A proprietary, validated AI/ML powered platform for novel target discovery and development of
potential first-in-class cancer immunotherapies

Advancing immuno-oncology with an AI/ML powered platform

Immuno-oncology represents a paradigm shift in cancer treatment. Although current biological drugs can extend life expectancy
for certain cancer types and patient populations, the potential of immuno-oncology remains largely untapped, with success rates
of only 20-30% in specific cancer indications. We believe that the identification of novel drugs and biological pathways hold the
potential to broaden the reach of cancer immunotherapies to more cancer patients.

Compugen has been the at the forefront of decoding of cancer biology, revolutionizing immuno-oncology therapies with its
computational discovery platform, Unigen™. This innovative platform combines proprietary AI/ML technology-agnostic cloud-based
tools with vast human expertise, enabling the agile discovery of novel drug targets and the continuous development of first-in-class drugs.

The Unigen™ edge

Integrating AI/ML with human expertise in a continuously enriched flexible-loop platform

Unigen™ combines Compugen’s deep scientific knowledge, AI/ML predictive algorithms and a cloud-based, technology-agnostic platform integrating a variety of biological datasets to enable discovery of novel drug targets and potential first-in-class cancer immunotherapies


A flexible-loop platform for novel IO drug target discovery & development

A flexible-loop platform for novel IO drug target discovery & development

Validated, predictive discovery of novel immuno-oncology drug targets

Discovering drug targets demands an integrative, multi-dimensional approach that relies on a sophisticated toolkit to achieve optimal results; this, together with the scientific intricacies of biology requires us to develop a system that isn’t bound to any specific technology

First-in-class-drug target candidate selection

An experimental validation phase to assess the accuracy of our in-silico predictions and gain a deeper scientific understanding of the newly identified drug targets and biological pathways; this data enhances our computational platform

Biomarker driven strategy

Leveraging our newly discovered biological pathways, we identify the tumors and subtypes most likely to respond to the novel pathway modulation. Following the indication selection, we aim to identify new biomarkers to more accurately forecast patient responsiveness to our novel therapies. This long-term approach also seeks to improve the probability of success of our clinical studies