detail & Related papers  (2021-09-23T22:58:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.