, extending the deployment of its neurosymbolic artificial intelligence platform, ASCEND, within Merck’s early-stage research workflows.){kind=link}
BenchSci has renewed a two-year agreement with Merck (known as MSD outside the United States and Canada), extending the deployment of its neurosymbolic artificial intelligence platform, ASCEND, within Merck’s early-stage research workflows. The continuation of the collaboration reflects ongoing use of the platform to support hypothesis generation, evaluation of disease-biology evidence, and decision-making across discovery efforts.
Seeing Merck’s teams use ASCEND to unravel disease biology, strengthen hypotheses, and make more evidence-driven decisions underscores the real scientific value AI can deliver. This renewed agreement gives us the opportunity to deepen that impact and continue advancing how complex biological questions are explored in early discovery.
Liran Belenzon, CEO and Co-Founder, BenchSci
The renewed agreement aligns with Merck’s broader efforts to incorporate artificial intelligence into scientific research processes. Through continued platform access, researchers can integrate literature-derived knowledge with multi-omics datasets and clinical evidence to conduct structured evaluations of disease pathways and therapeutic targets.
At the core of ASCEND is the Biological Evidence Knowledge Graph (BEKG), a curated framework that formalizes experimentally validated relationships drawn from peer-reviewed publications, omics data, and clinical trial sources. The BEKG is coupled with foundation models designed to support inferential reasoning and the generation of interpretable outputs. Together, these components provide AI-enabled tools for navigating large-scale biological datasets and extracting mechanistically relevant insights.
ASCEND also incorporates internal partner data into private extensions of the knowledge graph, enabling organizations to establish customized biological reference maps. These secure, continuously updated datasets are intended to support longitudinal discovery programs by integrating emerging experimental findings with the existing evidence base.