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Description
First of all, I would like to thank you for your work with MolPal, I'm sure this will be very useful to the drug discovery community in the future.
I am trying to use your software to predict docking scores that have been generated by my own consensus docking pipeline .
I am struggling to understand (despite your very clear documentation) how I would go about this. If I understand correctly I would be using the lookup function of Molpal. However, when I look at your examples on GitHub (for example in /examples/objective/EnamineHTS_lookup.ini which points to /data/EnamineHTS_scores.csv.gz) it seems the software is looking at the entire libraries docking scores. Obviously, I am understanding something wrong here as the point is to have to dock only a small part of the library.
These are the two ways I understand this, feel free to let me know if I'm completely off with either of these...
- Assuming I dock 1% of the library to start with, I would then essentially train the model on that 1%, do one iteration of Molpal, then dock the compounds predicted to have good docking scores. Then repeat this process until I reach a total of 6 iterations as described in the paper?
- Alternatively, assuming I dock 1% of the library to start with, would I then run Molpal directly using this 1% as a lookup and not have to dock the suggested compounds (except if I wanted to check the performance of the prediction?). In this case, I don't fully grasp why there would be an option to 'retrain from scratch'.
I apologize if I am missing something obvious.
Thank you for your time and assistance.