For reproducing the reported results

[baoy-nlp]

Hi, Yinan

Thanks for the great work and for providing your code and checkpoint for the followers to reproduce 3DLinker.

First, I follow your instruction in the README to reproduce the experimental results in the paper. Specifically, I use the "pretrained_model.pickle" in the [check_points] for run the command python3 main.py --dataset zinc --config-file test_config.json --generation True --load_cpt ./check_points/pretrained_model.pickleto generate the candidates, and python3 evaluate_generated_mols.py ZINC ../generated_samples/generated_smiles_zinc.smi ../zinc/smi_train.txt 1 True None ./wehi_pains.csv to evaluate the generated candidates. But I get the results as follows:

Pass all 2D filters:            0.00%
Valid and pass all 2D filters:  0.00%
Pass synthetic accessibility (SA) filter:       0.00%
Pass ring aromaticity filter:                   0.00%
Pass SA and ring filters:                       0.00%
Pass PAINS filters:                             0.00%
Aveage RMSD is 0.068152

Is there anything wrong with my practice?

Second, I notice there is a step for processing raw graphs of train data while initializing the "Linker(args)." How much time does it need to process ZINC data? In my env, around 4 hours. Is it normal? Can we do preprocess just once and save it before initializing the Linker?

Third, I find it may take a very long time to re-train the 3DLinker. Are there more statistics about re-implementing the 3DLinker, such as training environment, batch_size, epochs, and training time?

I really appreciate any help you can provide. Thank you in advance!

Best wishes Yu

[FeilongWuHaa]

due to miss "fpscores.pkl.gz" in ./analysis filefold, you can download from Delinker (https://github.com/oxpig/DeLinker)

[YinanHuang]

It is due to the missing file as mentioned by FeilongWuHaa. Now it is added and the filtering should work. Thanks!