Molecular & Cell Biology · Life Sciences
Biomolecules
The newest papers in Biomolecules that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
68 papers, from 31 August 2026 to 5 November 2026: 65 arXiv preprints and 3 OpenAlex records.
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Newest papers, page 3
A Shortcut to Structure in AlphaFold 3
AlphaFold 3 predicts protein structures with remarkable accuracy, yet how structural information emerges within the model remains poorly understood.RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures.Learning Latent Protein Languages for Autoregressive Generation
Autoregressive transformers remain comparatively weak for protein sequence and structure generation.Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures
Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry.Fold'EM: Direct atomic structure inference from Cryo-EM particles
Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination.pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows
Biomolecular therapeutics often start from known sequences and require targeted editing to improve multiple properties while satisfying hard biochemical and manufacturability constraints.StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes
Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints.RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning
RNA design aims to identify sequences that fold into specified secondary structures.AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing
Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable.Benchmarking graph-based models for in-silico toxicity prediction in drug discovery
Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical stages.LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling
Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of…SPINET: Sheaf Protein Inverse Folding Network
Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone.M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization
Small-molecule optimization integrates medicinal-chemistry reasoning and computational evidence through iterative, multi-objective decisions.Continuous Variational Synthesis
Biological machine learning was long bottlenecked by the ability to synthesize designed DNA.Explainability from Training with Applications to TCR-Epitope Prediction
Deep learning models have achieved strong performance in artificial intelligence for science, yet their black-box nature limits our understanding of how they learn scientific tasks.Where Should Physics Enter a Molecular Crystal Generator?
Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations.Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models
Although $\mathbb{E}[\mathrm{dropout}(x)] = x$, here we show that $\mathbb{E}[\mathrm{LayerNorm}(\mathrm{dropout}(x))]$ is not equal to $\mathrm{LayerNorm}(x)$.PFArena: Benchmarking Language Models for Protein Modification
Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly.Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles
Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the experimental…Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening
Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget.