- Enterprise h2oGPTe can answer questions across the papers, abstracts and internal reports you load, and cite the passage behind each statement so a scientist can check it.
- Document AI can pull genes, targets, compounds, diseases and reported findings into structured output that your informatics team can load into the knowledge graph it already runs.
- An agent can watch approved sources and send a cited digest of new evidence for the targets and programs you track, including possible safety signals for your pharmacovigilance team.
- The assistant gathers and cites evidence. It makes no efficacy claim, and your scientists and medical reviewers decide what the evidence means.
The evidence for your target is spread across thousands of papers
Your team needs to know what is already known about a target, a pathway or a compound before it commits a program. That evidence sits in journals, preprints, conference abstracts, patents and years of internal study reports, and each source uses its own names for the same gene or compound.
Your scientists build the picture by hand, one search and one PDF at a time. A review for one target can take weeks, and new papers arrive while it is being written. A contradicting result or a failed approach can sit unread in a paper nobody searched for, and an answer without a source is hard to defend in a portfolio meeting.
How biomedical literature synthesis works
Enterprise h2oGPTe indexes the documents you choose, in your environment, and answers questions over them with citations.
Step 1: build the collection
You load the publications, abstracts, preprints and internal study reports your team already uses into a private collection. Scanned PDFs, tables and figures are read with multimodal AI and intelligent character recognition, so a result that lives in a table is still found.
Step 2: map entities for your biomedical knowledge graph
Document AI can read each paper against a schema you define and return structured output: the entities, the relationship reported and the source passage. Your informatics team decides how those records join the graph it already maintains, and which vocabularies and identifiers it uses.
Step 3: watch the literature for new signals
An agent can check the literature sources you approve on a schedule. It flags new papers that add, confirm or contradict evidence for the targets and compounds you track, and links each item to its source. The same watch can flag case reports that describe a possible adverse drug reaction; those go to your pharmacovigilance team as items to assess, and a medical reviewer decides whether anything is a signal.
Cancer and genomic drug discovery
In oncology and genomics, the questions are specific: which alterations are linked to a tumor type, what is known about a target's role in a pathway, and which approaches were tried and dropped. Agentic retrieval reasons across the retrieved papers and your internal reports and returns a cited evidence brief for each question. It does not predict whether a target or compound will work; your scientists weigh the evidence and decide.
What the assistant does not decide
Every retrieval-based answer carries references to the source passages, so a reviewer can open the paper and catch errors before a claim reaches a program decision. Unpublished results and internal reports stay inside the environment you choose, and collections are private by default.