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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |||
|---|---|---|---|---|
| Category | Research Tooling & Frameworks | Scientific ML Libraries & Supporting Tools | Scientific ML Libraries & Supporting Tools | — |
| Subcategory | Molecular Dynamics Toolkits | Conventional or Statistical Baselines | Conventional or Statistical Baselines | — |
| Company | Stanford University (Pande Lab) | Research / method authors | Research / method authors | — |
| Access mode | Open Source | Confirm distribution and exact release | Confirm distribution and exact release | — |
| Licence conditions | MIT & LGPL | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | NVIDIA or AMD GPU for maximum simulation throughput | Version and data dependent | Version and data dependent | — |
| Biological application | Protein folding dynamics, ligand binding kinetics, and free energy calculations | Not recorded | Not recorded | — |
| Research workflow | PDB structure -> parameterization with force field -> GPU-accelerated MD trajectory | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed research with widespread adoption in biophysics and drug design | PEER_REVIEWED_METHOD_OR_CONFERENCE | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Permissive open source release | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Verify access and licence | Verify access and licence | — |
| Availability ⓘ | Available via Conda (conda-forge) | Method paper or official documentation | Method paper or official documentation | — |
| 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 | HMMER 3 scientific method, Query sequences or profile HMM and sequence database, Profile HMM hits and statistical homology significance | MMseqs2 scientific method, Amino acid queries and reference sequence database, Sequence matches, clusters and alignment results | — |
| Entry type | Software Toolkit | Classical/statistical scientific method | Classical/statistical scientific method | — |
| AI role | Physics & Neural Force Field Simulation | NON_AI_CLASSICAL | NON_AI_CLASSICAL | — |
| Input data | Not recorded | Query sequences or profile HMM and sequence database | Amino acid queries and reference sequence database | — |
| Output data | Not recorded | Profile HMM hits and statistical homology significance | Sequence matches, clusters and alignment results | — |
| Validation | Not recorded | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Molecular dynamics simulation library | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | Stanford University & OpenMM Contributors | Not recorded | Not recorded | — |
| Integration evidence | https://openmm.org | Not recorded | Not recorded | — |
| Laboratory handoff | Identifies transient binding pockets for experimental hit discovery | Not recorded | Not recorded | — |