New talktorial: Molecular Transformer

[PaulaKramer]

I added the code for a basic molecular transformer. It's not a notebook yet, but this could be a starting point for a future talktorial on transformers.

Details

• Talktorial ID: 039
• Title: Molecular Transformer
• Original authors: Paula Kramer
• Reviewer(s): XXX
• Date of review: DD-MM-YYYY

Content

• One line summary: Introduction to transformers for molecular property prediction
• Potential labels or categories (e.g. machine learning, small molecules, online APIs): XXX
• Time it took to execute (approx.):
• [ ] I have used the talktorial template and followed the content and formatting suggestions there
• [ ] 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.
  • package1: Already in TeachOpenCADD
  • package2 (conda-forge): I use it for XXX
  • package3 (pip only): I use it for XXX
• [ ] 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:
  • Resource 1 (link to resource): Access via XXX
  • Resource 2 (link to resource): Access via XXX

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:
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