5 articles tagged with #protein-engineering. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers have developed GILC, a plug-and-play framework that enables efficient controllable generation in discrete diffusion models without retraining. The method uses gradient-informed logit correction and a Jacobian-free mechanism to stabilize guidance across DNA, protein, and molecular generation tasks, achieving state-of-the-art results.
Researchers introduce AgentPLM, a protein language model enhanced with real-time biophysical feedback and tool integration to generate optimized protein sequences. The system combines reasoning-augmented decoding with a novel training approach, achieving state-of-the-art performance on enzyme design, antibody optimization, and structural stability tasks.
OpenAI and Retro Bio collaborated using a specialized AI model called GPT-4b micro to engineer more effective proteins for stem cell therapy and longevity research. This represents a significant application of AI technology in advancing life sciences and medical research capabilities.
Researchers introduce GeoCoupling, a framework that optimizes how different molecular modalities (protein sequences and structures) are temporally coupled during AI model training and generation. The approach outperforms existing synchronous coupling methods in biomolecular co-design tasks, producing molecules with improved physical validity and diversity for drug design and protein engineering applications.
Researchers introduce HD-Prot, a hybrid diffusion protein language model that integrates continuous structure tokens with discrete sequence tokens for joint sequence-structure modeling. The approach achieves competitive performance on protein generation and prediction tasks while using significantly fewer computational resources than existing multimodal protein language models.
