[Project]: Train REINVENT Mol2MolSimilarity model to predict molecules similar in 3d shape about ersilia HOT 2 OPEN

Hello @ankitskvmdam,

After some thought, I suggest the following:

1. Let's use REINVENT in transfer learning mode, as you suggested. As a starting model, we can use the Mol2MolSimilarity.
2. Let's use 3D shape search via CHEESE.

The process will be the following:

1. A molecule A is passed as input.
2. We do a 3D Shape Similarity search via the CHEESE API. We can use the Enamine REAL database as the reference library. From that search, we get the top N compounds (L).
3. Of these 100 compounds, we use 80% as a training set for the transfer learning, 10% as a validation set, and 10% as a held out test set.
4. We train (fine-tune) REINVENT with transfer learning using the training set, and controling its performance with the validation set.
5. With the test set, we make sure that, indeed, the test compounds are similar in 3D shape to molecule A. This 3D shape comparison can be done with VSFlow, if that is easier.

I hope this makes sense?

Then, I have more ideas to complicate things further (for example, to search against other databases such as ZINC in CHEESE, to do multiple similarity searches, to penalize molecules that are similar in 2D (favouring scaffold hopping), etc.). But let's go step by step.

Please let me know if something is not clear, @ankitskvmdam !

from ersilia.

@miquelduranfrigola It makes sense. I will proceed with this.

from ersilia.