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Artificial intelligence-driven engine
Combining phenotypic patient-derived screening data with AI-drug discovery
Patient-derived phenotypic drug screening is at the core of our approach, providing critical data that fuels our AI-driven de novo design algorithms. By leveraging this data, we design (in silico) novel therapeutic candidates (small molecules and molecular glue degraders), synthesize them, and iteratively retest our predictions on these patient-derived models to refine and optimize their biological activity.
Immuno-oncology degrader
Our immuno-oncology molecular degrader represents a groundbreaking approach in targeted cancer therapy, uniquely bridging protein degradation with immune modulation. Unlike traditional small molecules or biologics, our degrader selectively eliminates oncogenic drivers while simultaneously activating the immune system—creating a dual mechanism that enhances therapeutic efficacy.
Structure informed, multi-featured
A unique aspect of our drug discovery approach lies in our multi-featured drug design. Specifically, our AI/ML models are trained on in-house generated (proprietary) structure-activity data from both single therapy and combination therapy screens. As a result, we can design molecules to exhibit both monotherapy efficacy and synergistic capabilities with therapy X (e.g. antibody, CAR-T/NK, small molecule and PROTAC). Our SIGHT001 immuno-oncology degrader program was also optimized to work synergistic together with specific treatment modalities.
Partnering opportunities
The final outcome of our drug discovery engine is the development of a chemical diverse library of novel small molecules, predicted to possess potent pre-defined properties and with a unique MoA for diverse application fields. Furthermore, the AI/ML algorithms prioritize novelty and bioactivity, giving us a strategic advantage.
Disease area focus
Unique small molecules in oncology and regenerative medicine
Oncology and regenerative medicine represent two critical fields in healthcare, each facing its own set of challenges. From cutting-edge research to innovative treatments, we aim to foster a deeper understanding of the complexities surrounding cancer and tissue regeneration. Join us on our journey to accelerate the development of innovative therapies and improve drug discovery while integrating complex data derived from patient-centric drug screening models at the start of the drug discovery process.
Patient-centric AI-drug discovery
-60%
R&D costs
3
In-house programs
6 months
From screen to in vivo
>10TB
Bio-active screening data(patient-derived models)
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