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Argon AI is a platform where biopharma and life sciences professionals can execute complex and data driven workflows using natural language. We help professionals get thorough answers to questions about clinical trials, existing treatments, healthcare landscape, and the competitive market in minutes rather than months. Prior to starting Argon, Samy was responsible for Flatiron Health’s (Roche) first data analytics project which eventually led to the start of the Flatiron Services business unit. There, he saw first-hand the heavily manual process that biopharma companies struggle through to get the insights they need to drive forward their drug development programs. He also helped close over $6M+ in ARR and is an expert in enterprise pharma sales. Cyrus previously led engineering teams and built 0 to 1 across pre-seed and unicorn startups, managed mission-critical trade generation systems at Bridgewater, and held AI advisory roles at various startups. Cyrus has a duel degree in EE and CS from USC. Breakthroughs in AI present an opportunity to reinvent biopharma and life science workflows to reduce the time and cost of bringing treatments to patients where delays in bringing a drug to market can cost a pharma company $3M / day.

Our Mission: We aim to accelerate and automate chemical process development by equipping wet-lab chemists with the power of data-driven optimization and robotic execution of experiments. The Problem: The discovery of novel pharmaceuticals is one of our most important weapons in fighting disease. However, the drug development pipeline is often held up for many months during the design of chemical processes to manufacture these drugs at scale, delaying FDA trials and lengthening the time until drug launch. Designing chemical processes involves the identification of suitable parameters such as catalyst/temperature/solvent. Currently process development is often done via tedious trial-and-error experimentation (slow) or exhaustive screening (expensive and wasteful). Our Approach: In our research, we have developed algorithms for chemical process optimization, which leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to 95% reduction in required experiments and cost compared to exhaustive screening. We have made our approaches accessible to chemists through our user-friendly no-code software platform and to automated laboratory equipment with our API.