WHAT IF YOU COULD PREDICT MOLECULES TO MAKE THE CAUSE OF DISEASE DISAPPEAR?
Differentiated Therapeutics (DXTX) has built the first end-to-end computational engine for Targeted Protein Degradation (TPD) which drives the discovery and optimization of novel bifunctional molecules designed to target the genetically defined drivers of disease.
Due to the highly heterogenous nature of pharmaceutical development, our field has yet to experience an organization built from the ground-up with an end-to-end platform that deploys a custom toolkit of applications for specific problems in the drug development process. DXTX fills this gap. We believe our organization can only be successful by discovering differentiated therapeutic development strategies for which our platform drives significant value.
Our AutoDx platform consists of a suite of tools that enable differentiable portfolio planning by mining unstructured data to identify TPD development strategies for targets with indications of high unmet need and patients with significant market valuation. AutoDx significantly reduces time reviewing literature, assessing patents, and manually designing molecules providing our scientists more time to focus on experimental design and high quality data collection. To compute TPD development strategies, AutoDx precision engineers compound design and molecular dynamics to assess platform performance and predict likelihood of successful strategy execution. Key components of our platform include biological discovery, multiobjective design, and translational studies.
Our carefully engineered systems combine advanced differentiable design with experimental validation to rapidly progress our TPD development strategies. The AutoDx platform is better at learning which bifunctional molecules meet the complex project-specific criteria required for successful clinical development than traditional human-led design, enabling a more efficient and effective trajectory to a development candidate. Every program in our pipeline represents hope for an important segment of patients in need of a treatment. By targeting the known drivers of disease, we are engineering the integration of AI, molecular simulation, and biomedical informatics to develop drugs that show promise of becoming safe, effective TPD therapies.