Our technological pillars aim to exploit a confluence of preclinical and patient-derived endpoints to deconvolute complex molecular and cellular networks, reprogram the fate of a cell to leverage pharmacological and biological synergies.
Our suite of proprietary algorithms drastically accelerate the validation of new hypotheses on how cellular pathways are reprogrammed, ultimately leading to shortened cycle times and a higher success rate for discovering novel medicines.
1. Wooten et al., Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers.
5. Udyavar et al., Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.
- Integrates a constellation of big data from complex diseases to generate prioritized and testable therapeutic hypotheses in a systematic and agnostic manner
- Our proprietary explainable AI platform overcomes the “curse of dimensionality” and focuses our biological validation onto the most plausible hypotheses
- Achieves new levels of efficiency in Drug Discovery cycle times
- Increases translational success of novel targets and co-targets in patients