AI Calc
Converging LLM reasoning with symbolic execution for precise mathematical and engineering modeling.

High-performance, GPU-accelerated molecular dynamics simulation toolkit for biophysical modeling.
OpenMM is a high-performance toolkit for molecular simulation, engineered to provide both a low-level C++ library for high-speed computation and a high-level Python API for ease of use. In the 2026 landscape of computational drug discovery, OpenMM serves as the foundational engine for AI-integrated molecular dynamics (MD). Its architecture is uniquely flexible, allowing researchers to implement custom force fields and integrate machine learning potentials via libraries like OpenMM-ML. It supports a wide range of hardware, including NVIDIA (CUDA), AMD (OpenCL), and Apple Silicon (Metal), ensuring high throughput for large-scale biomolecular simulations. By utilizing mixed-precision arithmetic, OpenMM achieves significant speedups without compromising physical accuracy, making it the preferred choice for simulating protein folding, ligand binding, and membrane dynamics. Its modular design allows it to function as a standalone application or as a library integrated into larger pipelines like folding@home or commercial pharmaceutical workflows.
Allows users to define arbitrary mathematical expressions for forces that are automatically compiled into optimized GPU kernels at runtime.
Converging LLM reasoning with symbolic execution for precise mathematical and engineering modeling.
Petascale parallel molecular dynamics for high-fidelity biomolecular simulations.
Scalable Graph Neural Networks for high-fidelity physical simulation on unstructured meshes.
The industry-standard Python library for high-performance molecular dynamics trajectory analysis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Seamlessly integrates neural network potentials (e.g., ANI-2x, DeepMD) into classical MD simulations.
Uses single precision for force calculations and double precision for accumulation of positions.
Ability to define particles whose positions are computed from other atom locations, such as lone pairs or Drude oscillators.
Supports CUDA, OpenCL, and Reference implementations through a unified abstraction layer.
A companion tool that automates the addition of missing atoms, residue renaming, and water solvation.
Implementation of forces that allow for backpropagation through the simulation steps in some configurations.
Identifying potential drug candidates from libraries of millions of molecules.
Registry Updated:2/7/2026
Understanding the residence time of a drug molecule in its target.
Predicting if a drug can cross the cell membrane.