How Variational AI supported Rakovina Therapeutics in advancing a novel oncology program
A Collaboration to Enable Faster Oncology Innovation
At the 2025 AACR Annual Meeting, Rakovina Therapeutics presented early-stage results from a program aimed at developing a brain-penetrant ATR inhibitor—an approach with potential relevance for patients with brain tumors or CNS metastases.
This initiative was supported by Variational AI’s Enki™ platform, which was used to rapidly generate and prioritize novel chemical structures aligned with Rakovina’s preclinical design objectives.
The Challenge: Limited Options for CNS-Targeting ATR Inhibitors
Ataxia telangiectasia and Rad3-related protein serine/threonine kinase (ATR) inhibition is a promising therapeutic strategy in oncology, because of its crucial role in DNA damage response. Yet many of the ATR inhibitors currently under development do not sufficiently penetrate the blood-brain barrier. This limits their application in indications involving the CNS.
Designing molecules that are both potent and selective for ATR—and possess the physicochemical properties required for CNS exposure—is a non-trivial challenge. It demands multi-parameter optimization and the ability to search widely and efficiently across chemical space.
The Role of Enki™ in Supporting the Program
Rakovina defined a clear target product profile (TPP) for a next-generation ATR inhibitor. Using this specification, Enki, our generative AI platform for small molecule design, was tasked to propose a diverse set of compounds that met the required criteria:
- Potent and selective inhibition of ATR
- Properties consistent with CNS penetration
- Acceptable drug-like physicochemical properties for oral bioavailability
Enki generated and prioritized 138 novel compounds, which were then further refined into a shortlist of candidates for synthesis and in vitro testing. This front-loaded design effort enabled Rakovina to progress more quickly toward experimental validation.
From Molecule Generation to Real-World Testing
This collaboration underscores how AI can deliver more than incremental gains. By enabling rapid hit generation tailored to real-world design constraints, Enki empowers teams like Rakovina’s to move quickly from concept to candidate—without compromising on chemical novelty or developability.
The compounds generated by Enki are now undergoing in vitro and in vivo evaluation by Rakovina’s team. As noted in their public release, the ability to rapidly move from in silico candidate generation to preclinical testing highlights the efficiency gains made possible through AI-enabled design.
For Variational AI, this collaboration exemplifies how Enki can complement drug discovery teams by delivering high-quality leads tailored to difficult design challenges—particularly when traditional screening or structure-based methods may be limited.
But the implications go further. For any biotech or pharma team facing tight timelines, complex target product profiles, or tough-to-drug indications, Enki represents a new lever: faster, smarter chemical exploration—at scale.
Interested in Collaborating?
Whether you’re navigating lead optimization or building a new program from scratch, we’d love to explore how Enki can support your goals.