#molecular dynamics

DeePMD-kit facilitates the creation of deep learning models for simulating interatomic potential energy and molecular dynamics using Python and C++. Integrated with TensorFlow, it offers streamlined and efficient training workflows. This open-source tool, licensed under GNU LGPLv3.0, connects with leading MD packages, such as LAMMPS and GROMACS, utilizing MPI and GPU support to boost computational efficiency. Its modular design supports varied energy model descriptors. Discover its ability to simulate a broad range of molecular systems effectively, from metals to organic compounds, while optimizing computational resources.

TorchMD-NET provides neural network potentials enhanced for GPU molecular dynamics, supporting architectures like Equivariant Transformer, Graph Neural Network, and TensorNet. Installable via conda-forge or pip, it offers configuration flexibility through YAML or command line, supporting multi-node and GPU-specific settings, with comprehensive documentation to assist in molecular research applications.

ProLIF is a tool for generating interaction fingerprints from molecular dynamics data, docking simulations, and experimental structures involving ligands and biopolymers. Comprehensive documentation is provided on ReadTheDocs with installation guides, tutorials, and citation information. It is open for community feedback and issue tracking on GitHub, contributing to a collaborative development platform. The tool is distributed under the Apache License, Version 2.0.

Explore an innovative program for accurate protein molecular dynamics simulation using AI. Access the full simulation tool, datasets, and related resources. Understand the simple setup with Docker and Python and learn about performance enhancements for best results. Investigate datasets like AIMD-Chig and advanced models like ViSNet and Geoformer without exaggeration.