127 papers
The intersection of quantum physics and biology is a frontier where the strange rules of the microscopic world begin to explain life itself. In this emerging field, researchers explore how quantum effects like coherence and tunneling might drive essential processes in living organisms, from how birds navigate using the Earth's magnetic field to the incredible efficiency of photosynthesis. These studies challenge our traditional understanding of biology, suggesting that quantum mechanics plays a more active role in nature than previously imagined.
At Gist.Science, we track every new preprint appearing in the Q-Bio — Bm category on arXiv to bring these complex discoveries to a broader audience. As soon as a paper is posted, our system processes it to generate both a clear, plain-language explanation and a detailed technical summary, ensuring that whether you are a student or a specialist, you can grasp the significance of these findings without getting lost in dense jargon.
Below are the latest papers in this category, freshly processed and ready for you to explore the quantum side of biology.
The paper introduces TIGER, a text-informed framework that leverages protein-to-text generation models and a dynamic gating network to create generalized enzyme representations, significantly outperforming existing methods in bidirectional enzyme-reaction retrieval across diverse distributions and tasks.
This paper introduces MetaboKG, an analysis-centric knowledge graph framework that integrates fragmented untargeted metabolomics data from public repositories and GNPS molecular networks into a unified, traceable, and semantically rich structure to enable reproducible SPARQL exploration and reuse of biochemical knowledge.
This paper demonstrates that the Boltz2 co-folding model, which leverages protein-ligand interactions for pretraining, generates superior and complementary small-molecule representations that outperform existing standalone models across diverse tasks including ADMET prediction, generative modeling, and structure-guided optimization.
This paper reviews recent computational advances in modeling crowded cellular environments, highlighting new methods that enable microsecond-scale simulations to better understand biological phenomena in vivo compared to traditional diluted conditions.
The paper proposes TriProRep, a structure-aware pretraining method that jointly models amino-acid identity, backbone geometry, and local full-atom geometry via VQ-VAE tokenizers to improve protein structure prediction, and introduces the RepSP benchmark to validate its superior performance over existing sequence-only and structure-aware models.
This paper proposes a novel life detection framework that distinguishes biological from abiotic chemical signatures by analyzing the statistical elemental composition and scaling laws of small molecules in ecological systems, demonstrating its potential to identify biosignatures in planetary science mass spectrometry data.
This study reveals that proteomes across cellular and viral life exhibit a strikingly consistent elemental composition that predates the Last Universal Common Ancestor and suggests fundamental biochemical constraints, rather than evolutionary relatedness or specific amino acid usage, shaped the selection and stabilization of the modern amino acid alphabet.
This paper reveals that the ESM2-8M protein language model predicts the universal biological rule of methionine initiation not through direct recognition of the masked residue, but by retrieving a statistical prior via a complex, distributed computational circuit, thereby demonstrating that high model confidence may reflect statistical defaults rather than genuine biological evidence.
MoleCode introduces a training-free, graph-explicit molecular language that replaces implicit linear representations like SMILES with explicit structural relations, enabling large language models to directly reason about and manipulate molecular topology for improved performance in complex chemical tasks.
The paper introduces SAE-RNA, a sparse autoencoder model that interprets RNA language model representations by mapping them to human-level biological features, serving as a representation-level probe to characterize how these models organize biological information and identify sparse components related to RNA family identity and structural context.