MORAD is an online reinforcement learning framework that trains a single shared policy for backbone‑conditioned RNA diffusion models, enabling structure‑conditioned RNA inverse design across diverse target backbones. It integrates backbone‑conditioned group sampling, group‑relative reward normalization, and a forward‑process diffusion objective, jointly optimizing six measurements—including tertiary geometry, base pairing, ensemble behavior, and sequence composition—by mapping each to a bounded desirability score and combining them with a weighted geometric mean. Compared to a tertiary‑structure‑only reward, MORAD improves 10 of 11 evaluation metrics, achieving significant gains in GDT‑TS, pairing MCC, and normalized ensemble defect, and surpasses existing methods such as RiboDiffusion, gRNAde, and RDesign on tertiary structural accuracy, secondary‑structure pairing, and thermodynamic quality.
arXiv:2609.36885v1 Announce Type: cross Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or...
arXiv:2610.20277v1 Announce Type: new Abstract: Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthe...
CocoRNA is a cooperative multi-agent reinforcement learning framework that tackles RNA inverse design by breaking the problem into smaller sub‑problems solved collaboratively by multiple agents. A centralized critic provides global structural guidance during training, allowing the agents to jointly optimize their design strategies. Experiments on the Rfam dataset show that CocoRNA outperforms state‑of‑the‑art methods in both success rate and design speed, and additional tests on other biological sequence design tasks demonstrate its broad effectiveness.
arXiv:2605.01513v2 Announce Type: replace Abstract: Designing therapeutic messenger RNA (mRNA) requires generating full-length transcripts that optimally balance intracellular stability and translati...
Eco‑RNA is an 80‑million‑parameter RNA foundation model that introduces adjustable inference depth via learned iterative refinement. The shared, input‑conditioned Transformer core repeatedly refines sequence representations, with the number of iterations chosen after training to control effective depth. On the RNAGym fitness benchmark, increasing iterations from one to four improves Spearman correlation from 0.242 to 0.286, and at four iterations Eco‑RNA outperforms the 40‑billion‑parameter Evo 2 model across all aggregate fitness metrics, while also excelling in non‑coding RNA classification, secondary‑structure prediction, and chemical probing profile prediction.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
arXiv:2609.36683v1 Announce Type: new Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relations...
VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion proposes a new inference-time framework that improves steering of frozen masked discrete diffusion models. By reducing the variance of guidance estimates, applying reward tilting to clean-token logits, and adapting the selection temperature at each step, VGAS addresses three default choices in existing pipelines. Experiments on regulatory DNA, protein, and small-molecule benchmarks show that VGAS achieves the best training-free reward performance and matches or surpasses reward-fine-tuned generators.
arXiv:2510.03811v2 Announce Type: replace Abstract: We develop an approach for the de novo synthesis of messenger RNA (mRNA) sequences encoding target proteins. This requires optimizing multiple biol...
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distribution, making it difficult to align with specific preferences.