De Novo

Ultra-low biomass sequencing workflow (LBV-Seq) enables de novo metagenomic reconstruction of DNA and RNA viral genomes

Genome-resolved virome analysis remains inaccessible for many samples, including those with clinical relevance, because viral nucleic acid recovered after enrichment is often too scarce to support de novo genome assembly. As a result, many analyses are limited to sparse read-level detection, which cannot recover divergent viruses, resolve strains, or interpret gene-level variation. Here, we developed Low Biomass Viral Sequencing (LBV-Seq), a workflow that couples low-input viral sample...

De novo design of miniproteins targeting GPCRs

Author Correction: De novo design of quasisymmetric two-component protein cages

Correction to: Nature https://doi.org/10.1038/s41586-026-10464-0 Published online 20 May 2026 In the version of the article initially published, David Chmielewski’s surname appeared incorrectly (as Chemielewski) and has now been corrected in the HTML and PDF versions of the article. Author information Authors and Affiliations Corresponding authors Rights and permissions About this article Cite this article Wang, S., Xie, Y., Chmielewski, D. et al.

De novo mutation of an RNA virus is increased in the presence of engineered synonymous mutations that disrupt RNA structural elements

Using a combination of methods including selective 2?-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), a complete RNA structure map of the cucumber mosaic virus (CMV) RNA 3 segment was mapped (Watters et al. (2018) Nucleic Acids Research 46, 2573?2584). To explore the effect of structural perturbations on genomic stability, infectious mutants were engineered to contain changes in one of four open reading frame (ORF) stem-loop (SL) structures SL1362, SL1439, SL1745 and...

FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation

arXiv:2504.10564v3 Announce Type: replace-cross Abstract: We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a thoroughly curated dataset comprising ligand-pocket co-crystal complexes specifically designed to address existing data quality issues.

De novo molecular generation with optical property preconditioning at the token level

Announce Type: new Abstract: Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs. Here, we benchmark a token-conditioned autoregressive language model for OLED molecular generation in a realistic low-data regime. A GPT2 model is pretrained on large chemical corpora, augmented with discrete property tokens, and fine-tuned using...

De novo design of quasisymmetric two-component protein cages

Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer

arXiv:2601.05770v3 Announce Type: replace Abstract: Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of executable mechanisms from weights without relying on human-written target programs. However, applying this paradigm to Transformer is complicated by representation entanglement (e.g., superposition), where features encoded in overlapping directions substantially hinder the recovery of symbolic...

Controlling energy delivery with bistable nanostructures

arXiv:2506.14266v3 Announce Type: replace-cross Abstract: Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other \emph{de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time?...

Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review

Announce Type: new Abstract: Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes. Despite this, there is no concise synthesis of how fairness is defined, measured, and tested in DRL-based drug discovery. In this rapid evidence review, we synthesize fairness definitions and metrics for DRL-driven molecule generation in healthcare.