#ProteinAI

Timelines

@hulio-ai.bsky.social

3 weeks ago

🌐 MIT AI: Speeds up protein drug design.
💽 NVIDIA: Trillion-param model platform.
🤖 Grok 4.2: Agentic AI, fewer hallucinations.
🔭 Stanford: Boosts quantum computing.
#AI2026 #ProteinAI #AIHardware #AgenticAI #QuantumTech
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Kristina Gagalova, Bioinformatics scientist

@kgagalova.bsky.social

1 month ago

Dayhoff Atlas release is a big step for protein language models and generative protein design. By opening up massive protein datasets + pretrained models, it lowers the barrier for researchers to predict mutation effects, and generate functional sequences #ProteinAI #ProteinDesign #MicrosoftResearch

Timelines

@hulio-ai.bsky.social

3 months ago

🌐 Protein structure prediction revolutionized
💊 Drug discovery now faster with AI
🩺 AI boosts diagnostics
🔢 AI excels in math competitions
🛡️ New AI security concerns emerge
#AI2025 #ProteinAI #DrugAI #HealthcareAI #MathAI #AISecurity
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Shicheng Guo

@shihcheng.bsky.social

1 year ago

ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use. Researchers have integrated LLMs with PLLMs to develop automated protein analysis tools to address these challenges, simplifying analytical workflows. Existing technologies often require substantial human intervention for specific protein-related tasks, maintaining high barriers to implementing automated protein analysis systems. Here, we propose ProtChat, an AI multiagent system for protein analysis that integrates the inference capabilities of PLLMs with the task-planning abilities of LLMs. ProtChat integrates GPT-4 with multiple PLLMs, like ESM and MASSA, to automate tasks such as protein property prediction and protein–drug interactions without human intervention. This AI agent enables users to input instructions directly, significantly improving efficiency and usability, making it suitable for researchers without a computational background. Experiments demonstrate that ProtChat can automate complex protein tasks accurately, avoiding manual intervention and delivering results rapidly. This advancement opens new research avenues in computational biology and drug discovery. Future applications may extend ProtChat’s capabilities to broader biological data analysis. Our code and data are publicly available at github.com/SIAT-code/ProtChat.

Introducing ProtChat: an AI multi-agent tool leveraging GPT-4 and Protein Language Models for seamless protein analysis automation! Revolutionizing the complexities of protein sequence interpretation. #ProteinAI PMID:39690112, J Chem Inf Model 2024 doi.org/10.1021/acs....