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DCVC led the round to support Noetik’s mission to redefine cancer drug development using spatial biology and machine learning
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Noetik is building one of the largest, most differentiated clinical datasets in the industry to drive precision immune therapies
Noetik, an AI drug discovery company leveraging machine learning and proprietary human data to develop precision therapeutics in immuno-oncology, announced that it closed an oversubscribed $14M seed financing round led by DCVC, with participation from Zetta Venture Partners, 11.2 Capital, Catalio Capital Management, Epic Ventures, Intermountain Ventures, North South Ventures, Chau Khuong and CJNV BioVentures, Enveda Founder and CEO, Viswa Colluru and Hummingbird Nomads Fund, and Recursion CEO & CFO, Chris Gibson and Michael Secora.
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“We founded Noetik not only to conduct impactful science, but to bring together some of the most talented people in the industry to build the ML-enabled therapeutics company of the future. One of the most challenging aspects of bringing together biology and machine learning is building exceptional interdisciplinary teams”
Noetik has built an industry-defining multimodal tissue profiling platform that combines self-supervised learning with spatial biology to tackle fundamental problems in cancer immunology. In less than 12 months of operations Noetik has generated hundreds of terabytes of human data across a data stack that includes genomics, transcriptomics, and proteomics to enable the development of foundation models for cell and tissue biology.
The funding will support Noetik’s significant internal data generation efforts and the continued development of transformer-based machine learning methods. From these data, the company aims to rapidly initiate target and therapeutics discovery. The funding will also be used to expand Noetik’s team in machine learning, computational biology, and cancer immunology.
“The microscope enabled a view of nature at the cellular level, now advanced AI methods provide us with a view of nature at the systems level. We can integrate molecular, cellular, and tissue information to discover novel therapeutic targets and study patients’ response to immunotherapies. Science has tended to remove complexity and simplify problems for human understanding, but now using modern machine learning we embrace natural complexity to discover more relevant representations of biology,” said Ron Alfa, M.D., Ph.D., CEO & Co-Founder, Noetik. “We are here to make cancer drugs. Our commitment to patients is to discover better cancer therapeutics that have a higher probability of clinical success, using the most advanced computational tools.”
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“Technology will have the greatest impact on therapeutics by enabling better prediction of clinical success of new oncology therapies. The biggest bottleneck is clinical failure, with an ever-growing discovery funnel. Using machine learning, Noetik can discover the circuit diagram of tumor biology to unlock the next generation of precision oncology for end-to-end therapeutics discovery,” said James Hardiman, General Partner, DCVC.
“The first generation of AI drug discovery was built on supervised learning which relies on human labels that are difficult to scale and limited by existing knowledge. Recent advances in self-supervision allow AI to learn directly from data and build internal representations of biology untethered to existing models of disease. This creates an opportunity to rethink the role of AI in drug development and build new platforms that use foundation models to encode and extract insights from machine-learned biology. We are excited to work with Noetik to apply these ideas to oncology and develop the next-generation of precision therapies,” said Dylan Reid, Partner, Zetta Venture Partners.
Noetik’s founding leadership team includes Lacey Padron, Ph.D., Chief Technical Officer, who was previously VP Informatics at the Parker Institute for Cancer Immunotherapy. At the Parker Institute, Padron focused on using integrated multiomic data analysis to uncover biomarkers, mechanisms of action, and novel therapies from cancer immunotherapy patient data. She also led the development of a best-in-class data platform to store, integrate, and analyze data from 20+ molecular data types with detailed clinical outcomes and annotation.
“We founded Noetik not only to conduct impactful science, but to bring together some of the most talented people in the industry to build the ML-enabled therapeutics company of the future. One of the most challenging aspects of bringing together biology and machine learning is building exceptional interdisciplinary teams,” said Jacob Rinaldi, Ph.D., CSO & Co-Founder, Noetik. “We’re recruiting a world-class, deeply technical team that have built in this space and are passionate to work on solving hard problems to make an impact for patients.”
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