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 a differentiable Gauss-Seidel projection module that enforces strict physical validity constraints during biomolecular modeling, enabling a 2-step diffusion process to achieve state-of-the-art structural accuracy with 10x faster inference speed compared to traditional 200-step baselines.
This paper presents a knowledge graph-based framework that integrates heterogeneous data sources to enable contextual comparison of efficacy and adverse drug reactions for protein kinase inhibitors, thereby supporting hypothesis generation and enhancing pharmacovigilance.
The paper introduces SpaceGFN, a generative framework that transforms chemical space into a programmable object by decoupling the explicit construction of synthetically coherent molecular universes from GFlowNet-based exploration, thereby enabling both targeted discovery of novel scaffolds and synthesis-aware lead optimization.
The paper introduces MDIntrinsicDimension, an open-source Python package that estimates the intrinsic dimension of molecular dynamics trajectories using invariant projections and state-of-the-art estimators to provide detailed, time-resolved insights into the flexibility and heterogeneity of macromolecular motions.
This review categorizes and surveys recent strategies for steering generative protein design models toward user-specified properties, distinguishing between methods that modify model parameters and those that employ fixed-parameter techniques like conditional generation and tailored sampling to explore valuable low-probability regions.
This review outlines the conceptual framework for integrating fluorescence spectroscopy and modeling to characterize biomolecular structures and dynamics, while highlighting advances in dye representation, quantitative structural analysis, and the prediction of spectroscopic properties for enhanced assay design.
This paper introduces a statistical physics framework based on the grand canonical ensemble to model how macromolecular crowding and excluded volume effects drive the dynamic assembly and structural diversity of protein megacomplexes, using the INO80 chromatin remodeler as a case study to reveal how divergent subunits orchestrate configurationally distinct architectures.
This paper advocates for a metrology-centered approach using molecular assembly theory to quantify molecular complexity, aiming to establish standardized frameworks that unify origin-of-life research, bridge competing hypotheses, and enhance the detection of life beyond Earth.
The paper introduces SEISMO, a trajectory-aware LLM agent that significantly improves sample efficiency in molecular optimization by performing strictly online updates conditioned on full optimization trajectories and explanatory feedback, achieving superior performance across 23 benchmark tasks with far fewer oracle calls than prior methods.
Protect* is a neuro-symbolic framework that enhances the reliability of large language models in retrosynthesis by integrating automated rule-based logic and expert constraints to steer generative processes away from chemically sensitive sites, enabling the discovery of valid and novel synthetic pathways for complex molecules like Erythromycin B.