125 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 demonstrates that knotting in curves, including proteins and synthetic examples, can be detected by computing one-dimensional persistent homology from point-cloud data and assigning a hypergraph curvature-based score to the resulting cycle representatives.
MegaFold is a novel cross-platform system that overcomes the memory and computational bottlenecks of training next-generation 3D attention protein models by combining memory-efficient kernels, optimized sharding, fused operators, and a determinism-aware pipeline to achieve significantly longer sequence lengths and faster training times on both NVIDIA and AMD GPUs.
Pepti-Agent is a closed-loop AI framework that integrates generative models, property predictors, and mutation operators via Model Context Protocol tools to enable an LLM controller to iteratively optimize therapeutic peptides by tracking their evolving multi-property profiles rather than relying solely on natural language reasoning.
This paper establishes a generalized, substrate-independent formal definition of assembly spaces and indices while introducing efficient grammar-based algorithms to approximate these metrics, thereby providing a unified framework to advance the detection of life signatures across chemistry, biology, and complexity science.
This paper introduces a thermodynamically informed, temperature-transferable machine-learned coarse-grained framework for proteins that explicitly decomposes the potential of mean force into energetic and entropic components, enabling accurate prediction of temperature-dependent properties like heat capacity across different thermal regimes without retraining.
RicciBind is a novel geometric representation framework that enhances protein-ligand binding affinity prediction by integrating Ricci curvature-guided hierarchical structure learning with optimal transport-based cross-domain alignment to effectively model both local interaction tightness and globally coordinated cross-molecular interactions.
This paper introduces an information-theoretic framework that optimizes the selection of representative conformations in cryo-electron microscopy by maximizing mutual information between ensemble weights and images, thereby defining a measurement-induced coarse-graining that balances structural resolution with statistical identifiability in the presence of noise.
This paper evaluates seven protein-protein interface scoring functions by correlating their scores with structural similarity (DockQ) across a non-redundant dataset, revealing that performance varies based on target complexity and leading to the development of a new, highly effective scoring function based on three physical features.
This paper proposes a Bayesian inference framework that utilizes an individual's fixed genomic profile as a personalized prior to solve the cold-start problem in health AI, enabling the immediate separation of constitutional physiological baselines from environmentally driven deviations before longitudinal behavioral data is available.
The paper introduces GLACIER, a computationally efficient student-teacher foundation model that integrates molecular graphs, SMILES strings, and physicochemical descriptors through a three-stage framework of pretraining, Finsler geometry-aware fusion, and contrastive knowledge distillation to achieve high-performance molecular property prediction.