Pauling vs. GROMACS

Pauling runs GROMACS under the hood -- same engine, same force fields, same physics -- but handles the entire pipeline automatically, from PDB to production trajectory, with automatic ligand parameterization and MM-PBSA. GROMACS itself is the gold standard for molecular dynamics -- fast, free, incredibly capable -- but incredibly manual: topology files, .mdp configs, pdb2gmx, solvation, ion addition, equilibration. A full day of expert setup per simulation.

Category Pauling GROMACS
MD engineGROMACS (same engine)GROMACS
Force fieldsAMBER, CHARMM, OPLS via GROMACSAMBER, CHARMM, OPLS, GROMOS, custom
Simulation physicsIdentical -- same integrators, PME, constraintsFull control over every parameter
Setup complexityConversational: describe your system, get a running simulationManual: pdb2gmx, editconf, solvate, genion, grompp, mdrun
Ligand parameterizationAutomatic via ACPYPE -- topology and charges generated from SMILES or SDFManual: ACPYPE/CGenFF/GAFF outside GROMACS, then merge topologies by hand
Checkpoint & resumeBuilt-in save/resume with automatic state managementNative .cpt files, but you manage restarts and file paths manually
GPU accelerationCloud GPUs provisioned automatically -- no hardware to manageExcellent GPU kernels, but you provision and configure the hardware
Cloud scalingGoogle Cloud Dataflow with Apache Beam -- auto-scales to hundreds of workersNo cloud integration -- you manage HPC queues or cloud VMs yourself
Pipeline integrationDocking, pocket detection, ADMET, QC all in one workflowMD only -- you stitch together separate tools for a full pipeline
Learning curveConversational interface -- describe what you want in plain languageSteep: weeks to months to become proficient with config files and workflows
CustomizationCurated defaults with parameter overridesFull control over every .mdp parameter and simulation detail
CostPay-per-use cloud compute, no licenseFree and open source, but you pay for hardware and sysadmin time