Next-Gen Superior Drugs Require Targeted Insights
The development of safer and more effective drugs requires granular understanding of the disease-relevant cell populations. However, due to the heterogeneity of the disease states and the diversity of the immune system, the disease relevant cells are like needles in a haystack. To study these cells in a population, we need a sensitive platform at the million-cell throughput to identify and comprehensively characterize them. However, no existing technologies can achieve this with acceptable speed and cost.
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What We Do
We combine ultrafast screening with advanced AI models to greatly speed up drug development. We’ve developed a proprietary platform -- TCELERATORTM that functionally profiles individual cell-cell interactions at ultrahigh speed followed by multidimensional computational analysis. We can process 10s of millions of events in one go, functionally characterize each one of them and uncover new insights within rare populations. We also curated a proprietary multidimensional large dataset of tumor and normal samples. The combination of TCELERATORTM and the dataset enabled us to build advanced custom AI models that halve the time of development while ensure safety by predicting potential clinical failures. This allows us to develop effective drugs not just faster, but more safely.
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OUR ADVANTAGES
Functional Dataset and Broad Spectrum AI Engine to Discover Novel Targets
We developed the most comprehensive AI engine PINTATM that integrates multiple dimensions of data from a large set of tumor and normal samples. It enables us to discover therapeutically relevant, novel targets for 'undruggable' diseases, providing a therapeutic path for patients with limited or no treatment options.
Functional Ultrafast Screening to Find Best Therapeutic Binders: TCELERATORTM
TCELERATORTM platform probes 10s of millions of functional interactions dynamically, which allows us to screen for the best therapeutic binders efficiently.
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AI-Driven Deep Molecular Scanning to Predict Potential Adverse Reactivity for Clinical Safety: SERISTM
Our AI model SERISTM searches wide and deep into molecular details of normal human tissues to look for potential cross reactivity with the drug candidate. This approach has enabled us to successfully predict 100% of previously reported toxicity events, providing a powerful safeguard for our drug design process.
