Merge pull request #341 from volkamerlab/fix-dead-links

Fix dead links

.github/workflows/docs.yml

@@ -72,6 +72,13 @@ jobs:
             -s "http[s]://doi.org/*" \
             -s "http[s]://pubs.acs.org/*" \
             -s "ftp://*" \
+            -s "https://machinelearningmastery.com/*" \
+            -s "https://www.schrodinger.com/training/videos/*" \
+            -s "/*.mp4" \
+            -s "/*.pdf" \
+            -s "/*.png" \
+            -s "https://www.icoa.fr/pkidb/index.html" \
+            
 
       - name: Check 404s (README)
         uses: gaurav-nelson/github-action-markdown-link-check@v1

docs/contact.rst

@@ -5,5 +5,6 @@ Please contact us if you have questions or suggestions!
 
 - If you have questions regarding our Jupyter Notebooks, please `open an issue <https://github.com/volkamerlab/teachopencadd/issues>`_ on our GitHub repository.
 - If you have ideas for new topics, please fill out our `questionnaire <http://contribute.volkamerlab.org>`_.
+- If you have any further inquiries, please send us an `email <teachopencadd@lists.cs.uni-saarland.de>`_.
 
-We are looking forward to hearing from you!
+We are looking forward to hearing from you!

docs/external_dependencies.rst

@@ -13,7 +13,7 @@ Python packages
   - ``openbabel``: https://openbabel.org/
   - ``mdanalysis``: https://www.mdanalysis.org/
   - ``biopython``: https://biopython.org/
-  - ``biopandas``: http://rasbt.github.io/biopandas/
+  - ``biopandas``: https://biopandas.github.io/biopandas/
   - ``opencadd``: https://opencadd.readthedocs.io/en/latest/
   - ``plip``: https://github.com/pharmai/plip
   - ``openff``: https://github.com/openforcefield/openff-toolkit

docs/external_tutorials_collections.rst

@@ -7,8 +7,8 @@ Python programming
 The TeachOpenCADD platform is not a Python programming course from scratch but teaches how to solve tasks in cheminformatics and structural bioinformatics programmatically.
 If you wish to get started first with a Python programming introduction before diving into the TeachOpenCADD material, here are a few great resources to do so:
 
-- `AI in Medicine course <https://github.com/volkamerlab/ai_in_medicine>`_ by the `Volkamer Lab <https://volkamerlab.org/>`_ and `Ritter Lab <https://psychiatrie-psychotherapie.charite.de/metas/person/person/address_detail/ritter-7/>`_ at the Charité: Introduction to Python basics, Jupyter Notebook, and important data science packages such as ``pandas``, ``matplotlib``, and ``scikit-learn``
-- `Python for Chemists course <https://github.com/GDChCICTeam/python-for-chemists>`_ by the `GDCh/CIC <https://en.gdch.de/network-structures/divisions/computers-in-chemistry-cic.html>`_ team: Crash-course introduction to Python for natural scientists
+- `AI in Medicine course <https://github.com/volkamerlab/ai_in_medicine>`_ by the `Volkamer Lab <https://volkamerlab.org/>`_ and `Ritter Lab <https://psychiatrie-psychotherapie.charite.de/metas/person/person/address_detail/prof_dr_rer_nat_kerstin_ritter/>`_ at the Charité: Introduction to Python basics, Jupyter Notebook, and important data science packages such as ``pandas``, ``matplotlib``, and ``scikit-learn``
+- `Python for Chemists course <https://github.com/GDChCICTeam/python-for-chemists>`_ by the `GDCh/CIC <https://en.gdch.de/network-structures/gdch-structures/computers-in-chemistry-cic.html>`_ team: Crash-course introduction to Python for natural scientists
 - `MolSSI Education Resources <http://education.molssi.org/resources.html>`_ by `The Molecular Sciences Software Institute <https://molssi.org/>`_: Collection of tutorials on Python programming basics and data analysis but also more advanced material on software development and computational molecular science
 - `Core lessons <https://software-carpentry.org/lessons/>`_ by the `Software Carpentry <https://software-carpentry.org/>`_: Introduction to Python, Git, command line interfaces and more
 - `Best practices for Python, Git and Jupyter <https://zenodo.org/record/4630714>`_, a slide deck used in the introduction of our CADD Block course 2021, part of the Master's degree in Bioinformatics curriculum at the Freie Universität (FU) Berlin.

teachopencadd/talktorials/T004_compound_similarity/talktorial.ipynb

@@ -75,12 +75,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### References\n",
     "\n",
-    "* Review on \"Molecular similarity in medicinal chemistry\" ([<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204](http://pubs.acs.org/doi/abs/10.1021/jm401411z))\n",
+    "* Review on \"Molecular similarity in medicinal chemistry\" ([<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204](https://pubmed.ncbi.nlm.nih.gov/24151987/))\n",
     "* [Morgan fingerprints](http://www.rdkit.org/docs/GettingStartedInPython.html#morgan-fingerprints-circular-fingerprints) with `rdkit`\n",
     "* Description of the extended-connectivity fingerprint ECFP ([<i>J. Chem. Inf. Model.</i> (2010), <b>50</b>,742-754](https://pubs.acs.org/doi/abs/10.1021/ci100050t))\n",
     "* What is the chemical space?\n",
@@ -109,12 +110,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Molecular descriptors \n",
     "\n",
-    "Similarity can be assessed in many different ways depending on the application (see [<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204</a>](http://pubs.acs.org/doi/abs/10.1021/jm401411z)): \n",
+    "Similarity can be assessed in many different ways depending on the application (see [<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204</a>](https://pubmed.ncbi.nlm.nih.gov/24151987/)): \n",
     "\n",
     "- 1D molecular descriptor: Solubility, logP, molecular weight, melting point.\n",
     "  - Global descriptor: only one value represents the whole molecule\n",
@@ -193,12 +195,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Molecular similarity measures \n",
     "\n",
-    "Once the descriptors/fingerprints are calculated, they can be compared to assess the similarity between two molecules. Molecular similarity can be quantified with a number of different similarity coefficients, two common similarity measures are the Tanimoto and Dice index ([<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204](http://pubs.acs.org/doi/abs/10.1021/jm401411z))."
+    "Once the descriptors/fingerprints are calculated, they can be compared to assess the similarity between two molecules. Molecular similarity can be quantified with a number of different similarity coefficients, two common similarity measures are the Tanimoto and Dice index ([<i>J. Med. Chem.</i> (2014), <b>57</b>, 3186-3204](https://pubmed.ncbi.nlm.nih.gov/24151987/))."
    ]
   },
   {