Variational AI partnered with Rakovina Therapeutics to support the discovery of a brain-penetrant ATR inhibitor using our Enki™ generative AI platform. By generating and prioritizing novel, CNS-targeting compounds aligned with a complex target product profile, Enki helped accelerate Rakovina’s transition from in silico design to preclinical testing—demonstrating how AI can advance oncology programs facing challenging design constraints.

QSAR models struggle with extrapolation compared to conventional ML tasks like image recognition. Machine learning generalizes effectively when structured to align with its problem domain, suggesting that improving QSAR models may close this gap in drug discovery.

Generative AI in drug discovery is showing promise by optimizing molecule searches beyond traditional methods. Variational AI’s Enki algorithm created 100 AI-generated molecules that outperform 1,000,000 in conventional high-throughput screening, highlighting AI’s efficiency in exploring chemical space.

Traditional QSAR models are limited to interpolation within known chemical spaces, restricting drug discovery. In contrast, modern machine learning excels at extrapolation, opening new possibilities for exploring untapped chemical compounds and enhancing hit discovery.