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.
Using Monte Carlo simulations to isolate topological effects, this study demonstrates that protein knots enhance kinetic stability—particularly as knot depth increases—suggesting that evolutionary pressure for resistance to unfolding may drive the persistence of knotted proteins.
This paper demonstrates that simple, local molecular fingerprints combined with LightGBM outperform complex graph neural networks and transformers across 132 peptide datasets, challenging the assumption that modeling long-range interactions is essential for accurate peptide function prediction.
This paper introduces a novel representation of local protein environments derived from atomistic foundation models that effectively captures structural and chemical features, enabling the construction of data-driven priors and achieving state-of-the-art accuracy in physics-informed NMR chemical shift prediction.
This paper critiques conventional aDNA methods for oversimplifying molecular origins and introduces the HSF posterior traceability framework, which utilizes first-principles analysis and a four-system classification to improve authenticity evaluation and reduce misassignment in complex, mixed-signal fossil samples.
This paper introduces Quantifying Cross-Attention Interaction (QCAI), a novel post-hoc explainable AI method that interprets cross-attention mechanisms in encoder-decoder transformers to improve the understanding of TCR-pMHC binding, achieving state-of-the-art performance on the newly established TCR-XAI benchmark of 274 experimentally determined structures.
This paper introduces a general sampling-based continuous optimization framework that iteratively refines parameterized distributions to design messenger RNA sequences, effectively navigating the vast synonymous space to optimize multiple coupled stability and performance objectives outperforming existing methods like LinearDesign and EnsembleDesign.
The paper introduces HEroBM, a deep equivariant graph neural network that utilizes a hierarchical, local-principle-based approach to achieve accurate, transferable, and universal backmapping from coarse-grained to all-atom molecular representations across diverse chemical systems.
This paper introduces a general inference-time optimization framework that generates experiment-grounded protein ensembles by optimizing latent representations and employing novel sampling schemes, thereby overcoming the limitations of current diffusion-based methods to produce thermodynamically plausible structures with improved agreement to experimental data while exposing vulnerabilities in existing confidence metrics.
FLOWR.root is an SE(3)-equivariant flow-matching foundation model that unifies structure-aware 3D ligand generation with multi-purpose affinity prediction and confidence estimation, achieving state-of-the-art performance through mixed-fidelity training and parameter-efficient finetuning for efficient, high-quality drug design.
This paper introduces a nonparametric framework that optimizes reaction coordinates by incorporating trajectory histories to overcome standard machine learning limitations, enabling robust characterization of rare event dynamics in complex systems like protein folding and climate models without requiring extensive sampling or ground truth data.