SpotRM+ – potential reactive metabolite formations – batch analysis in Knime

Modelling and prediction of toxicity of drug compounds has been, is, and will be be a continuous area of interest. I won’t go into the detailed literature of this, here, I want to focus on SpotRM+’s contribution to that field:
This methodology focuses on reactive metabolite formation and avoidance as a means to reduce structure based toxicity issues. In addition, it is a computationally cheap method since it is solely based on SMARTS, carefully hand-curated ones at that. In addition to identifying certain structural features, SpotRM+ delivers one to three page monographs on the marketed (or withdrawn) reference compounds including mechanistic summaries. So it is more about learning than pure black box filtration.

SpotRM+ requires Bioclipse, a platform which has chemical data-mining in its focus. There is a disadvantage to this package – you can only run and analyze one compound at a time, batch mode isn’t possible.
According to the company Awametox AB, the batch mode analysis is a feature requested by a number of customers, e.g. for design/synthesis prioritization. And yes, it is possible – IF you use script based or workflow based tools with one of the simpler ones being Knime. For this, you require access to the SpotRM+ database itself and the standard chemistry mining nodes in Knime.
[note that SpotRM+ is a commercial package, though there is a free demo available; both are based on Bioclipse. For the mining suggested here you need the database itself which can be purchased separately]

One of the drawbacks of the database and the SpotRM+ system with regards to batch analysis is that it isn’t really designed for batch analysis. The readout usually consists of a traffic light colouring system of reference compounds and links to their analysis monographs. Thus, for batch mode to work, you need to ask what you desire of it -e.g.

  • Is a single “red” or “green” reference hit sufficient?
  • Do you want to summarize all the reference hits?
  • Combine with other data for further calculations?

In principle, anything goes, that’s the beauty of the flexibility of a package such as Knime. But, would that be sufficient for you to make a decision? I can imagine that a batch based “high quality” decision should be possible, if you combine the output with, e.g., a model based on measured ADMET data (and/or reactive metabolite data).
Independent of the latter, a basic workflow could look simply like this:

You can find more info and access to mentioned programs here:
SpotRM+: (bioclipse included; recently updated to V1.2!)
Bioclipse: (mainly for info, not required to download separately)

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