The most important therapeutic innovations of this century are being slowed by the capital and time required by clinical trials. Billions of dollars and countless hours are poured into clinical R&D every year, yet 90% of that spend and time is wasted on programs that ultimately fail. The high risk/reward ratio of clinical trials incentivizes biopharma companies to pursue safer drug programs at the expense of the bolder scientific breakthroughs necessary to cure diseases like cancer, Alzheimer’s, or even aging.
At Valinor, we believe the way forward lies in harnessing machine learning and massive, patient-derived multi-omics datasets to accurately model the systemic impact of a drug before the first dose is administered.
Biology is not an indecipherable black box; it’s living code that we can model and understand. We’ve built our foundation on the premise that a sufficiently advanced model—trained on perturbed multi-omics and matched clinical assay datasets—can simulate real-world outcomes before any in-human tests.
This isn’t speculative optimism; it’s an inevitability. We will be the first company to build the machine learning architectures and data pipelines required to create virtual patients, not just virtual cells.
The key to our approach lies in the unprecedented data pipelines we’re assembling. Our proprietary partnerships give us the ability to generate large-scale multi-omics data datasets—enabling precise selection of patient cohorts, disease states, clinical assays, cell types, and sampling time points. This ensures the generation of exceptionally clean and comprehensive datasets. Additionally, we are working with leading sequencing platform providers, ensuring high-throughput, batch-free sequencing of patient samples at scale.
This creates a reflexive cycle: more data means more accurate model predictions; more accurate predictions drive better virtual experiments; better experiments generate better data. We can scale our models alongside our customers’ assets, allowing them to continuously fine-tune our models on new data after each development stage.
We are building an end-to-end system where every step—from target discovery to clinical trial design—evolves under the guidance of generative modeling. Instead of tedious guesswork at the bench, we turn labs into automated verification nodes that rapidly test only the most promising hypotheses generated by our models in silico.
Soon, months of trial-and-error experiments will be replaced by accurate simulations run on GPUs, continuously optimizing therapies so that clinical results are confirmations, not revelations.
We are spearheading a new era of drug development where the world of cells innovates as quickly as the world of bits. Our roadmap involves deeper integrations of data, more powerful models, and close collaborations with top-tier labs and industry leaders.
We’re looking for builders who share our conviction that our mission isn’t just possible – it’s necessary. If you’re hell-bent on redefining drug development and powering the development of therapeutics at unprecedented scale, please reach out to us.
Valinor develops advanced ML models trained on proprietary patient-derived samples, capturing comprehensive multi-omic and clinical assay profiles both before and after treatment.
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