SALT LAKE CITY--(BUSINESS WIRE)--Valinor Discovery, a company pioneering machine learning-powered simulation of therapeutic performance to derisk drug development, today exited stealth to announce their mission to train the first clinically translatable models on matched primary cell and clinical assay datasets. Valinor is currently generating data from proprietary matched multi-omics and oncology-focused clinical assay datasets to create virtual patient profiles that can be used to accelerate clinical drug development.
Valinor's approach represents an exciting step forward in predictive drug development.
“Valinor was founded on the belief that with enough patient-derived data, machine learning will transform clinical drug development from a slow, costly, and high-risk undertaking into a streamlined process where therapeutic efficacy against clinical endpoints has already been simulated computationally,” said Josh Pacini, Co-Founder and CEO of Valinor Discovery.
“By linking molecular perturbations to actual clinical assay data, we will empower drug hunters to test therapies virtually in a matter of weeks before investing years at the bench or in the clinic,” said Zhanel Nugmanova, Co-Founder and Chief Scientific Officer of Valinor Discovery.
Valinor is excited to announce its collaboration with the Computational Health Center at Helmholtz Munich to develop new industry benchmarks for drug perturbation models. Reliable benchmarks for drug perturbation models across drug modalities and therapeutic areas are crucial for the biotech industry. Professor Fabian Theis and his research team are leaders in applying machine learning models to perturbation prediction and single-cell genomics.
"Valinor's approach represents an exciting step forward in predictive drug development. We are thrilled to collaborate with them to develop more robust and clinically translatable benchmarks for drug perturbation prediction models, which we will add to the popular OpenProblems platform," said Dr. Theis.
Valinor is also announcing a collaboration with the Montgomery Lab at Stanford Medicine, led by Dr. Stephen Montgomery, Professor of Pathology, Genetics, and Biomedical Data Science and a leading expert in functional genomics. Renowned for its contributions to the Genotype-Tissue Expression (GTEx) project and transcriptomic studies of complex disease, Dr. Montgomery’s lab will work with Valinor to apply its perturbation models to identify and assess compounds with the potential to treat Alzheimer’s Disease.
Valinor has also partnered with Latch Bio to offer a fully compliant, hosted web portal that gives Valinor customers easy access to their model and supporting plug-and-play preclinical and clinical workflow automation tools.
In addition to actively generating its own patient-matched pre- and post-treatment multi-omics datasets, Valinor is actively collaborating with leading -omics sequencing companies to provide clinical-stage biopharma with custom models trained on data from their ongoing and past clinical trials.
Strategic and Scientific Advisors Appointed
Valinor has also recruited a number of biopharma and academic leaders as advisors, including current and former executives in machine learning, business development, clinical operations, and drug discovery, they include:
Stephen Montgomery, PhD, Head of the Montgomery Lab and Endowed Professor of Pathology, Genetics, and Biomedical Data Science, Stanford University
Chase Neumann , PhD , Associate Director of Oncology, Recursion
Matt Donne, PhD, Former Head of Operations and Chief of Staff, Spring Science
Bryan Norman, PhD, Former SVP of Lead Generation Chemistry at Enveda Biosciences and Senior Researcher, Eli Lilly
Tim Sullivan, PhD, Chief Business Officer, Infinimmune
About Valinor Discovery
Valinor Discovery is building a new foundation for therapeutic R&D using generative machine learning combined with matched pre- and post- treatment multi-omics data longitudinally collected from the same patients. Valinor is currently developing a suite of models to empower drug developers to predict transcriptomic shifts, protein abundance levels, methylation changes, and clinical assay outcomes before a single patient is dosed. By reducing trial-and-error and de-risking development, Valinor aims to accelerate breakthroughs across therapeutic areas, starting with oncology. To learn more about Valinor’s approach or collaborate with them on customized perturbation models for specific assets, diseases, or clinical endpoints, please visit www.ValinorDiscovery.com and follow Valinor on LinkedIn and X.
