[DL Edition] T038: Protein Ligand Interaction Prediction

[Old-Shatterhand]

Description

Proof of concept for GNN-based protein ligand interaction prediction in talktorial T038

Todos

• [x] Find an appropriate dataset
• [x] Fix bug in code to be able to train on batches larger than one sample

Questions

None

Status

Initial draft for further discussion

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[Old-Shatterhand]

Pull request update

After some minor updates on the nodebook, the following work is left.

TODOS:

• [x] Find an appropriate dataset for training
• [x] Discussion: Put plots and explanations of training based on selected dataset
• [x] Questions: Come up with questions about the content

[Old-Shatterhand]

Talktorial review

Pullrequest of talktorial about GNN-based protein-ligand interaction prediction.

Details

• Talktorial ID: T038
• Title: Protein-Ligand Interaction Prediction
• Original authors: Roman Joeres
• Reviewer(s): Andrea Volkamer, tbd
• Date of review: DD-MM-YYYY (tbd)

Content

• One line summary: Introduction of GNN-based protein-ligand interaction prediction
• Potential labels or categories (e.g. machine learning, small molecules, online APIs): Kinases, Machine Learning, Graph Neural Networks
• Time it took to execute (approx.): 1 hour
• [x] I have used the talktorial template and followed the content and formatting suggestions there
• [x] Packages must be open-sourced and should be installable from conda-forge. If you are adding new packages to the TeachOpenCADD environment, please check if already installed packages can perform the same functionality and if not leave a sentence explaining why the new addition is needed. If the new package is not on conda-forge, please list them and their intended usage here.
  • biotite, pypdb, chembl-webresource-client, rdkit: Already in TeachOpenCADD
  • torch 1.10.1, torch-geometric 2.2.0, torch-cluster 1.6.0, torch-scatter 2.0.9, torch-sparse 0.6.13, torch-spline-conv 1.2.1, cpuonly 2.0: From "dubious" sources (either own conda-channel (pyorch) or installed as pip wheel. All of them only for cpu
• [x] Data must be publicly available, preferably accessible via a webserver or downloadable via a URL. Please list the data resources that you use and how to access them:
  • KiBA dataset: Access via website

Content style

• [ ] Talktorial includes cross-references to other talktorials if applicable
• [ ] The table of contents reflects the talktorial story-line; order of #, ##, ### headers is correct
• [ ] URLs are linked with meaningful words, instead of pasting the URL directly or linking words like here.
• [ ] I have spell-checked the notebook
• [ ] Images have enough resolution to be rendered with quality, without being too heavy.
• [ ] All figures have a description
• [ ] Markdown cell content is still in-line with code cell output (whenever results are discussed)
• [ ] I have checked that cell outputs are not incredibly long (this applies also to DataFrames)
• [ ] Formatting looks correctly on the Sphinx render (bold, italics, figure placing)

Code style

• [ ] Variable and function names follow snake case rules (e.g. a_variable_name vs aVariableName)
• [ ] Spacing follows PEP8 (run Black on the code cells if needed)
• [ ] Code line are under 99 characters each (run black-nb -l 99)
• [ ] Comments are useful and well placed
• [ ] There are no unpythonic idioms like for i in range(len(list)) (see slides)
• [ ] All 3rd party dependencies are listed at the top of the notebook
• [ ] I have marked all code cell with output referenced in markdown cells with the label # NBVAL_CHECK_OUTPUT
• [ ] I have identified potential candidates for a code refactor / useful functions
• [ ] All import ... lines are at the top (practice part) cell, ordered by standard library / 3rd party packages / our own (teachopencadd.*)
• [ ] I have used absolute paths instead of relative paths

  HERE = Path(_dh[-1])
  DATA = HERE / "data"

Website

We present our talktorials on our TeachOpenCADD website (https://projects.volkamerlab.org/teachopencadd/), so we have to check as well if the Jupyter notebook renders nicely there.

• [ ] If this PR adds a new talktorial, please follow these steps:
  • [ ] Add your talktorial to the complete list of talktorials here (at the end).
  • [ ] Add your talktorial to one or multiple of the collections here. Or propose a new collection section in your PR.
  • [ ] Add your talktorial's nblink file by running python generate_nblinks.py from within the directory teachopencadd/docs/talktorials.
  • [ ] Please complile the website following the instructions here.
• [ ] Check the rendering of the talktorial of this PR.
• [ ] Is your talktorial listed in the talktorial list?
• [ ] Is your talktorial listed in the talktorial collections?
  • [ ] Add a picture for your talktorial in the collection view by following these instructions.

[gerritgr]

• I will (and elsewhere)-> We will? (I think in other notebooks, it is third person, have not checked though).
• "field of protein ligand interaction prediction" -> "protein-ligand"
• Maybe explain the terms protein and ligand very shortly in the intro.
• Titles are lowercase (Sentence Case) in the other notebooks.
• "..., I'll link to this otherwise, I'll explain new things below." -> "... , I will link to this. Otherwise, I will explain new things below."
• "one wants to" -> wants
• There are some other typos, but Grammarly or so can catch these I guess.
• simple Feed-forward Neural Network (FNN): do you mean MLP? GNNs are also technically feed-forward networks.
• State in the beginning of the workflow that this is a binary classification task.
• " from the PDB entry with ID 4O75." link to a talkturial explaining PDB earlier.
• explain C_alpha
• In the "Technical background", say that we use the same GNN architecture for both ligands and proteins.
• I think the BCE explanation should be clarified. What are the negative and positive samples here (binding and non-binding?)?
• suppress DtypeWarning and add a comment explaining what kiba_preprocessing is doing
• "Storing and representing data in PLI-prediction is a bit different from other neural networks. ": You mean the input data (i.e., the graphs), right? Maybe clarify this. Also, even though it is obvious, PLI was not introduced as an abbreviation.
• Maybe merge together with the "Data Points" subsection.
• I have not checked the code yet.

[Old-Shatterhand]

I implemented Gerrits comments and uploaded a new notebook.