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 \themodel{}, a novel multi-resolution transcriptome-guided diffusion framework that addresses the ill-posed nature of transcriptome-based drug design by bridging the biology-chemistry domain gap to generate molecules conditioned on desired cellular state transitions.
This paper introduces Frequency-Space Mechanics, a coordinate-free framework that encodes proteins as mechanical harmonics graphs based on vibrational dynamics rather than sequence or static structure, demonstrating that such physics-based representations can accurately predict protein function and capture dynamic functional states like fold-switching.
This paper proposes a continuous-depth Neural ODE formulation of the Evoformer architecture that significantly reduces computational costs and memory usage while maintaining the ability to predict plausible protein structures, offering a lightweight and adaptive alternative to traditional deep learning models like AlphaFold.
This paper introduces the Joint Gromov-Wasserstein (JGW) objective, an extension of the traditional Gromov-Wasserstein distance that enables efficient and accurate simultaneous matching of multiple objects, demonstrating superior performance in applications ranging from geometric shape alignment to biomolecular complex modeling.
This paper introduces a machine-learning framework for coarse-grained molecular dynamics that augments traditional force matching with stochastic Hessian-vector product matching to incorporate second-order curvature information, significantly improving the accuracy and transferability of coarse-grained potentials for biomolecular simulations.
This paper introduces Ensembits, the first tokenizer designed to represent protein conformational ensembles, which outperforms existing static structure tokenizers in predicting residue motion and functional properties while enabling dynamics-aware protein language modeling through a novel frame distillation objective.
BioBlobs is an encoder-agnostic, end-to-end differentiable framework that unsupervisedly discovers cohesive functional substructures ("blobs") from protein sequences or structures to predict function and identify catalytic sites using only a small fraction of residues, without requiring explicit substructure-level annotations.
This paper introduces SoftBlobGIN, a lightweight, plug-and-play framework that projects protein language model representations onto contact graphs to learn interpretable, structure-aware functional substructures, significantly improving performance on enzyme classification and binding-site detection while providing auditable biological explanations without retraining the underlying language model.
This thesis leverages deep learning to advance protein complex prediction and design by developing specialized architectures that capture structural hierarchies and creating search algorithms to efficiently navigate sequence spaces for identifying interacting homologs and designing novel protein sequences.
This paper decomposes the generalization gap in PROTAC activity prediction, identifying inter-laboratory measurement variance as the dominant limiting factor that creates a performance ceiling around 0.67 AUROC, while introducing the PROTAC-Bench dataset and calibration protocols to address these evaluation challenges.