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 Hermes, a lightweight transformer model trained exclusively on large-scale, diverse DNA-encoded library (DEL) datasets that successfully generalizes to predict protein-ligand binding across novel targets and chemical scaffolds, demonstrating that unified DEL data can overcome the biases of traditional public affinity datasets for effective virtual screening.
This review highlights how Molecular Dynamics simulations address the inherent challenges of cryo-electron tomography data, such as noise and conformational variability, by providing a physically grounded framework to interpret low-resolution density maps and characterize continuous conformational landscapes in cellular environments.
This paper proposes a canonicalization-based framework for diffusion models that maps samples to unique orbit representatives to train unconstrained generators, thereby achieving superior efficiency and performance in molecular graph generation compared to traditional symmetry-constrained approaches.
Using a custom deep learning framework, this study identifies that somatic mechanotherapy triggers a systemic "mechano-resonance axis" where fascial telocytes (CPTCs) sense mechanical force and communicate via the Wnt pathway to reprogram colonic telocytes into regenerative hubs that restore intestinal barrier integrity.
This study proposes that the clinical severity of RPE65-mediated retinal diseases is determined by a "Quantum Cliff," where minute sub-Angstrom structural changes caused by mutations drastically reduce the probability of proton tunneling, a mechanism that can be used to predict disease phenotype through a new quantum-structural modeling framework.
TerraBind is a new foundation model for drug discovery that achieves significantly faster inference and higher binding affinity prediction accuracy by utilizing coarse-grained structural representations instead of computationally expensive all-atom diffusion.
This paper compares three simulation schemes for modeling protein evolution using generative models and finds that while standard Monte Carlo methods fail to capture realistic evolutionary dynamics, a population genetics approach successfully reproduces complex phylogenetic structures and selective sweeps by accounting for finite-population effects.
This paper proposes a unifying mathematical framework that treats redox biology as a structured, dynamical, and spatially embedded system, moving beyond descriptive catalogs to explain how biological function and behavior emerge from the geometric and algebraic organization of molecular transformations.
This paper introduces Adaptive Protein Tokenization, a global tokenization method that overcomes the limitations of existing local approaches by progressively adding detail to protein representations, thereby improving performance in generative, reconstruction, and classification tasks while enabling adaptive inference and novel applications like zero-shot protein shrinking.
This paper introduces ConforMix, an inference-time algorithm that enhances diffusion models to efficiently sample and discover diverse biomolecular conformational landscapes, including domain motions and cryptic pockets, without requiring prior knowledge of degrees of freedom or retraining.