arXiv:2608.22849v2 Announce Type: replace Abstract: Full-length RNAs, particularly messenger RNAs, often exceed the context lengths used to pretrain existing RNA foundation models, limiting complete-...
arXiv:2505.23862v2 Announce Type: replace-cross Abstract: The mRNA optimization is essential for mRNA vaccines, therapies, and industrial protein production. Based on current explorations, an ideal o...
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...
Predictor-Guided Latent Space Codon Optimization (LSCO) transforms the discrete codon selection problem into a continuous optimization task by embedding sequences into the latent space of a pretrained mRNA language model, allowing gradient-based search. LSCO integrates a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior derived from a protein-to-codon back-translation model, and constrained decoding to preserve protein fidelity. In a real-world wet-lab antibody expression dataset, LSCO outperforms both simple frequency-based methods and modern deep generative baselines in predicted expression while maintaining appropriate biophysical properties.
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:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv:2609.39644v2 Announce Type: new Abstract: Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions...
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.
Pepti-drift is a one‑step peptide generation framework that uses a single latent refinement and parallel decoding, avoiding costly inference‑time guidance. It incorporates attraction toward target‑specific binders and repulsion from liability‑associated regions, achieving an 18.37% predicted Safe‑Active yield across 88 held‑out targets. The method delivers competitive gains to multi‑property‑guided baselines while reducing generation cost by 468×, enabling scalable and fair high‑throughput peptide design.
The paper introduces Noise Policy Optimization (NPO), a method for aligning few-step generative models with black-box reward signals. NPO learns an amortized flow policy that transforms initial Gaussian noise into a reward-tilted distribution, allowing the pretrained generator to produce higher-reward outputs without fine-tuning. The approach achieves superior reward-quality trade-offs across image, language, and protein generation tasks while drastically reducing inference cost compared to test-time search methods.
The paper introduces Prompteina, an all‑atom protein structure generation model that accepts natural‑language descriptions of desired structural and functional properties, similar to text‑to‑image systems. It is trained on a large paired dataset of 1.8 million protein structures and over 50 million captions, and fine‑tuned from the Proteina‑Complexa model to align residues with textual cues. Prompteina outperforms existing baselines on most structural metrics, improves binder design performance, and experimentally validates nine new binders for the GM2A target.