sanity checking on new target about rtmscore HOT 40 OPEN

I recommend you to assess our method on your own target in advance, e,g., docking power (redocking, cross-docking), screening power (testing whether our approach could distingush active compounds from inactive componds for your own target).

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hi,I would like to ask you a question, Can I use it to screen large molecular databases?
Thanks!

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Of course you can have a try.

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The database given in the literature cannot be downloaded. Is there any way to solve it？
thanks！

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I can access all the links provided in the litertature (http://www.pdbbind.org.cn/; https://
zenodo.org/record/5525936; https://zenodo.org/record/6859325). Perhaps there is something wrong with your network.

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Ok, thank you for your answer.

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You can directly use the code provided as the example for rescoring, without any changes. Just see our method as a generic scoring function like X-Score or AutoDock Vina. However, it should be noticed that our model in this version just serves as a scoring function rather than a docking program, so you need to generate the binding poses first. Additionally, it is better for you to generate multiple poses (e.g., 10) for a certain ligand for our model to select the most reasonable pose for rescoring, which may achieve better performance.

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Yes, you need to generate the binding poses between the each ligand in the database and a protein.
Of course you can, but it is better for you to assess our model on your own target first, since the performance of our method may be highly target-specific.

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As shown in Figure 4 in our paper, the virtual screening performance of our method may vary a lot when different targets are focused on. Hence of you try our method on your own target, I'm unclear how will it actually perform and thus I suggest you to conduct an initial assessment first.

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It is defined as the percentage of true binders observed among all of the true binders for 1% of the top-ranked candidates of
a chemical library.
The bigger means the better.
Not necessarily. It is just a metric for retrospective study, but not necessarily suitable for your own system in real-world applications since both the size of the screening library and the number of compounds you tend to select for purchase/synthesis are unknown. The simplest method to judge the performance of our model for your target may just see whether the model can make the active compounds lie in the front rank.

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This file is downloaded from http://www.pdbbind.org.cn/casf.php.
Actually I cannot understand what you mean to do.

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You can directly use our trained models to predict the binding poses. But first, you should use the external docking program (e.g., AutoDock Vina, Glide, GOLD, etc.) to generate multiple docking poses for your own ligands and the target, and then use our method for rescoring, just as described in our demo files.

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You can generate the pocket using the "extract_pocket" function in RTMScore/feats/extract_pocket_prody.py.

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You should call the "extract_pocket" function in RTMScore/feats/extract_pocket_prody.py and write a script by yourself rather than directly run the script.
You can use some multiprocessing utilities (e.g., multiprocessing module in Python) to handle the molecules simultaneously.

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It is calculated as:

You can refer to the following script:
11.zip

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You can refer to the following script.
generate_pocket.zip

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You can just use the rtmscore.py for prediction. Just do as the demo.

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The score represents the binding strength of a certain ligand conformation to the target, and the higher the better. Multiple docking poses of a certain molecule should be generated first, and then rescored by rtmscore.py. The best-scored pose of a certain molecule can be selected as the final binding pose of that molecule, and this score could represent the binding of the molecule to the target.
If you use our method to screen the database, you can first enrich the database to a certain number of top-ranked molecules (e.g., 500), and then determine your compounds from these molecules just based on your experience.

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" Can't the model directly screen or rank based on the score of the binding pose?" Of course you can. But I think you can never completely rely on the docking scores when conducting a virtual screening campaign.

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1qkt_p.pdb the structure of the protein
1qkt_l.sdf the pose of crystal ligand used to define the pocket
1qkt_decoys.sdf the poses of the molecules that you want to score

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