ZebiAI Therapeutics, Inc. Announces Collaboration With Google Accelerated Science to Focus on Unlocking Novel Therapeutic Targets and Transforming Small Molecule Drug Discovery

Companies Are Combining Machine Learning and DNA Encoded Library Technologies in AI-Driven Drug Discovery, With an Initiative to Drive Academic Chemical Probe Enablement

ZebiAI Therapeutics, Inc., a privately held company, announced a multiyear service and collaboration agreement with Google Accelerated Science (GAS), Mountain View, CA, focused on the application of machine learning to accelerate the identification of small molecule therapeutics. ZebiAI Therapeutics was established to apply machine learning and DEL technologies to revolutionize target and drug candidate identification.

“Through rigorous testing and innovation, our scientists have demonstrated the power of combining machine learning and DNA encoded library screening data to enhance lead discovery and optimization”

Through the Chemome (“Chemistry of the Genome”) Initiative, ZebiAI and GAS are focused on collaborating with academic researchers to further characterize the function of understudied proteins and validate novel therapeutic targets. Thousands of proteins remain understudied with limited or complete lack of understanding about their function and/or relevance to disease pathophysiology. As a result, there is untapped potential for major scientific advances within the unexplored proteome. ZebiAI and GAS have partnered to develop chemical probe molecules for the academic community across thousands of novel targets through application of machine learning and DEL technologies to the acceleration of candidate drug discovery.

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“There is a critical need for better chemical probes to understand disease pathways as a first step towards drug discovery. We are excited that our machine learning research with ZebiAI is enabling the Chemome to tackle this important problem,” said Jason Miller, Product Management Director for GAS. “We expect the Chemome will spur significant new biological discoveries and ultimately accelerate new therapeutic discovery.”

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“Through rigorous testing and innovation, our scientists have demonstrated the power of combining machine learning and DNA encoded library screening data to enhance lead discovery and optimization,” said Christelle Huguet, CSO of ZebiAI. “Since machine learning methodology applied to DEL data does not rely on protein structure information, ZebiAI’s approach also opens up further target space for AI-driven drug discovery.”

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