BenchSci provides a neuro-symbolic AI platform, ASCEND, designed to accelerate drug discovery by decoding complex disease biology. The platform utilizes a Biological Evidence Knowledge Graph (BEKG) that integrates public, licensed, and proprietary client data to ensure evidence-backed scientific reasoning. This system delivers AI copilots and co-scientists that help biopharma R&D teams move from hypothesis to successful experiments faster.
Funding
$174.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
GIFounders
Product
Problem
Pharmaceutical research and development faces challenges in efficiently understanding complex disease biology, leading to delays and increased failure rates in drug discovery. Scientists struggle to navigate vast amounts of biomedical data, hindering their ability to identify novel connections and make informed decisions.
Solution
BenchSci's ASCEND platform addresses these challenges by providing a disease biology generative AI platform tailored for pharmaceutical R&D. ASCEND leverages multimodal AI and ontologies to analyze extensive biomedical datasets, including closed-access papers, patents, preprints, and internal pharma data. The platform acts as an AI assistant, guiding scientists through research complexities, elevating biology-related decision-making, and uncovering novelty while increasing experimental productivity. By decoding and harmonizing data from diverse sources, ASCEND helps scientists generate better ideas and identify optimal experimental paths, ultimately accelerating the discovery of novel medicines.
Target Audience
The primary users are drug research scientists in the pharmaceutical industry who aim to accelerate drug discovery by leveraging AI to understand disease biology and improve experimental outcomes.
Features
- Multimodal AI that understands experiments from both text and figures
- Access to a comprehensive biomedical dataset, including closed-access papers and patents
- Proprietary and robust ontology knowledge base (OKB)
- Integration of internal pharma data (ELNs, SharePoints, materials)
- Specialized AI assistants with a deep understanding of pharma workflows
- GUI interfaces and collaboration areas
- Enterprise-ready integrations and report generation
- In-silico prediction capabilities