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 introduces "eigencone constellations," a hierarchical framework that embeds sparse spatial graphs into concentric spherical shells partitioned by spectral mass to define a geometric metric for measuring spectral distance and enabling efficient, deterministic graph edit trajectories known as isomorphic walks.
This paper introduces DrugPlayGround, a novel framework designed to objectively benchmark large language models and embeddings on their ability to generate accurate drug-related descriptions and provide expert-justified reasoning for physiochemical characteristics, synergism, interactions, and physiological responses, thereby addressing the current lack of standardized assessment in drug discovery.
ViraHinter is a novel dual-modal deep learning framework that integrates structural generation and sequence representation to accurately predict virus-host interactions, outperforming existing models in identifying novel host factors and enabling broad-spectrum antiviral discovery across diverse viral families.
This paper demonstrates that the choice of molecular representation fundamentally shapes the interpretation of protein conformational dynamics, arguing for a comparative framework and introducing the "Orientation" feature and "ManiProt" library to capture complementary aspects of protein motion that no single representation can fully reveal.
This paper proposes Adversarial Distribution Alignment (ADA), a domain-agnostic framework that bridges the simulation-to-experiment gap by pre-training a generative model on fully observed simulation data and then aligning it with partial experimental observations to recover the target observable distribution.
This paper introduces TransIP, a novel Transformer-based framework that achieves state-of-the-art performance in machine-learned inter-atomic potentials by learning SO(3)-equivariance through embedding space optimization rather than relying on explicit equivariant architectural constraints or data augmentation.
This study employs a statistical mechanics framework to demonstrate that training large language models for protein structure prediction is optimal at intermediate temperatures, where transformer models exhibit stable learning properties and conserved parameters without first-order phase transitions, while also revealing that higher temperatures and embedding dimensions enhance the attention matrix's ability to predict protein contact maps.
This paper proposes a thermodynamically consistent model demonstrating that driven, nonequilibrium protein complexes can function as stochastic molecular automata, thereby providing a framework for engineering error-tolerant memory, molecular stopwatches, and finite-state machines within living cells.
PI-Mamba is a linear-time generative model that combines a Mamba-based state-space architecture with flow matching and physics-informed constraints to produce geometrically valid, designable protein backbones for sequences exceeding 2,000 residues without iterative refinement.
STAR-GO is a Transformer-based framework that integrates textual definitions and hierarchical graph structures of Gene Ontology terms to achieve state-of-the-art, zero-shot protein function prediction by learning unified semantic representations that adapt to evolving ontologies.