125 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.
Site4Drug is an AI agent that predicts ranked, modality-aware drug-binding target sites on proteins by integrating diverse biological evidence to overcome the ambiguity and failure rates associated with selecting actionable intervention regions, particularly for membrane proteins.
This paper introduces APLSuite, a comprehensive and GPU-accelerated software suite that integrates Antigen Processing Likelihood (APL) algorithms with multiple user-friendly interfaces to streamline and enhance CD4+ T cell epitope prediction by accounting for antigen processing factors often overlooked by existing methods.
The paper introduces FLOWR, a novel structure-based framework that combines flow matching with equivariant optimal transport and a curated SPINDR dataset to generate and optimize 3D ligands with superior validity, accuracy, and 70-fold faster inference than state-of-the-art methods, while also offering a versatile multi-purpose variant for fragment-based design without retraining.
This paper presents a novel generative AI framework that achieves cross-chirality generalization from L-protein training data to D-peptide binder design by integrating axial vectors into -equivariant models, marking the first method to be successfully validated in wet-lab experiments for *de novo* hetero-chiral protein-peptide interaction design.
This paper introduces Scalable Inference-Time Annealing (SITA), a method that overcomes the intractability of existing inference-time annealing techniques for large molecular systems by utilizing energy-based models to provide fast surrogate likelihoods, thereby achieving state-of-the-art sampling performance on peptides without costly divergence calculations.
This paper addresses the miscalibration of generative models by framing calibration as a constrained optimization problem and introducing two tractable fine-tuning objectives—the relax loss and reward loss—that effectively align sampling distributions with desired statistical constraints across diverse applications and large-scale models.
The paper introduces ProtoCol, a protein homology search model that leverages ColBERT-style late interaction over residue-level embeddings to outperform existing alignment-based and pooled representation methods in identifying remote homologs within the "twilight zone" of sequence similarity.
The paper introduces ProtDiS, a knowledge-guided framework that decomposes entangled protein embeddings into biologically grounded, independent dimensions using the information bottleneck principle, thereby enhancing the interpretability and performance of protein structure-function modeling across diverse downstream tasks.
The paper introduces TIGER, a text-informed framework that leverages protein-to-text generation models and a dynamic gating network to create generalized enzyme representations, significantly outperforming existing methods in bidirectional enzyme-reaction retrieval across diverse distributions and tasks.
This paper introduces MetaboKG, an analysis-centric knowledge graph framework that integrates fragmented untargeted metabolomics data from public repositories and GNPS molecular networks into a unified, traceable, and semantically rich structure to enable reproducible SPARQL exploration and reuse of biochemical knowledge.