A new research collaboration between Insilico Medicine and Memorial Sloan Kettering Cancer Center aims to harness generative AI technology to identify novel therapeutic targets for gastroesophageal cancers.
Insilico Medicine and Memorial Sloan Kettering Cancer Center (MSK) have entered into a collaborative research agreement aimed at discovering new therapeutic targets for gastroesophageal malignancies.
The initiative builds on MSK’s global leadership in the field under the direction of Yelena Janjigian, Carroll and Milton Petrie Chair, Chief of GI Oncology and Founding Director of the MSK GEC Therapeutics Accelerator. Her group has delivered multiple practice-changing advances in gastric and gastroesophageal junction cancers.
Patrick Evans, Senior Project Manager in the Janjigian Lab, will serve as the MSK project lead, supporting the integration of scientific, operational and translational efforts across teams.
AI platform to accelerate target discovery
Insilico’s multidisciplinary team will deploy its PandaOmics platform and other proprietary technologies to accelerate the discovery of new disease mechanisms and target hypotheses.
Insilico’s multidisciplinary team will deploy its PandaOmics platform and other proprietary technologies to accelerate the discovery of new disease mechanisms and target hypotheses.
PandaOmics is an AI-driven biological data analysis platform developed to speed up drug target discovery. It integrates advanced multidimensional artificial intelligence and bioinformatics models with multimodal omics and biomedical text data to identify therapeutic targets and biomarkers. The platform incorporates more than 20 proprietary AI and bioinformatic models designed to systematically prioritise druggable biological targets with high translational potential.
A key aspect of the collaboration is the integration of MSK’s extensive multiomic clinical datasets into PandaOmics. MSK will contribute high-quality patient genomic, proteomic and transcriptomic data, alongside deeply annotated clinical cohorts. This will provide a solid foundation for systematic analysis and identification of disease drivers across diverse gastroesophageal cancer subtypes.
By combining MSK’s clinical and translational expertise with Insilico’s AI-powered platforms, the joint teams aim to discover actionable targets and biological pathways that could lead to more personalised therapeutic options and improved outcomes for patients.
Phased research approach
The collaboration is currently focused on data gathering, quality control and integration. Subsequent phases are expected to involve AI-driven hypothesis generation, target ranking and detailed biological investigation.
The project will also support the evaluation and advancement of identified targets across a range of therapeutic modalities, including biologics and small molecules.
"This collaboration with MSK brings together leading clinical oncology expertise with our generative AI platforms," said Dr Alex Zhavoronkov, Founder and CEO of Insilico Medicine. "Gastroesophageal cancers remain among the most challenging solid tumours. By integrating MSK’s exceptional clinical data resources with our target discovery technologies, we aim to identify meaningful biological insights and accelerate the development of new therapeutic options for patients worldwide."
Accelerating drug development
Harnessing state-of-the-art AI and automation technologies, Insilico has sought to improve the efficiency of preclinical drug development and set new benchmarks for AI-driven research and development.
By combining patient-level clinical and molecular data with transformative AI tools, we can accelerate the discovery of clinically meaningful targets.
While traditional early-stage drug discovery typically takes an average of four and a half years, the company nominated 20 preclinical candidates between 2021 and 2024. The average timeline from project initiation to preclinical candidate nomination was between 12 and 18 months per programme, with only 60 to 200 molecules synthesised and tested in each case.
“GEC patients need new breakthroughs, and those breakthroughs must come from a deeper understanding of each patient’s unique disease biology. By combining patient-level clinical and molecular data with transformative AI tools, we can accelerate the discovery of clinically meaningful targets and move more personalised therapies toward patients who need them most, in real time and faster,” said Dr Janjigian.
