Infrastructure
Stop managing hardware, workstations, clusters, and pipelines. Pauling runs everything on end to end automated cloud infrastructure.
Pauling vs. HPC Cluster
SLURM scripts, failed jobs at 3 AM, a full-time sysadmin. Wait for sysadmin support, and then your job spends days in a queue. Pauling runs on Google Cloud Dataflow. Auto-scaling, fault-tolerant, zero infrastructure to manage.
InfrastructurePauling vs. In-House Pipeline
Six months stitching together AutoDock, GROMACS, RDKit, and a dozen Python scripts. Strict license limitations. High experitse required. Meanwhile, Pauling is the full pipeline. Dozens of validated tools, integrated end-to-end.
InfrastructurePauling vs. CROs
Send specs, wait weeks, pay per project, lose control. They may or may not use state-of-the-art protocols. Pauling gives you the same capabilities in-house. Run docking, MD, ADMET yourself in minutes.
Traditional Comp Chem Suites
Enterprise software with enterprise pricing. Pauling delivers the same capabilities through conversation.
Pauling vs. Schrodinger
Over $35,000/year per workstation, token-based licensing, steep learning curve. Pauling delivers docking, MD, ADMET, and pocket detection on cloud. No licenses, no tokens.
SuitePauling vs. OpenEye / Cadence Orion
Orion is cloud-native but still requires Floe scripting and costly enterprise pricing. Pauling is conversational. No scripting, no workflow builders.
SuitePauling vs. Cresset Flare
Flare is modern with strong FEP and docking. But it's still a desktop app with per-seat licensing. Pauling runs on cloud infrastructure through natural language.
Individual Tools
Pauling uses many of these tools under the hood. The difference is you don't have to.
Pauling vs. AutoDock Vina
Vina needs dozens of auxiliary steps. We use the same scoring function but in Uni-Dock, a GPU-accelerated fork of Vina. 1000x throughput, automatic receptor prep and box placement, zero setup.
ToolPauling vs. GROMACS
How well do you know the dozens of internal file formats and hundreds (thousands?) of parameters of GROMACS? Pauling runs GROMACS. Same engine, same force fields, same physics, in a chat interface.
ToolPauling vs. OpenBabel
OpenBabel converts formats but crashes on edge cases and silently drops stereochemistry. Lots of conflicts with other tools. Needs significant expertise. Pauling handles all conversions internally.
ToolPauling vs. AMBER
Excellent force fields, decades of validation. But requires a commercial license and complex setup and complicated pipelines for preparation and analysis. Pauling runs MD on cloud GPUs with no license fees. No need to manage GPUs or write scripts.
ToolPauling vs. OpenMM
Python-native, GPU-accelerated, great for custom workflows. But nowhere near as validated as other tools. You will be writing scripts and managing GPUs. Pauling gives you MD through conversation on managed cloud.
ToolPauling vs. NAMD
Excels at large biomolecular systems on HPC clusters. But needs deep expertise for preparation and analysis. Pauling runs MD on cloud infrastructure. No cluster access or HPC expertise required.
AI-Native Drug Discovery Platforms
New platforms using AI for drug discovery. Pauling runs validated physics, not AI approximations of chemistry.
Pauling vs. Rowan
Both aim to make comp chem accessible. Rowan gives you many tools, but Pauling runs well-chosen AI and physics-based pipelines so you don't have to.
AI PlatformPauling vs. Potato.AI
Potato lets you run workflows if you have substantial knowledge of chemistry. Pauling differentiates with a full end-to-end pipeline, from PDB download through docking, ADMET, QC, and molecular dynamics, all in one conversation.
AI PlatformPauling vs. DeepOrigin
DeepOrigin is a general-purpose computational biology platform. But with limitations for real world workflows. Pauling is purpose-built for drug discovery. Every default tuned for comp chem, with no need to manage GPUs or write scripts.
AI PlatformPauling vs. Atomwise
Atomwise is a service: you send targets, they send hits. Pauling puts the full pipeline in your hands. Screen, iterate, validate, all in real time.
AI PlatformPauling vs. Iambic Therapeutics
Iambic uses ML surrogates for structure prediction. Pauling runs real simulations with validated physics. When you need to trust the chemistry, run the chemistry.
AI PlatformPauling vs. Elix
Elix gives medicinal chemists predictive and generative AI models trained via federated learning across 16+ pharma partners. Pauling runs the full physics-based pipeline -- docking at scale, MD, ADMET, QC -- through conversation, no GPU provisioning or PhD onboarding required.
AI PlatformPauling vs. AIDDISON
AIDDISON (Merck KGaA) is design-and-search: de novo generation, similarity search, synthesis planning. Pauling runs the simulations that decide which molecules are worth making -- docking at scale, MD, ADMET, QC -- plus a hit optimization pipeline built on REINVENT 4 that generates novel analogs.
LLM-Based Tools
Language models that discuss science. Pauling does science. The difference between reading about drug discovery and doing it.
Pauling vs. ChatGPT Deep Research
ChatGPT can summarize papers and suggest molecules. It cannot run a docking simulation. Pauling runs real computational chemistry on actual molecular structures.
LLM ToolPauling vs. Gemini Deep Research
Excellent at literature synthesis and scientific reasoning. Does not dock molecules, run MD, or calculate binding energies. Pauling does. Real pipelines, real results.
LLM ToolPauling vs. Google Co-Scientist
Generates research hypotheses and experimental plans. It's a thinking tool, not a doing tool. Pauling executes. Screen compounds, run MD, get validated candidates.
LLM ToolPauling vs. Edison Scientific
LLM-based scientific research assistance. Pauling runs computational chemistry pipelines. When you need to dock a molecule, you need tools that execute simulations.
LLM ToolPauling vs. BiOmni
Applies LLMs to biological research. Pauling runs physics-based comp chem: docking, MD, ADMET, structure validation. Every result is a real simulation.