<p>It is utterly arbitrary and depends on your resources.</p>
<p>We can all agree your cutoff would be a lot lower during the ordering step than at followup <em>in silico</em> analyses, but even then if you were ordering the top <em>N</em> at \$100-250 each, should you spend \$1,000 or \$10,000?</p>
<p>In the <em>in silico</em> step you have real time and CPU-time to factor in making this call even blurrier.</p>
<p>But there are some things to put those scores into context:</p>
<ul>
<li><a href="https://www.schrodinger.com/kb/1015" rel="nofollow noreferrer">The units are kcal/mol</a>, i.e. these are predicted ∆∆G of binding. <a href="https://michelanglo.sgc.ox.ac.uk/docs/venus_energetics" rel="nofollow noreferrer">Here is an explanation of what does a kcal/mol mean for docking</a> — 1 kcal/mol is a hydrogen bond or the average collision energy of water at 37°C.</li>
<li>If you have a crystal structure with a known ligand, re-docking and cross-docking that would give you a benchmark of what you should expect ——keeping my mind that natural ligand are horrid and don't obey Lipinski's rule. The natural ligand will wander and to assess how good the docking went RMSD is used, but for this I mean looking at its GlideScore as if it were any other compound.</li>
<li>Rescore with a different tool —Gold etc.</li>
</ul>
<h2>Ligand efficiency</h2>
<p>An important thing to remember is that ∆∆G is size dependent —a huge peptidomimetic will score more than a smaller molecule that hits the hotspots perfectly. Therefore, ligand efficiency (LE), ∆∆G over heavy atoms (<a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0330-2" rel="nofollow noreferrer">reviewed in detail here</a>) is a good metric to prevent huge molecules dominating spuriously. If your library has a variety of different sizes, it is best to sort by LE than ∆∆G alone. In fact, if you get a few small hits you can merge/link them or expand on them, whereas if you get nothing because you gambled on larger hits you have to start from square one. I would argue that this is true for repurposing screens and the low success of repurposing VLS despite the enthusiasm is due to the neglect of this.</p>

CC BY-SA 4.0
2020-11-24T08:34:53.737Z
Great answer. Just to add context to the kcal/mol point; these will only be close to accurate if the parameters and assumptions perfectly mimic the chemistry, and even then the biochemistry can complicate things further as the linked article points out.
James
CC BY-SA 4.0
2020-11-24T09:56:26.343Z
@James Your link did not stick. But thanks —you reminded me that I did not mention ligand efficiency!!
Matteo Ferla
CC BY-SA 4.0
2020-11-24T19:11:35.783Z
Sorry Matteo, I was meaning to refer to the link in your question explaining kcal/mol in docking.
James
CC BY-SA 4.0
2020-11-23T16:53:05.380Z
2020-11-24T09:52:48.580Z
2020-11-24T09:52:48.580Z
Matteo Ferla
<p>I made a virtual screening of a large database (~5M) and want to filter ligand structures for further more accurate screening (from Schrodinger HTVS to SP resolution). My next step is to define a cutoff docking score, but it seems arbitrary to choose one. Are there any established methods for filtering the ligands out? <a href="https://i.sstatic.net/SredH.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/SredH.png" alt="distribution of Schrodinger gscore values for 4 different conformations of a protein structure" /></a></p>

<protein-structure><docking>
How to choose docking score cutoff?
# How to choose docking score cutoff?

(Asked by: username_0 on 2020-11-23)

I made a virtual screening of a large database (~5M) and want to filter ligand structures for further more accurate screening (from Schrodinger HTVS to SP resolution). My next step is to define a cutoff docking score, but it seems arbitrary to choose one. Are there any established methods for filtering the ligands out? [![distribution of Schrodinger gscore values for 4 different conformations of a protein structure](https://i.sstatic.net/SredH.png)](https://i.sstatic.net/SredH.png)

**Tags:** protein-structure, docking

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## 1 answers

### Answer by username_1 on 2020-11-23. Score: 3 | Accepted answer

It is utterly arbitrary and depends on your resources.

We can all agree your cutoff would be a lot lower during the ordering step than at followup *in silico* analyses, but even then if you were ordering the top *N* at \$100-250 each, should you spend \$1,000 or \$10,000?

In the *in silico* step you have real time and CPU-time to factor in making this call even blurrier.

But there are some things to put those scores into context:

* [The units are kcal/mol](https://www.schrodinger.com/kb/1015), i.e. these are predicted ∆∆G of binding. [Here is an explanation of what does a kcal/mol mean for docking](https://michelanglo.sgc.ox.ac.uk/docs/venus_energetics) — 1 kcal/mol is a hydrogen bond or the average collision energy of water at 37°C.
* If you have a crystal structure with a known ligand, re-docking and cross-docking that would give you a benchmark of what you should expect ——keeping my mind that natural ligand are horrid and don't obey Lipinski's rule. The natural ligand will wander and to assess how good the docking went RMSD is used, but for this I mean looking at its GlideScore as if it were any other compound.
* Rescore with a different tool —Gold etc.

## Ligand efficiency

An important thing to remember is that ∆∆G is size dependent —a huge peptidomimetic will score more than a smaller molecule that hits the hotspots perfectly. Therefore, ligand efficiency (LE), ∆∆G over heavy atoms ([reviewed in detail here](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0330-2)) is a good metric to prevent huge molecules dominating spuriously. If your library has a variety of different sizes, it is best to sort by LE than ∆∆G alone. In fact, if you get a few small hits you can merge/link them or expand on them, whereas if you get nothing because you gambled on larger hits you have to start from square one. I would argue that this is true for repurposing screens and the low success of repurposing VLS despite the enthusiasm is due to the neglect of this.

> **Comments:**
> **username_2**: Great answer. Just to add context to the kcal/mol point; these will only be close to accurate if the parameters and assumptions perfectly mimic the chemistry, and even then the biochemistry can complicate things further as the linked article points out.  
> **username_1**: @James Your link did not stick. But thanks —you reminded me that I did not mention ligand efficiency!!  
> **username_2**: Sorry Matteo, I was meaning to refer to the link in your question explaining kcal/mol in docking.  

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