How do you make a large biological network actually actionable?
My explainable model maps to biology but is still too complex to act on, how do I turn a large network into prioritised, interpretable decisions?
An explainable network whose nodes map to genes and pathways can still be too complex to act on, and a connection to an outcome doesn't tell you a node's role. We layered an LLM over a Reactome-derived network for an HR-deficient cancer: it surfaced key pathways, a candidate target, and a repurposing lead, with each node role-scored across 300+ tools.
Who this is for: AI/ML and computational-biology teams who need to interpret large biological or causal networks and act on them.
- connected tools used to score each node's role
- 300+
- explainable network built from pathway data
- Reactome
- surfaced via LLM-driven literature search
- 1 repurposing lead
connected tools used to score each node's role
explainable network built from pathway data
surfaced via LLM-driven literature search
Explainable isn't the same as interpretable
Even a fully explainable neural network, one whose nodes map to genes, pathways, and outcomes, can be hard to act on. The sheer number of nodes and relationships makes it difficult to see what matters, and a connection to a clinical outcome alone doesn't tell you whether a node is a target, a biomarker, or a surface receptor.
Teams working with large causal or biological networks need to do three slow, expert-intensive things at once: interpret the results, prioritise the nodes, and merge new proprietary data with existing knowledge. None of it scales by hand.
What teams in this space search for
- How do I interpret a large biological or causal network?
- What's the difference between explainable and interpretable AI?
- Can LLMs find drug-repurposing opportunities?
How we solved it with Hydra
“We have an explainable network built from Reactome data for a cancer driven by homologous-recombination deficiency without the usual BRCA1/2 mutations. Layer an LLM over it to identify the key upregulated genes and pathways, surface candidate targets, and search the literature for repurposing opportunities. For the uncertain nodes, score each one's role, druggability, location, essentiality, competing compounds, and sort them into targets, biomarkers, and receptors.”
What Hydra ran
The network mapped nodes to biological functions, but was too dense to read directly, so the Causal-Network Interpretation module layered a large language model over it to investigate whether signals were being missed between high- and low-HR-deficiency patients.
For nodes whose role was uncertain, it scored each one, druggability, cellular location, essentiality in healthy versus diseased cells, and any competing compounds, using more than 300 connected tools and databases (UniProt, AlphaFold, DepMap, ChEMBL and others), then sorted them into targets, biomarkers, and receptors.
What it found
The LLM layer identified the key genes and pathways upregulated in HR-deficient patients and surfaced a candidate target that hadn't been prominent in the indication's prior literature.
An LLM-driven literature search then found that a drug originally developed for a different cancer could plausibly be repurposed for this indication, a concrete, human-checkable lead pulled out of a network no one could read by eye.
What we learned
Explainability tells you how a model reached an output; interpretability tells you which nodes matter and what role each plays. The LLM layer plus node-role scoring is what bridges the two and turns a graph into a decision.
Some discovery decisions can't be reduced to fixed rules, which confounder, which subgroup, which edge to trust shifts as data arrives. A human-in-the-loop loop captures that judgment, highlighting and prioritising what matters while saving expert time, and can be pointed back at the network to drive the next experiment.
What you get
- Key pathways upregulated in HR-deficient patients identified from a complex network
- A candidate target surfaced that wasn't prominent in the indication's prior literature
- A drug-repurposing lead found via LLM-driven literature search
- Every node role-scored and explained, druggability, location, essentiality, competition
Data sources used
- Reactome (pathway network)
- UniProt & AlphaFold (protein roles & structure)
- DepMap (essentiality healthy vs diseased)
- ChEMBL & primary literature (competition, repurposing)
Figures reflect analyses PharosBio ran on public datasets and public benchmarks. Named competitors, collaborators, and logos are withheld at this stage; the methods and results shown are real and repointable to your own target.
Sources & methods
- Pathway network: Reactome
- Node-role scoring: UniProt; AlphaFold; DepMap; ChEMBL
Frequently asked questions
What's the difference between explainable and interpretable AI here?
Explainability tells you how a model reached an output, but a network with thousands of nodes can still be too complex to act on. Interpretability, knowing which nodes matter and what role each plays, is what turns the model into a decision, and that's where the LLM layer plus node-role scoring adds value.
Can an LLM really find drug-repurposing opportunities?
Yes. In this case, scoring node roles and running an LLM-driven literature search surfaced a drug developed for a different cancer as a plausible repurposing candidate for an HR-deficient indication, a lead a human can then validate.
Run this analysis on your question
Hydra plans, executes, and validates, so you reach a defensible answer in hours, not weeks.