Answers.Body: <blockquote>
  <p>This is a comment to redirect to other answers, but go too long.</p>
  </blockquote>
  <p>Docking is the <em>in silico</em> prediction of where a ligand binds in a protein.</p>
  <p>There are several Q&amp;As about docking here that are relevant, so please check out:</p>
  <ul>
  <li>machine learning, docking and the factors that affect it: <a href="https://bioinformatics.stackexchange.com/questions/12971/generate-ligands-candidates-based-on-protein-shape/12975#12975">Generate ligands candidates based on protein shape</a> —also worth a read the <a href="https://academic.oup.com/bioinformatics/article-abstract/36/3/758/5554651" rel="nofollow noreferrer">&quot;learning from the ligand&quot; paper</a> paper, which discusses why some ligand only parameters tell a lot about binding.</li>
  <li>correlation between docking scores and enzyme kinetics: <a href="https://bioinformatics.stackexchange.com/questions/7504/purpose-of-correlating-docking-score-with-catalytic-efficiencykcat-km/10593#10593">Purpose of correlating docking score with catalytic efficiency(Kcat/km)</a> —discusses a paper <a href="https://www.nature.com/articles/ncomms12965" rel="nofollow noreferrer">https://www.nature.com/articles/ncomms12965</a> wherein a linear regression is done between docking scores and kcat/KM</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-29T08:10:29.220Z
Answers.LastActivityDate: 2020-06-29T08:10:29.220Z
Answers.UserDisplayName: Matteo Ferla
Body: <p>I would like to work on a project that involves the prediction of protein-ligand binding kinetics. What might be the feature that is relevant for the prediction?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-28T17:11:14.670Z
Comments.Text: Does this answer your question? [What is the current state-of-the-art in protein-ligand binding affinity prediction using machine learning?](https://bioinformatics.stackexchange.com/questions/13636/what-is-the-current-state-of-the-art-in-protein-ligand-binding-affinity-predicti)
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-28T18:06:30.460Z
Comments.Text: Anyway, yes but you need access to a large database of experimental daa for protein-ligand affinity binding for training and validation. In addition, you'll need to vectorise the ligands ... which may not be at all easy. Personally, I would update your original question.
Comments.UserDisplayName: M__
Tags: <proteins><machine-learning><interactions>
Title: Is it possible to predict protein-ligand binding kinetics using machine learning?
ThreadText: # Is it possible to predict protein-ligand binding kinetics using machine learning?
  
  (Asked by: username_0 on 2020-06-28)
  
  I would like to work on a project that involves the prediction of protein-ligand binding kinetics. What might be the feature that is relevant for the prediction?
  
  **Tags:** proteins, machine-learning, interactions
  
  > **Comments:**
  > **username_1**: Does this answer your question? [What is the current state-of-the-art in protein-ligand binding affinity prediction using machine learning?](https://bioinformatics.stackexchange.com/questions/13636/what-is-the-current-state-of-the-art-in-protein-ligand-binding-affinity-predicti)  
  > **username_1**: Anyway, yes but you need access to a large database of experimental daa for protein-ligand affinity binding for training and validation. In addition, you'll need to vectorise the ligands ... which may not be at all easy. Personally, I would update your original question.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-06-29. Score: 1 | Accepted answer
  
  > This is a comment to redirect to other answers, but go too long.
  
  Docking is the *in silico* prediction of where a ligand binds in a protein.
  
  There are several Q&As about docking here that are relevant, so please check out:
  
  * machine learning, docking and the factors that affect it: [Generate ligands candidates based on protein shape](https://bioinformatics.stackexchange.com/questions/12971/generate-ligands-candidates-based-on-protein-shape/12975#12975) —also worth a read the ["learning from the ligand" paper](https://academic.oup.com/bioinformatics/article-abstract/36/3/758/5554651) paper, which discusses why some ligand only parameters tell a lot about binding.
  * correlation between docking scores and enzyme kinetics: [Purpose of correlating docking score with catalytic efficiency(Kcat/km)](https://bioinformatics.stackexchange.com/questions/7504/purpose-of-correlating-docking-score-with-catalytic-efficiencykcat-km/10593#10593) —discusses a paper <https://www.nature.com/articles/ncomms12965> wherein a linear regression is done between docking scores and kcat/KM
  
  
  ---
  
  