Traditionally, companies test for molecular compatibility using a method dubbed "spray and pray." Millions of molecules are essentially launched at a target, while researchers hope to be able to note which molecules appear to be effective.

But this trial-and-error strategy is both incredibly costly and time-consuming, involving thousands of hours of lab time, workforce, and potential waste of resources — all ultimately leading to a low chance of success.

Evogene’s platform revolutionizes molecular development by tapping into billions of data points to efficiently identify the most promising candidates for a specific life science product. The inherent challenge, however, was having sufficient computing power to process nearly unlimited data points. On-premise capacity proved limited, and Evogene required rapid processing of vast datasets to make complex molecular simulations and ultimately accelerate life science research.

Turning to Google Cloud, Evogene’s new nearly-unlimited infrastructure now facilitates Evogene's large-scale data processing, enabling their teams to engage in deep analysis and molecular screening, positioning their partners to slash their development times by half.

"With our growing data demands, we needed a flexible infrastructure solution that could grow alongside us," says Ilia Zhidkov, VP of Computational Technologies at Evogene. 

An infrastructure solution offering scalability and agility in a fast-evolving, fluid environment was essential to support Evogene's dynamic needs.

"We moved to Google Cloud seeking a true ally — someone who would collaborate with us, not just provide cloud services," says Zhidkov.

"Flexibility was a major factor in why we chose Google Cloud. They met our tight timelines and adapted on short notice. Google’s agility and commitment to match our innovative pace felt like working with a start-up, but with the experience of a corporate giant. With Google Cloud, we are able to optimize our innovative discovery process," Zhidkov says.

Zhidkov emphasized that Google Cloud provided not only the logistical support Evogene needed for in-depth data analysis and its daily operations, but also delivered stellar guidance and advice that went beyond the industry standard, contributing significantly to Evogne’s ability to increase the probability of success.

Evogene's development of a foundation model for small molecule drug discovery is another ongoing collaborative effort with the Google Cloud team. This ambitious project, aimed at revolutionizing drug discovery by generating novel small molecules, is currently in progress. A key component of its development plan involves leveraging Vertex AI to train the model on an expanding dataset, starting with millions, then billions, and ultimately reaching a training set of approximately 40 billion small molecules.