Set up an automated ML task to predict drug -> drug target edges

Knowledge-Graph-Hub / kg-idg

A Knowledge Graph to Illuminate the Druggable Genome

https://knowledge-graph-hub.github.io/kg-idg/

BSD 3-Clause "New" or "Revised" License

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Set up an automated ML task to predict drug -> drug target edges #43

Open justaddcoffee opened 2 years ago

justaddcoffee commented 2 years ago

Describe the desired behavior

It would be useful for Drug Central to have inferred drug -> drug target edges, which we could formulate as a graph ML link prediction task.

We maybe could/should do this in an automated way, on each build of KG-IDG.

A possible roadmap:

[ ] Write Jupyter notebook for link prediction task
[ ] Write some tooling to run this code from Jenkins
[ ] Write some code to push the results to KGHub

Additional context

Per convo with Tudor et al on IDG just now

justaddcoffee commented 2 years ago

Discussing this more with Harry - some thoughts.

We can use NEAT for this (i.e. spin up a Google Cloud instance from Jenkins, pass off a YAML file to the instance describing the graph ML task, wait for it to complete and upload artifacts to S3).

This strategy has the advantage of:

helping us get NEAT updated
helping us flesh out the general use case of getting automatic embedding and graph ML working with KGHub and other projects (Monarch, eco-kg, others)

caufieldjh commented 2 years ago

As per IDG meeting on Jan 20, following drug categories would be good for evaluation:

Those targeting GPCRs
antihistaminics (H1 blockers)
antipsychotics (multi-target)
antivirals (no human targets)
antiestrogens (mostly ERalpha)

caufieldjh commented 2 years ago

I don't think we include any viral proteins right now, so we'd expect the antivirals to have few/no human targets, especially because we aren't doing any inference based off sequence or structure.