Turbine Appoints Pharma Industry Veteran Bob Silverman As Chief Business Officer
09/29/23, 11:02 AM
Location
london
Industry
health diagnostics
machine learning
artificial intelligence
biotechnology
Position
chief business officer
LONDON & BUDAPEST, Hungary–– Turbine , a digital biology company that deploys empirically validated in silico cell simulations to guide biopharma R&D, today announced the appointment of Bob Silverman as the company’s Chief Business Officer . In appointing a seasoned pharmaceutical executive to this newly created position, Turbine is strengthening its ability to create value from its industry-leading Simulated Cell™ platform across a broad set of biopharma partners as it expands its global presence.
Company Info
Location
london, england, united kingdom
Additional Info
Based in London, UK, with offices in Budapest, Hungary and Cambridge, UK, Turbine was founded in 2016 by Kristof Szalay, Ph.D., Daniel Veres, M.D., Ph.D., and Szabolcs Nagy to overcome the limitations of existing methods in identifying oncology treatments that truly benefit the patients who receive them by combining molecular biology and artificial intelligence (AI). Since its founding, Turbine has developed and validated the Simulated Cell™, a proprietary and cutting-edge platform that runs billions of simulations prior to ever initiating preclinical development guiding real-life experiments with invaluable biological insights. This improves the likelihood of success for truly novel therapies and allows existing assets to be optimally targeted to patients most likely to benefit from them. Turbine’s technology leverages artificial intelligence (AI) to build a constantly evolving, predictive simulation of cellular signaling. These virtual cells are used for in silico experiments having never been run in lab, capturing patient biology better than available experimental models and testing more drug-like effects than current high throughput screening approaches. Validating the uncovered mechanisms and using the resulting data as feedback further improves the model’s capabilities to reveal novel biological mechanisms.