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.
This paper proposes "GROUNDING.md," a community-governed document that encodes non-negotiable scientific constraints and conventions to ensure agentic AI-assisted coding produces valid, best-practice software even when used by non-domain experts.
This paper introduces LAFA, a persistent server-based framework that enables continuous, reproducible, and dynamic benchmarking of protein function prediction models by evaluating containerized methods against evolving ground truth, thereby addressing the lack of ongoing assessment platforms outside periodic CAFA challenges.
The paper introduces DynaProt, a lightweight and SE(3)-invariant framework that efficiently predicts residue-level protein dynamics and reconstructs full covariance matrices for ensemble generation directly from static structures, offering a scalable alternative to computationally expensive molecular dynamics simulations.
ConforNets introduces a reusable, channel-wise affine transformation of pre-Pairformer pair latents in OpenFold3 that enables efficient, state-of-the-art control over protein conformational variability, allowing for both the unsupervised generation of alternate states and the supervised transfer of conformational changes across protein families.
This paper develops a kinetic theory demonstrating that cohesin-driven loop extrusion on disordered chromatin follows universal statistical laws where the distribution shape (peaked vs. exponential) serves as a direct discriminator for two-sided versus one-sided extrusion, thereby supporting a model where both cohesin arms actively operate in living cells.
This study demonstrates that ammonia acts as a critical chemical gatekeeper in Titan's Selk Crater, where its presence (≥1%) enables the thermodynamic formation of diverse prebiotic molecules like nucleobases, ribose, and fatty acids, providing testable predictions for the Dragonfly mission to distinguish abiotic chemistry and reconstruct the moon's past aqueous environment.
PUFFIN is a data-driven framework that utilizes graph neural networks with structure-aware pooling and functional supervision to discover interpretable, multi-residue protein units that effectively bridge the gap between structural arrangements and biological function.
This study utilizes a novel 2D computational framework to demonstrate that while denser platelet plugs accelerate fibrin gelation initiation near the vessel periphery by concentrating thrombin, they simultaneously restrict intraplug transport, revealing a mechanistic tradeoff where rapid plug densification may impede the internal fibrin formation necessary for durable thrombus stabilization.
This paper highlights the scarcity of experimental 3D structural data on protein folding intermediates, demonstrates the failure of existing native-structure prediction tools like AlphaFold2 in modeling these non-native states, and advocates for the development of new, dynamics-aware methods to advance the understanding of protein folding and related diseases.
This paper introduces MolSeek-OCR, a DeepSeek-OCR-2 adaptation for Optical Chemical Structure Recognition that employs a two-stage progressive fine-tuning strategy on synthetic and patent data to achieve competitive sequence-level accuracy, though it still lags behind state-of-the-art image-to-graph models and fails to improve further with reinforcement or data-curation post-training.