Atomwise vs Augment Code
Side-by-side comparison
| Atomwise | Augment Code | |
|---|---|---|
| Product | AtomNet | Augment Code |
| Category | Drug Discovery AI | AI Coding Copilots |
| What it does | Deep-learning virtual screening to find novel small-molecule hits faster | Enterprise AI coding with a 400K+ file Context Engine for multi-repo codebases. |
| Pricing model | Partnership / Per Project | Usage-based (user messages or tokens/credits), with tiered plans. |
| Starting price | — | $20/month (Indie plan) |
| Free trial | No | Yes |
| Target company size | Enterprise, Mid-Market | — |
| Deployment | Cloud/SaaS | SaaS |
| Integrations | Molecular Databases, Laboratory Systems | GitHub, Linear, Jira, Confluence, Notion, Sentry, Stripe |
| API available | — | Yes |
| Certifications | — | SOC 2 Type II, ISO/IEC 42001 |
About AtomNet
AtomNet is Atomwise’s (now Numerion Labs) structure-based AI platform that predicts protein–small-molecule binding to accelerate early drug discovery. It is used by pharma/biotech R&D teams and academic labs to run virtual high-throughput screening (vHTS) across extremely large, synthesizable chemical libraries and prioritize compounds for synthesis and experimental testing. The business value is
Use cases: Virtual high-throughput screening to identify initial hits for a protein target, Finding novel scaffolds for targets with limited or no known ligands (hard-to-drug targets), Prioritizing compounds for synthesis-on-demand procurement and experimental validation, Supporting partner programs from hit identification through lead optimization and candidate selection, Running multi-target discovery programs within strategic pharma collaborations
About Augment Code
Enterprise AI coding with a 400K+ file Context Engine for multi-repo codebases.
Use cases: Automated Code Review, AI-driven Test Coverage Generation, Incident Management Automation, Ticket-to-PR Workflow Automation, Spec-Driven AI Code Generation