Topic
computational biology
Scientists Use AI and Quantum Computing to Generate New Peptides in Spare Time
Researchers at the Technical University of Denmark used a hybrid AI-quantum computing system to generate novel peptides, achieving better results than classical models especially with limited data. The work, done on weekends with leftover funds, could accelerate personalized immunotherapies and vaccines.
New Graph Neural Network Learns Protein Representations with Secondary Structure and Energy-Filtered Hydrogen Bonds
Researchers propose a secondary-structure-aware graph neural network for protein representation learning. The model augments residue-level node representations with secondary structure assignments and constructs edges from hydrogen-bond interactions filtered by energetic strength. It achieves consistent improvements over existing methods on standard protein benchmarks and offers enhanced biological interpretability.
Emyx: New AI Model Generates All-Atom Proteins Faster and More Efficiently
Researchers have developed Emyx, a 140M-parameter conditional flow matching model for all-atom protein generation. Despite being the smallest model, Emyx outperforms both Proteína-Complexa and RFdiffusion3 on the AME enzyme design benchmark across success rate, structural novelty, scaffold diversity, and geometric validity, while training in just 682 GPU-hours—roughly 4× less than RFdiffusion3.
Agent Rosetta: How an LLM Agent Masters Protein Design for Specialized Scientific Tasks
Researchers introduce Agent Rosetta, an LLM-based agent integrated with the Rosetta software environment to automate complex protein design tasks. The agent achieves performance comparable to specialized ML models and human experts on canonical amino acids, and excels on non-canonical residues where standard ML fails. The study highlights the critical role of environment design in enabling LLM agents to operate specialized scientific software.
BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks
Researchers have introduced BRIDGE, a novel framework that refines biological evidence and uses heterogeneous dynamic gating to infer gene regulatory networks from single-cell RNA sequencing data. Benchmark tests show BRIDGE achieves a 5% improvement in average AUPRC over the second-best baseline on Specific networks, with strong validation in a human embryonic stem cell case study.