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.
The paper introduces Supergroup Relative Policy Optimization (SGRPO), a flexible framework that constructs set-level diversity rewards from candidate supergroups to effectively decouple and simultaneously optimize both utility and diversity in biomolecular generation tasks, thereby expanding the utility-diversity Pareto frontier across various molecular and protein design benchmarks.
The paper introduces Yeti, a compact and expressive protein structure tokenizer based on lookup-free quantization and flow matching that achieves high reconstruction accuracy and token diversity, enabling the training of small multimodal models capable of generating plausible protein sequences and structures from scratch.
This paper introduces a fully automated, rule-regularized framework that generates a large-scale dataset of approximately 163,000 high-precision molecular structure-language pairs, overcoming the cost barriers of human annotation to enable robust alignment between molecular structures and natural language for chemical reasoning.
The paper introduces CA-DEL, a novel multi-target, multi-modal benchmark designed to advance machine learning in drug discovery by training models on noisy DNA-encoded library (DEL) sequencing data and rigorously evaluating their ability to predict true binding affinities across homologous carbonic anhydrase isoforms.
This paper advocates for increased investment in antiviral drug discovery by presenting a comprehensive survey of open-source tools, a curated dataset of 43,005 viral protein-ligand interactions, and a benchmark of 15 AI-based models that demonstrates the superior performance of fine-tuned machine learning approaches like Boltz-2 and DrugFormDTA for predicting binding affinities.
Ligandformer is a multi-layer self-attention Graph Neural Network framework that predicts compound properties with high accuracy, robustness, and generalization while providing interpretable attention maps to reveal the structural rationales behind its predictions.
This study evaluates various docking and scoring methods on the LIT-PCBA library, finding that while supervised machine learning re-ranking significantly improves enrichment over single methods like AutoDock-GNINA, no single approach dominates across all targets, highlighting the continued modest performance of current virtual screening workflows on realistic datasets.
This paper introduces Q-SFD, a quadratic unconstrained binary optimization method that simultaneously docks two molecular fragments with an explicit distance constraint to significantly increase the recovery of geometrically linkable pose pairs without compromising fragment-level accuracy.
This paper demonstrates the capability of agentic AI to autonomously orchestrate two specialized platforms: one that builds high-accuracy predictive machine learning models for human-human and virus-human protein-protein interactions, and another that induces interpretable, human-readable rules that align with those models' findings.
VARIANT is a freely available web server designed to comprehensively analyze RNA viral mutations at both genome and protein levels, offering automated annotation of standard variants, detection of unique patterns like "row" and "hot" mutations, and dual graph topology analysis for RNA secondary structures across diverse viral families.