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Comparez modèles biologiques, moteurs de docking et flux de calcul côte à côte. Filtrez par modèle d'accès, empreinte de calcul et catégorie pour parvenir à une table de décision exploitable en moins d'une minute.
| Attribut | ＋ Ajouter un outil | |
|---|---|---|
| Category | Research Tooling & Frameworks | — |
| Subcategory | Molecular Dynamics Toolkits | — |
| Company | Stanford University (Pande Lab) | — |
| Access mode | Open Source | — |
| Licence conditions | MIT & LGPL | — |
| Compute requirements | NVIDIA or AMD GPU for maximum simulation throughput | — |
| Biological application | Protein folding dynamics, ligand binding kinetics, and free energy calculations | — |
| Research workflow | PDB structure -> parameterization with force field -> GPU-accelerated MD trajectory | — |
| Evidence level | Peer-reviewed research with widespread adoption in biophysics and drug design | — |
| Commercial eligibility | Permissive open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available via Conda (conda-forge) | — |
| Features | Extremely high simulation speed on NVIDIA CUDA, OpenCL, and AMD ROCm GPUs, Python API enabling custom force fields, non-equilibrium dynamics, and active learning, OpenMM-Torch plugin for incorporating neural network potentials (ANI, MACE) into simulations, Support for AMBER, CHARMM, GROMOS, and OpenFF force fields | — |
| Entry type | Software Toolkit | — |
| AI role | Physics & Neural Force Field Simulation | — |
| Input data | Non enregistré | — |
| Output data | Non enregistré | — |
| Validation | Non enregistré | — |
| Type | Molecular dynamics simulation library | — |
| Intended use | Non enregistré | — |
| Compatibility | Non enregistré | — |
| Manufacturer | Stanford University & OpenMM Contributors | — |
| Integration evidence | https://openmm.org | — |
| Laboratory handoff | Identifies transient binding pockets for experimental hit discovery | — |