Body: <p>Could I ask, is anyone familiar with the Drugebility score provided by Chembl?</p>
  <p>I wanted to add a druggability score to a set of human targets, to understand the likelihood that they are druggable.</p>
  <p>I found Chembl's drugebility score <a href="https://chembl.gitbook.io/chembl-interface-documentation/downloads" rel="nofollow noreferrer">here</a>, but I don't fully understand which file I'm meant to use that adds a drugebility score to each human protein? In particular, should I be looking at the drugebility.dmp.gz file (that was created in 2019) or the site_drugebility.txt file that was created in 2013 (seems very long ago)?</p>
  <p>I'm unclear in drugebility.dmp how do understand what ensembl/NCBI/uniprot ID each score is assigned to?</p>
  <p>Or does someone have a better suggestion for how to link ideally Ensembl/UniProt IDs to a score indicating the likelihood that the protein is druggable?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-06T08:46:09.720Z
Comments.Text: The DrugEBIlity score is multiple scores (`tractable`, `druggable` & `confidence` etc.) and actually uses an even older pocket finding algorithm IIRC (probably written in [here](https://www.ebi.ac.uk/sites/ebi.ac.uk/files/content.ebi.ac.uk/materials/2013/131209DrugDiscovery/3_-_anna_galton_-_druggable_genome.pdf)), but the major issue that it is solved structures only —not even close threaded models. Now with AlphaFold2 there's a whole new dataset to play with. I would bet they are going to or are currently rescoring... May be worth checking with them by email/Twitter/etc.
Comments.UserDisplayName: Matteo Ferla
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-06T13:54:44.987Z
Comments.Text: @MatteoFerla while AlphaFold2 has generated a lot of media attention, as far as I can tell
   it has actually generated very little new data that wasn't already available (and likely more reliable) in [SwissModel](https://swissmodel.expasy.org/).
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-06T14:10:28.483Z
Comments.Text: @terdon. The oligomerisation in SwissModel is a real winner and being threaded the other chains and ligands from the template can be stolen. But bar for oligomers and the spaghetti loops of AF2, AF2 is more accurate (for starters it does not blow-up in minimisation) and there are a lot of horrors in the Felsenstein zone of SwissModel (<30% id). Also, there are definitely plenty of protein which we have no idea what they do that have enzyme-like cavities solved in AF2, but not SM, e.g. Q9NX78, simply because it's nobody's favourite protein.
Comments.UserDisplayName: Matteo Ferla
Tags: <ensembl><uniprot><drugs>
Title: Best resource to link a gene/protein to a druggability score?
ThreadText: # Best resource to link a gene/protein to a druggability score?
  
  (Asked by: username_0 on 2021-10-05)
  
  Could I ask, is anyone familiar with the Drugebility score provided by Chembl?
  
  I wanted to add a druggability score to a set of human targets, to understand the likelihood that they are druggable.
  
  I found Chembl's drugebility score [here](https://chembl.gitbook.io/chembl-interface-documentation/downloads), but I don't fully understand which file I'm meant to use that adds a drugebility score to each human protein? In particular, should I be looking at the drugebility.dmp.gz file (that was created in 2019) or the site\_drugebility.txt file that was created in 2013 (seems very long ago)?
  
  I'm unclear in drugebility.dmp how do understand what ensembl/NCBI/uniprot ID each score is assigned to?
  
  Or does someone have a better suggestion for how to link ideally Ensembl/UniProt IDs to a score indicating the likelihood that the protein is druggable?
  
  **Tags:** ensembl, uniprot, drugs
  
  > **Comments:**
  > **username_1**: The DrugEBIlity score is multiple scores (`tractable`, `druggable` & `confidence` etc.) and actually uses an even older pocket finding algorithm IIRC (probably written in [here](https://www.ebi.ac.uk/sites/ebi.ac.uk/files/content.ebi.ac.uk/materials/2013/131209DrugDiscovery/3_-_anna_galton_-_druggable_genome.pdf)), but the major issue that it is solved structures only —not even close threaded models. Now with AlphaFold2 there's a whole new dataset to play with. I would bet they are going to or are currently rescoring... May be worth checking with them by email/Twitter/etc.  
  > **username_2**: @username_1 while AlphaFold2 has generated a lot of media attention, as far as I can tell  it has actually generated very little new data that wasn't already available (and likely more reliable) in [SwissModel](https://swissmodel.expasy.org/).  
  > **username_1**: @username_2. The oligomerisation in SwissModel is a real winner and being threaded the other chains and ligands from the template can be stolen. But bar for oligomers and the spaghetti loops of AF2, AF2 is more accurate (for starters it does not blow-up in minimisation) and there are a lot of horrors in the Felsenstein zone of SwissModel (<30% id). Also, there are definitely plenty of protein which we have no idea what they do that have enzyme-like cavities solved in AF2, but not SM, e.g. Q9NX78, simply because it's nobody's favourite protein.  
  
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  ## 0 answers
  
  