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TransMolecular, Inc. is a Cambridge, MA-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Cerno Bioscience is a company that develops and publishes mass spectrometry calibration and analysis software that extends the limits of mass spectrometry.
We founded Cedilla at a time of fundamental and rapidly growing insight into the mechanisms underlying protein stability. Small molecules that degrade or stablize target proteins are precedented, but have been discovered serendipitously. Cedilla is taking an integrated approach to discover new drugs with this mode of action. Our present focus is to develop novel medicines in oncology, but this approach will also be applicable to other fields, including central nervous system disorders and genetic diseases. Cedilla launched with $56.2 million in Series A funding from Third Rock Ventures.
Argene SA is a Massapequa, NY-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.