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Description:
ProtAgents is an AI-driven protein discovery model that combines large language models (LLMs), physics-based simulations, and multi-agent collaboration to design and optimize functional proteins. Unlike traditional protein engineering methods, ProtAgents employs multi-agent reinforcement learning (MARL) with physics-aware machine learning, allowing for a more systematic exploration of protein space. It is particularly useful for de novo protein design, functional annotation, and sequence optimization.Key Features:
Multi-Agent AI Framework: Uses multiple collaborating LLM-based agents for protein function prediction.
Integrates Machine Learning & Physics: Enhances protein structure modeling and function annotation.
Optimized for Protein Discovery: Enables the generation of novel protein sequences with desired properties.
Applications: Enzyme design, therapeutic protein engineering, bioinformatics research, and synthetic biology.Publication:
📄 ProtAgents: Protein Discovery via Large Language Model Multi-Agent Collaborations Combining Physics and Machine Learning
🔗 Read the PaperCode Source:
📂 GitHub Repository: ProtAgents on GitHubThis forum topic is for protein engineers, computational biologists, and AI researchers interested in ProtAgents. Join this discussion to:
Understand how multi-agent AI enhances protein discovery and sequence optimization.
Discuss the integration of LLMs with physics-based protein modeling.
Explore real-world applications in enzyme engineering and synthetic biology.
Collaborate on improving ProtAgents for functional protein prediction and design.
🚀 Join us in exploring AI-driven protein engineering with ProtAgents!
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