Compugen discovers novel drug targets through a unique, predictive, computational process that combines human biology derived from genome analysis with disease information derived from analysis of vast amount of proprietary and publically available data. This process, which is generally applicable to many fields of medical interest, usually results in multiple drug target candidates. Our comprehensive data analysis therefore enables us to often identify first-in-class drug target candidates, which are difficult to identify using traditional screening approaches.

Built on the understanding and mathematical modeling of biological phenomena, our discovery process is flexible enough to be applicable to a wide range of indications and diseases. Our computational tools were tested and optimized for over a decade and have been successfully applied to many biological phenomena, resulting in findings which were published in dozens of first-tier peer reviewed scientific journals. The applicability of the infrastructure was demonstrated internally in multiple therapeutic and diagnostic areas and, in contrast to traditional discovery methodologies, demonstrated significant advantages in terms of cost, time and most importantly, probability of successful validation.

Our in silico discovery process is followed by a therapeutically focused validation phase, in which the novel candidates undergo an extensive set of experiments to confirm the in silicoprediction and provide additional detailed data as to their relevance and potential as drug targets for therapeutic development.


Predictive Discovery

Our process for in silico discovery of novel drug targets

Target Discovery

An overview of our process for the discovery of B7/CD28-like and other immunomodulators

Target Validation

Therapeutically focused drug target validation pipeline


From Code To Cure Computational Identification, Functional Characterization and Antibody Blockade of a New Immune Checkpoint in the TIGIT Family of Interacting Molecules

Ofer Levy, Chris Chan, Gady Cojocaru, Spencer Liang, Eran Ophir, Sudipto Ganguly, Maya Kotturi, Tal Friedman, Benjamin Murter, Liat Dassa, Ling Leung, Shirley Greenwald, Meir Azulay, Sandeep Kumar, Zoya Gluzman, Xiaoyu Pan, Arthur Machlenkin, Andy Dr
Nov 2016

Computational Discovery and Experimental Validation of Novel Drug Targets in Immuno-oncology

Arthur Machlenkin, Ofer Levy, Galit Rotman, Amir Toporik, Gady Cojocaru, Yair Benita, Liat Dassa, Tal Fridman, Ilan Vaknin, Shirley Sameah-Greenwald, Inbal Barbiro, Spencer Lang, John Hunter, Eyal Neria, Zurit Levine
Nov 2015

Establishment of a New Target ADC Portfolio Derived from Predictive Discovery

Kyle Hansen, Gady Cojocaru, Andrew Pow, Andrew Drake, Liat Dassa, Itay Spector, Mark White and Mary Haak-Frendscho
Oct 2015

Computational identification of natural peptides based on analysis of molecular evolution.

Toporik A, Borukhov I, Apatoff A, Gerber D, Kliger Y.
May 2014