Academia.edu vs Atomwise
Side-by-side comparison
| Academia.edu | Atomwise | |
|---|---|---|
| Product | Academia.edu | AtomNet |
| Category | Academic Writing | Drug Discovery AI |
| What it does | Connect, share, and discover academic research to accelerate global scientific progress. | Deep-learning virtual screening to find novel small-molecule hits faster |
| Pricing model | Freemium | Partnership / Per Project |
| Starting price | Free, from $249/year (Premium) | — |
| Free trial | Yes | No |
| Target company size | Academic | Enterprise, Mid-Market |
| Deployment | Cloud/SaaS | Cloud/SaaS |
| Integrations | Paper Upload, Analytics Dashboard, Follow System, Notifications, Search | Molecular Databases, Laboratory Systems |
| API available | Yes | — |
| Certifications | GDPR, SOC 2 Type II | — |
About Academia.edu
Academia.edu is a platform for academics to share research papers, track their impact, and follow the work of others. It serves individual researchers, students, and academic institutions, providing tools for scholarly communication, discovery, and impact measurement. Its key value lies in democratizing access to research and fostering a global academic community, differentiating itself through it
Use cases: Sharing preprints and published research papers with a global audience., Discovering new research and staying updated on developments in specific academic fields., Tracking the impact and readership of one's own scholarly publications., Networking with other researchers and collaborating on projects., Providing institutional oversight and analytics on departmental research output and visibility.
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