arXiv:2610.16232v1 Announce Type: cross Abstract: In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one...
The paper introduces LOCOGEN, a method for designing regulatory DNA sequences that balances activity, novelty, and plausibility. LOCOGEN treats guidance as a locality problem, protecting motif positions with an invariant mask while applying out‑of‑distribution damping only to background regions. Experiments on HepG2, K562, and SK‑N‑SH datasets show that LOCOGEN reaches a previously underexplored region of the property‑novelty‑plausibility space, outperforming existing baselines.
arXiv:2610.24806v1 Announce Type: cross Abstract: Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for v...
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.
ARGUS is an evidence‑constrained framework that separates deterministic biological computation from large‑language‑model reasoning to interpret single nucleotide variants in noncoding regulatory regions. It uses 458 DNABERT‑based transcription factor binding models within a hypothesis‑directed loop where a planner selects evidence sources, a verifier interprets observations, and the investigation path adapts to uncertainty. In a case study of variant rs6983267, ARGUS generated divergent investigative trajectories for four transcription factors, demonstrating how real experimental data from ADASTRA, JASPAR, and ENCODE cCRE guide conclusions and how the LLM planner can reduce unnecessary tool calls.
EvoLen is a tokenization method for DNA language models that incorporates evolutionary information to prioritize functional sequence patterns such as regulatory motifs. It groups DNA sequences by cross-species evolutionary signals, trains separate BPE tokenizers for each group, merges vocabularies with a rule that favors preserved patterns, and uses length-aware decoding with dynamic programming. Experiments show EvoLen better preserves functional motifs, differentiates genomic contexts, and aligns with evolutionary constraints while matching or surpassing standard BPE on various DNALM benchmarks.
arXiv:2606. 02624v1 Announce Type: cross Abstract: AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static measurements.
The paper introduces AssayBench-Loop, a large benchmark of 1,389 CRISPR screens across five phenotype categories, and builds on it to develop AssayLoop, a sequential experimental design framework that combines a transformer-based acquisition policy (AssayFormer) trained on historical data with LLM-derived biological priors. AssayLoop achieves a 5.67‑fold enrichment over random selection, recovering 27.7% of hits after testing only about 5% of the library, and outperforms existing adaptive-design methods and standalone LLMs. The authors also present AssayLLM, extending the approach directly to an LLM via task‑specific post‑training, and show that performance improves with more historical training data and transfers to unseen phenotype categories.
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
AgentFold is a multi‑agent framework that treats protein‑folding model design as a closed‑loop search over executable code variants. Starting from the ESMFold codebase, the agents generate hypotheses, modify and debug code, evaluate model variants, and store both successes and failures in structured memory, guided by an MCTS‑style policy that allocates GPU resources. In an engineering‑scale experiment, AgentFold explored about 80 variants using 5,000 GPU‑hours and 170 million LLM tokens, improving the best lDDT score by 7.5% over independent Codex proposals and outperforming a random‑search baseline, while also uncovering empirical design patterns such as the benefits of early, soft, learnable priors.
arXiv:2610.15711v1 Announce Type: cross Abstract: Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DN...
The paper introduces LibStructBench, a document‑grounded benchmark designed to assess whether AI agents can accurately reconstruct sequencing‑library structures and molecular workflows from protocols. Experiments reveal that current state‑of‑the‑art agents often generate roughly correct reconstructions but struggle with exact molecular‑state representation and workflow connectivity. The study also finds that changing the underlying language model yields larger performance differences than changing agent harnesses, and that additional knowledge only modestly improves oligo‑family recovery without affecting overall workflow accuracy.