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 presents a theoretical analysis demonstrating that unsupervised feature selection for proteins using Differentiable Information Imbalance reveals a phase transition between glass-like and liquid-like states, where the critical number of physico-chemical features coincides with the saturation of downstream classification performance, offering a principled criterion for identifying minimal feature sets.
This paper introduces UCCS, a novel inference-time steering method that effectively reduces pathological repetition in protein language models by constructing utility-controlled contrastive datasets, thereby improving generation reliability without compromising structural foldability or requiring model retraining.
The paper introduces Minimal-action discrete Schrödinger Bridge Matching (MadSBM), a rate-based generative framework that optimizes peptide sequence design by learning a time-dependent control field to navigate high-likelihood regions on an amino-acid edit graph, while also pioneering the application of discrete classifier guidance to Schrödinger bridge models for functional optimization.
This paper introduces a computational framework combining a generative method for creating synthetic functional landscapes and a topology-aware graph neural network to overcome data scarcity and predict the efficacy of multispecific antibodies, thereby enabling the rational optimization of complex therapeutic designs like trispecific T-cell engagers.
This paper addresses the limitations of closed-vocabulary tokenizers in molecular foundation models by systematically evaluating existing methods, demonstrating their impact on property prediction, and proposing two new open-vocabulary tokenizers, Smirk and Smirk-GPE, that achieve full OpenSMILES coverage while integrating nuclear, electronic, and geometric degrees of freedom.
This study presents an AI-driven framework utilizing a Ligase-Conditioned Junction Tree Variational Autoencoder to design novel, synthesizable molecular glues that target intracellular amyloid beta-42 for degradation via the ubiquitin-proteasome system, offering a promising therapeutic strategy for Alzheimer's disease.
This paper proposes a new water oxidation mechanism in Photosystem II where the O-O bond forms between the O3 ligand of His337 and the O6 oxygen generated at Mn1, a pathway that resolves experimental controversies by demonstrating lower energy requirements and a crucial role for the protein environment in steering the reaction.