[DL Edition] T035: SMILES based property prediction

[azmtag]

Talktorial review

Details

• Talktorial ID: T035
• Title: [DL Edition] T035: SMILES based property prediction
• Original authors: Azat Tagirdzhanov
• Reviewer(s): XXX
• Date of review: DD-MM-YYYY

Content

• One line summary: SMILES based property prediction using recurrent neural networks
• Potential labels or categories (e.g. machine learning, small molecules, online APIs): XXX
• Time it took to execute (approx.):
• [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.
  • numpy, pandas, matplotlib: Already in TeachOpenCADD
  • pytorch 1.12.1 (conda-forge): I use it for implementing neural networks
• [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:
  • QM9 dataset: Added to the repository (12M)

Content style

• [x] Talktorial includes cross-references to other talktorials if applicable
• [ ] The table of contents reflects the talktorial story-line; order of #, ##, ### headers is correct
• [x] 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.
• [x] All figures have a description
• [x] Markdown cell content is still in-line with code cell output (whenever results are discussed)
• [x] I have checked that cell outputs are not incredibly long (this applies also to DataFrames)
• [x] Formatting looks correctly on the Sphinx render (bold, italics, figure placing)

Code style

• [x] 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
• [x] There are no unpythonic idioms like for i in range(len(list)) (see slides)
• [x] 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.*)
• [x] 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.

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[Old-Shatterhand]

@azmtag Did you implement Paula's feedback? I could review it then.