CEO Yeremia Gizarianz argues that their success stems from decades of deep scientific research, not from AI itself. He positions AI and machine learning as essential tools for accelerating and scaling the core science, rather than being the foundational driver of the company. This distinguishes them from hype-driven AI-centric ventures.
Soleil moves beyond the single-target model by mapping the entire flow of information a drug creates within a cell. They argue that even approved drugs have 30-40 other effects. By understanding the global cellular response from day one, they aim to better predict both efficacy and toxicity, addressing a key failure point in traditional discovery.
By focusing on the phenotypic outcome (cellular stress) rather than a predefined target, Soleil's platform can identify small molecules that modulate proteins considered undruggable by conventional means. Their lead oncology candidate, for example, modulates CCAP2, demonstrating the platform's ability to find novel biology and expand the druggable space.
Instead of the traditional 'disease-target-drug' approach, Soleil finds compounds that create a desired cellular change first. Only after identifying a promising, well-tolerated molecule with a known cellular mechanism do they use bioinformatics to determine which disease and patient population it's best suited for.
Before launching the company, the founders spent over a decade validating their platform at UCSF, funded by $7-8 million in philanthropic grants. This long-term, non-dilutive de-risking of the core science allowed them to approach VCs with a proven, data-producing platform, rather than just abstract claims.
The company's foundational insight is that cellular stress is a central mechanism in vastly different diseases. In cancer, they increase stress to kill cells; in degenerative conditions like Parkinson's or hair loss, they aim to decrease stress to restore function. This unifying principle allows their single platform to tackle a diverse therapeutic portfolio.