Predictive Oncology Inc. (NASDAQ: POAI) Finalizes Acquisition Deal Expected to Revolutionize Role of AI-Driven Predictive Models

• POAI completes acquisition designed to accelerate commercialization of proprietary AI-driven drug discovery, development
• Strategic move allows for one-of-a-kind, end-to-end ‘discovery machine’ that can rapidly generate potential therapeutic candidates
• Company to offer new capability to pharmaceutical company customers in revenue-generating projects later this year

Predictive Oncology (NASDAQ: POAI), a knowledge-driven company focused on applying artificial intelligence (“AI”) to personalized medicine, has completed its acquisition of Quantitative Medicine (“QM”) (http://ibn.fm/vqmwL). POAI signed a letter of intent to acquire QM, a biomedical analytics and computational biology company, in January (http://ibn.fm/4iRTV). The strategic move should accelerate the commercialization of Predictive Oncology’s proprietary AI-driven drug discovery and development.

“This acquisition will enable us to further leverage our unique database of drug-response and genomics profiles that our subsidiary, Helomics, has gathered from more than 150,000 cancer cases over more than 10 years of clinical testing,” said POAI CEO Dr. Carl Schwartz. “Integrating QM’s proven machine-learning platform, CoRE, with our proprietary database of drug response and genomics profiles is expected to revolutionize the role of our AI-driven predictive models in the discovery and development of new anti-cancers.

“We will be able to more quickly understand how specific types of tumors react to cancer drug therapies,” Schwartz continued. “This will allow our customers to accelerate the development and commercialization of personalized patient treatments that dramatically improve patient outcomes. We intend to offer this new capability to our pharmaceutical company customers in revenue generating projects this year.”

Founded by Dr. Robert Murphy and Dr. Joshua Kangas, both of Carnegie Mellon University, QM has developed a novel, computational drug-discovery platform called CoRE. CoRE is designed to dramatically reduce the time, cost and financial risk of discovering new therapeutic drugs by predicting the main effects of drugs on target molecules that mediate disease.

“CoRE is a predictive model-building platform for drug screening and optimization campaigns that uses hybrid machine learning approaches to build predictive models rapidly and drive wet-lab experimentation,” said Schwartz. “Unlike the approach of many AI companies working purely ‘in silico,’ our approach will unite the CoRE approach with our PDx tumor-profiling platform and tumor-data database, allowing for a one-of-a-kind, end-to-end ‘discovery machine’ that can rapidly generate potential therapeutic candidates in a cost-effective manner. Therapeutic candidates developed by this iterative AI and experiment cycle can be fast-tracked, since there will already be demonstrated activity in preclinical laboratory tests rather than just a computer model.”

The acquisition was completed in an all-stock transaction valued at approximately $1.8 million, Under the terms of the purchase agreement, POAI issued 954,719 shares of common stock at a valuation of $1.833 per share.

POAI is bringing precision medicine, or tailored medical treatment using the individual characteristics of each patient, to the treatment of cancer. Through the company’s Helomics division, the company leverages its unique, clinically validated patient derived (“PDx”) smart tumor profiling platform to provide oncologists with a road map to help individualize therapy. In addition, the company is utilizing artificial intelligence and its proprietary database of over 150,000 cancer cases tumors to build AI-driven models of tumor drug response to improve outcomes for the patients of today and tomorrow

For more information, visit the company’s website at www.Predictive-Oncology.com.

NOTE TO INVESTORS: The latest news and updates relating to POAI are available in the company’s newsroom at http://ibn.fm/POAI

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