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Cytovale, based in San Francisco, Calif., is a medical technology company dedicated to revolutionizing diagnostics using cell mechanics and machine learning, and applying this first to sepsis, a condition whose early detection dramatically improves patient outcomes.
GeneGo bioinformatics software is a Saint Joseph, MI-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Cogent Biosciences is a biotechnology company developing real solutions to treat genetically driven diseases. With a focus on rational drug discovery and development, we are leveraging validated biology to advance precision therapies designed to address the true underlying drivers of disease and provide real hope for patients. Cogent`s lead therapeutic candidate, PLX9486 (expected to be named CGT9486 in the future), is a precision kinase inhibitor designed to selectively and potently inhibit the KIT D816V mutation. This mutation is responsible for driving a rare and serious condition called Systemic Mastocytosis which can severely impact many different tissues and organs in the body. We are also studying PLX9486 to treat advanced gastrointestinal stromal tumors (GIST), which have a strong dependence on oncogenic KIT signaling.
EndGenitor Technologies is a Indianapolis, IN-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
insitro is a data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform the way medicines are created to help patients. At insitro, we are rethinking the entire drug discovery process, from the perspective of machine learning, human genetics, and high-throughput, quantitative biology. Over the past five decades, we have seen the development of new medicines becoming increasingly more difficult and expensive, leaving many patients with significant unmet need. We`re embarking on a new approach to drug development – one that leverages machine learning and unique in vitro strategies for modeling disease state and designing new therapeutic interventions. We aim to eliminate key bottlenecks in traditional drug discovery, so we can help more people sooner and at a much lower cost to the patient and the healthcare industry. We believe that by harnessing the power of technology to interrogate and measure human biology, we can have a major impact on many diseases. We invest heavily in cutting edge bioengineering technologies to enable the construction of large-scale, high-quality data sets that are designed specifically to drive machine learning methods. Our first application is to use human genetics, functional genomics, and machine learning to build a new generation of in vitro human cell-derived disease models whose response to perturbation is designed to be predictive of human clinical outcomes.