Pauling runs real computational chemistry -- UniDock, GROMACS, ADMET pipelines, PoseBusters validation -- on actual molecular structures. ChatGPT Deep Research can summarize papers and suggest molecules, but it cannot run a docking simulation. The difference between reading about drug discovery and doing drug discovery.
| Category | Pauling | ChatGPT Deep Research |
|---|---|---|
| Molecular docking | UniDock (GPU-accelerated Vina) -- real scoring functions, ranked poses | Cannot run docking; can only describe docking concepts |
| Molecular dynamics | Full GROMACS MD with save/resume, ligand parameterization via ACPYPE | Cannot run MD; can discuss simulation theory |
| ADMET prediction | Integrated ADMET profiling pipeline on computed hits | Can summarize ADMET rules of thumb, no actual prediction |
| Compound screening | 10M+ compounds via Cloud Dataflow auto-scaling | Cannot screen compounds -- no molecular computation |
| File handling (PDB/SDF) | Automatic conversion between PDB, PDBQT, SDF, MOL2, CIF, SMILES | Cannot parse or manipulate molecular structure files |
| Binding energy calculation | MM-PBSA from explicit MD trajectories | Cannot compute binding energies |
| Pose quality control | PoseBusters + MolProbity validation | No structural validation capability |
| Literature research | Basic -- focused on computational execution | Excellent -- deep web search, paper synthesis, citation tracking |
| Hypothesis generation | Guided by simulation results and binding data | Strong -- can reason across papers, suggest novel targets |
| Actual computation | Every result from real physics-based simulation | Zero -- generates text about science, not scientific data |
| Infrastructure | Cloud GPUs, auto-scaling workers, no installation | Runs on LLM inference only -- no HPC or molecular software |