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Deep Agents is an agent harness built on langchain and langgraph. Deep Agents are equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - making them well-equipped…
ATLAS: Software Engineer AI Agent. Living memory persists. Learning compounds. Every commit evolves it. Professional focus. KISS/YAGNI/DRY and Depend on Context. No overengineering. Clean code and …
This is a simple demonstration of more advanced, agentic patterns built on top of the Realtime API.
📄 Configuration files that enhance Cursor AI editor experience with custom rules and behaviors
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
A Python package for processing molecules with RDKit in scikit-learn
A generative model for programmable protein design
Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design
A list of manuscripts/tools using diffusion on biological enttieis
bio-transformers is a wrapper on top of the ESM/Protbert model, trained on millions on proteins and used to predict embeddings.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
Physicochemical properties, indices and descriptors for amino-acid sequences.
python tools for TCR:peptide-MHC modeling and analysis
insitro's repository for public research code and data
Sandbox for Deep-Learning based Computational Protein Design
PyAutoFEP: an automated FEP workflow for GROMACS integrating enhanced sampling methods
List of papers about Proteins Design using Deep Learning
A playbook for systematically maximizing the performance of deep learning models.
Matcher is a tool for understanding how chemical structure optimization problems have been solved. Matcher enables deep control over searching structure/activity relationships (SAR) derived from la…
Versatile computational pipeline for processing protein structure data for deep learning applications.
Making large AI models cheaper, faster and more accessible
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.