AI2BMD

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AI2BMD is an Open Source Research & Education tool that simulates protein molecular dynamics with ab initio accuracy. It provides a Dockerized program, datasets, and Python launcher for efficient biomolecular dynamics simulations.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is AI2BMD?

AI2BMD is an AI-powered program designed for efficiently simulating protein molecular dynamics with ab initio accuracy. This open-source project, hosted on GitHub by Microsoft, includes the simulation program, relevant datasets, and public materials. It leverages AI to provide a robust solution for biomolecular dynamics simulations. The tool is packaged as a Docker image with a Python launcher for simplified setup and execution. It supports simulations for proteins with neutral terminal caps, single chains, and standard amino acids, offering both fragmented and whole-molecule simulation modes. AI2BMD also provides extensive documentation for advanced setup, protein file preparation, preprocessing, and post-analysis, making it a comprehensive platform for researchers in biophysics and computational biology.

Best used for

Ideal for researchers and computational biologists who need to simulate protein molecular dynamics, explore protein conformational spaces, and analyze biomolecular interactions. Especially valuable for those requiring ab initio accuracy and efficient, Dockerized simulation environments for complex protein systems.

Common actions

simulate protein dynamics

analyze molecular conformations

perform ab initio calculations

process biomolecular data

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Capabilities

Key features

AI-powered ab initio simulation
Protein molecular dynamics
Dockerized program
Protein Unit Dataset
AIMD-Chig Dataset
Fragment simulation mode
ViSNet simulation mode

Target Audience

researcherprofessorcomputational biologist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running AI2BMD?

AI2BMD runs on x86-64 GNU/Linux systems, recommending 8+ CPU cores, 32+ GB memory, and a CUDA-enabled GPU with 8+ GB memory. It has been tested on Ubuntu 20.04 and ArchLinux with specific Docker versions.

How do I prepare protein files for AI2BMD simulations?

Input protein files must be in .pdb format with added hydrogen atoms and capped with ACE at the N-terminus and NME at the C-terminus. Tools like PyMOL and AmberTools' pdb4amber can assist with these preparation steps, ensuring correct atom naming and residue numbering.

Can I train my own machine learning potential with AI2BMD?

Yes, AI2BMD supports training your own machine learning potential, specifically the ViSNet model. This allows for simulations without fragmentation, enabling the program to be used for various molecules beyond proteins. Instructions and source code for training are available on a dedicated ViSNet branch.

What datasets are available with AI2BMD?

AI2BMD includes the Protein Unit Dataset, covering 20 million dipeptide conformations at DFT level, and the AIMD-Chig Dataset, which contains 2 million conformations of the 166-atom Chignolin protein with corresponding potential energy and atomic forces calculated using DFT.

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