Tobias Kind, Leader in Computational Metabolomics, Joins Enveda Biosciences

Enveda Biosciences, a pioneer in small molecule drug discovery using machine learning and metabolomics to harness the potential of natural products, announced that Tobias Kind, Ph.D., will join the company’s industry-leading data science team as Research Fellow. Dr. Kind is one of the world’s most respected and highly cited researchers in computational metabolomics and natural products research. He will play a key role in driving continued data science innovation for Enveda’s drug discovery platform and the company’s leadership in metabolomics.

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“We are excited to bring together Dr. Kind’s strong record of producing significant innovations and the advances that Enveda’s team already has achieved. Dr. Kind will play an important role in our efforts to continue to build a discovery platform that is even more powerful, precise and efficient.”

Dr. Kind brings more than 25 years of experience to Enveda through his transforming work in the use of mass spectrometry (MS) for the structural elucidation of small molecules through machine learning, quantum chemistry and other technologies. He also is widely noted for his articulation of the “Seven Golden Rules,” the use of in silico MS/MS databases and the use of retention time prediction with mass spectrometry, all of which improve biological interpretations of metabolomics data.

“Dr. Kind has extraordinary expertise, particularly with the application of new technologies to the structural elucidation of small molecules, including natural products like those we are targeting,” said Viswa Colluru, Ph.D., Enveda’s CEO. “We are excited to bring together Dr. Kind’s strong record of producing significant innovations and the advances that Enveda’s team already has achieved. Dr. Kind will play an important role in our efforts to continue to build a discovery platform that is even more powerful, precise and efficient.”

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Before joining Enveda, Dr. Kind was a team leader and senior researcher at the University of California, Davis. His research focused on identifying the structures of small molecules with mass spectrometry, predicting in silico mass spectra with quantum chemistry, and using machine learning for mass spectral predictions and small molecule identifications.

“It’s a privilege to be a part of Enveda’s team,” Dr. Kind said. “Even before I joined Enveda, I was impressed with the company, its leadership and the caliber of the data science team. I have been particularly struck by their innovative advances in spectral inference, an interest of mine, which give Enveda’s process significant advantages. I am looking forward to working with my new colleagues to identify new ways to make Enveda’s platform even more powerful.”

Prior to his nearly two decades at UC Davis, Dr Kind was a postdoctoral fellow at Max-Planck-Institute of Molecular Plant Physiology, Golm, Germany, one of the world’s most prestigious research institutions. He was affiliated with the UFZ-Environmental Research Centre Leipzig-Halle’s Department of Chemical Ecotoxicology prior to his time at the Max Planck Society. He earned his Ph.D. and M.S., both in analytical chemistry, from the University of Leipzig, Germany.

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