Discover novel drug candidates using generative AI
Variational AI's Enki platform uses generative AI to design optimized small molecule drugs without requiring proprietary data input. The system accelerates early-stage drug discovery by predicting molecular properties and generating novel compounds validated against 570+ biological targets.
Combines multiple AI algorithms for optimized molecule design
Design molecules without proprietary data input
Trained on 570+ biological targets across 10+ classes
Customize target product profiles in minutes
Enki requires no proprietary data input - users simply define their Target Product Profile (TPP) to begin generating candidates.
The platform covers 10+ target classes including GPCRs, kinases, and proteases, with new targets added regularly.
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Recent data shows that Variational AI has 4.2K monthly visits (42.8% increase from the previous month), 30.0% bounce rate, and average 1.88 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 2 countries worldwide, led by United States contributing 66% of total traffic. SEO performance is shown by 5 tracked keywords, with "computational chemist career" being the top-performing keyword with 130 monthly searches. See below for more info.
Monthly Visits
4.2K
Pages per Visit
1.88
Bounce Rate
30.0%
Average Time on Site
1m 9s
| SEO Keyword | Volume | CPC |
|---|---|---|
| computational chemist career | 130 | - |
| variational ai funding | 110 | - |
| variational ai | 110 | - |
| enki variational ai | 60 | - |
| variational | 1.2K | - |
| Source | Traffic Share |
|---|---|
| Direct | 43% |
| Search | 40% |
| Social | 11% |
| Referrals | 6% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 66% |
 | 34% |
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