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<section id="introduction">
<span id="index-0"></span><h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h1>
<section id="about">
<span id="index-1"></span><h2>About<a class="headerlink" href="#about" title="Permalink to this heading"></a></h2>
<p><em>CDPKit</em> (short for <em>Chemical Data Processing Toolkit</em>) is an open-source cheminformatics toolkit implemented in C++.
CDPKit comprises a suite of software tools and a programming library called the <em>Chemical Data Processing Library</em> (CDPL) which
provides a high-quality and well-tested modular implementation of basic functionality typically required by any higher-level
software application in the field of cheminformatics.
In addition to the CDPL C++ API, an equivalent Python-interfacing layer is provided that allows to harness all of CDPL’s
functionality easily from Python code.</p>
<p class="rubric" id="index-2">Key Features</p>
<ul class="simple">
<li><p>Data structures for the representation and processing of molecules, chemical reactions and pharmacophores</p></li>
<li><p>Routines for all typical cheminformatics pre-processing tasks (e.g. ring and aromaticity perception, stereochemistry processing, …)</p></li>
<li><p>Powerful methods for molecule and reaction substructure searching</p></li>
<li><p>Readers/writers for various file formats (MDL Mol, SDF, Rxn, RDF, Mol2, PDB, MMTF, SMILES, SMARTS, etc.) allowing the I/O of
small molecule, macromolecular, reaction and pharmacophore data</p></li>
<li><p>Molecule fragmentation algorithms (RECAP <span id="id1">[<a class="reference internal" href="bibliography.html#id14" title="Xiao Qing Lewell, Duncan B. Judd, Stephen P. Watson, and Michael M. Hann. Recapretrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. Journal of Chemical Information and Computer Sciences, 38(3):511-522, 1998. PMID: 9611787. URL: https://doi.org/10.1021/ci970429i, arXiv:https://doi.org/10.1021/ci970429i, doi:10.1021/ci970429i.">1</a>]</span>, BRICS <span id="id2">[<a class="reference internal" href="bibliography.html#id12" title="Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey. On the art of compiling and using 'drug-like' chemical fragment spaces. ChemMedChem, 3(10):1503-1507, 2008. URL: https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/cmdc.200800178, arXiv:https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cmdc.200800178, doi:https://doi.org/10.1002/cmdc.200800178.">2</a>]</span>)</p></li>
<li><p>Generation of molecule and pharmacophore fingerprints (e.g. ECFP <span id="id3">[<a class="reference internal" href="bibliography.html#id9" title="David Rogers and Mathew Hahn. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 50(5):742-754, 2010. PMID: 20426451. URL: https://doi.org/10.1021/ci100050t, arXiv:https://doi.org/10.1021/ci100050t, doi:10.1021/ci100050t.">3</a>]</span>)</p></li>
<li><p>Large collection of implemented chemical structure descriptors</p></li>
<li><p>2D structure layout and rendering of molecules and reactions</p></li>
<li><p>Gaussian shape-based molecule alignment and descriptor calculation <span id="id4">[<a class="reference internal" href="bibliography.html#id7" title="J. A. GRANT, M. A. GALLARDO, and B. T. PICKUP. A fast method of molecular shape comparison: a simple application of a gaussian description of molecular shape. Journal of Computational Chemistry, 17(14):1653-1666, 1996. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291096-987X%2819961115%2917%3A14%3C1653%3A%3AAID-JCC7%3E3.0.CO%3B2-K, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291096-987X%2819961115%2917%3A14%3C1653%3A%3AAID-JCC7%3E3.0.CO%3B2-K, doi:https://doi.org/10.1002/(SICI)1096-987X(19961115)17:14<1653::AID-JCC7>3.0.CO;2-K.">4</a>]</span></p></li>
<li><p>Pharmacophore generation, alignment and screening</p></li>
<li><p>3D structure and conformer generation <span id="id5">[<a class="reference internal" href="bibliography.html#id10" title="Thomas Seidel, Christian Permann, Oliver Wieder, Stefan M. Kohlbacher, and Thierry Langer. High-quality conformer generation with conforge: algorithm and performance assessment. Journal of Chemical Information and Modeling, 0(0):null, 0. PMID: 37624145. URL: https://doi.org/10.1021/acs.jcim.3c00563, arXiv:https://doi.org/10.1021/acs.jcim.3c00563, doi:10.1021/acs.jcim.3c00563.">5</a>]</span></p></li>
<li><p>Prediction of a wide panel of physicochemical properties</p></li>
<li><p>Full-blown test-suite compliant implementation of the MMFF94 <span id="id6">[<a class="reference internal" href="bibliography.html#id8" title="Thomas A. Halgren. Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94. Journal of Computational Chemistry, 17(5-6):490-519, 1996. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291096-987X%28199604%2917%3A5/6%3C490%3A%3AAID-JCC1%3E3.0.CO%3B2-P, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291096-987X%28199604%2917%3A5/6%3C490%3A%3AAID-JCC1%3E3.0.CO%3B2-P, doi:https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P.">6</a>]</span> force field</p></li>
<li><p>Runs without flaws on Linux, macOS and Windows</p></li>
<li><p>C++ implementation follows best practices for a maximum of robustness and speed</p></li>
<li><p>… and many more …</p></li>
</ul>
<p class="rubric">Machine Learning Integration</p>
<p>CDPKit seamlessly integrates with machine learning libraries like <a class="reference external" href="https://scikit-learn.org">scikit-learn</a>, <a class="reference external" href="https://pytorch.org">PyTorch</a>,
and <a class="reference external" href="https://www.tensorflow.org">TensorFlow</a>. Utilizing CDPKit for tasks like molecular data I/O, feature extraction, descriptor calculations, and so on,
greatly aids scientists that intend to build ML models for the prediction of physicochemical properties, biological activity, site of metabolism ,
toxicity, and other attributes of potential drug candidates. An example of such an integration with ML methods is showcased in the
source code of the software described in <span id="id7">Wieder <em>et al.</em> [<a class="reference internal" href="bibliography.html#id11" title="Oliver Wieder, Mélaine Kuenemann, Marcus Wieder, Thomas Seidel, Christophe Meyer, Sharon D. Bryant, and Thierry Langer. Improved lipophilicity and aqueous solubility prediction with composite graph neural networks. Molecules, 2021. URL: https://www.mdpi.com/1420-3049/26/20/6185, doi:10.3390/molecules26206185.">7</a>]</span>.</p>
</section>
<section id="license">
<span id="index-3"></span><h2>License<a class="headerlink" href="#license" title="Permalink to this heading"></a></h2>
<p>The CDPKit source code is released under the terms of the <a class="reference external" href="https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html">GNU Lesser General Public License (LGPL) V2.1-or-later</a>.
CDPKit documentation is licensed under the terms of the <a class="reference external" href="https://www.gnu.org/licenses/old-licenses/fdl-1.2.en.html">GNU Free Documentation License (GFDL) V1.2-or-later</a>.
Code snippets in tutorials and the source code of CDPL programming examples are distributed under the terms of the <a class="reference external" href="https://opensource.org/license/0bsd">Zero-Clause BSD License (0BSD)</a>.</p>
</section>
<section id="related-software">
<span id="index-4"></span><h2>Related software<a class="headerlink" href="#related-software" title="Permalink to this heading"></a></h2>
<p>Examples of software projects using CDPKit functionality:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/molinfo-vienna/FAME.AL">FAME.AL: Site-of-metabolism prediction with active learning</a> <span id="id8">[<a class="reference internal" href="bibliography.html#id2" title="Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte, Angelica Mazzolari, Alessandro Pedretti, Giulio Vistoli, Thierry Langer, Filip Miljković, and Johannes Kirchmair. Active learning approach for guiding site-of-metabolism measurement and annotation. Journal of Chemical Information and Modeling, 0(0):null, 0. PMID: 38170877. URL: https://doi.org/10.1021/acs.jcim.3c01588, arXiv:https://doi.org/10.1021/acs.jcim.3c01588, doi:10.1021/acs.jcim.3c01588.">8</a>]</span></p></li>
<li><p><a class="reference external" href="https://github.com/molinfo-vienna/GRAIL-Scripts">Python scripts for the generation of GRAIL datasets</a> <span id="id9">[<a class="reference internal" href="bibliography.html#id18" title="Doris A. Schuetz, Thomas Seidel, Arthur Garon, Riccardo Martini, Markus Körbel, Gerhard F. Ecker, and Thierry Langer. Grail: grids of pharmacophore interaction fields. Journal of Chemical Theory and Computation, 14(9):4958-4970, 2018. PMID: 30075621. URL: https://doi.org/10.1021/acs.jctc.8b00495, arXiv:https://doi.org/10.1021/acs.jctc.8b00495, doi:10.1021/acs.jctc.8b00495.">9</a>]</span></p></li>
<li><p><a class="reference external" href="https://github.com/molinfo-vienna/commonHitsApproach">Scripts implementing the Common Hits Approach (CHA)</a> <span id="id10">[<a class="reference internal" href="bibliography.html#id19" title="Marcus Wieder, Arthur Garon, Ugo Perricone, Stefan Boresch, Thomas Seidel, Anna Maria Almerico, and Thierry Langer. Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. Journal of Chemical Information and Modeling, 57(2):365-385, 2017. PMID: 28072524. URL: https://doi.org/10.1021/acs.jcim.6b00674, arXiv:https://doi.org/10.1021/acs.jcim.6b00674, doi:10.1021/acs.jcim.6b00674.">10</a>]</span></p></li>
<li><p><a class="reference external" href="https://github.com/molinfo-vienna/apo2ph4">Workflow scripts for the generation of receptor-based pharmacophore models (apo2ph4)</a> <span id="id11">[<a class="reference internal" href="bibliography.html#id16" title="Stefan Michael Kohlbacher, Matthias Schmid, Thomas Seidel, and Thierry Langer. Applications of the novel quantitative pharmacophore activity relationship method qphar in virtual screening and lead-optimisation. Pharmaceuticals, 2022. URL: https://www.mdpi.com/1424-8247/15/9/1122, doi:10.3390/ph15091122.">11</a>]</span></p></li>
<li><p><a class="reference external" href="https://github.com/molinfo-vienna/Ligand-Interaction-Maps">Analysis of MD-trajectories of ligand-receptor complexes regarding the frequency of observable non-bonding interactions</a></p></li>
<li><p><a class="reference external" href="https://github.com/StefanKohlbacher/QuantPharmacophore">Implementation of the QPhAR algorithm</a> <span id="id12">[<a class="reference internal" href="bibliography.html#id19" title="Marcus Wieder, Arthur Garon, Ugo Perricone, Stefan Boresch, Thomas Seidel, Anna Maria Almerico, and Thierry Langer. Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. Journal of Chemical Information and Modeling, 57(2):365-385, 2017. PMID: 28072524. URL: https://doi.org/10.1021/acs.jcim.6b00674, arXiv:https://doi.org/10.1021/acs.jcim.6b00674, doi:10.1021/acs.jcim.6b00674.">10</a>]</span></p></li>
</ul>
</section>
<section id="scientific-publications">
<span id="index-5"></span><h2>Scientific publications<a class="headerlink" href="#scientific-publications" title="Permalink to this heading"></a></h2>
<p>Published scientific work that relies on CDPKit functionality:</p>
<div class="docutils container" id="id13">
<ul class="simple">
<li id="id22"><p>Thomas Seidel, Christian Permann, Oliver Wieder, Stefan M. Kohlbacher, and Thierry Langer. High-quality conformer generation with conforge: algorithm and performance assessment. <em>Journal of Chemical Information and Modeling</em>, 0(0):null, 0. PMID: 37624145. URL: <a class="reference external" href="https://doi.org/10.1021/acs.jcim.3c00563">https://doi.org/10.1021/acs.jcim.3c00563</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1021/acs.jcim.3c00563">arXiv:https://doi.org/10.1021/acs.jcim.3c00563</a>, <a class="reference external" href="https://doi.org/10.1021/acs.jcim.3c00563">doi:10.1021/acs.jcim.3c00563</a>.</p></li>
<li id="id23"><p>Oliver Wieder, Mélaine Kuenemann, Marcus Wieder, Thomas Seidel, Christophe Meyer, Sharon D. Bryant, and Thierry Langer. Improved lipophilicity and aqueous solubility prediction with composite graph neural networks. <em>Molecules</em>, 2021. URL: <a class="reference external" href="https://www.mdpi.com/1420-3049/26/20/6185">https://www.mdpi.com/1420-3049/26/20/6185</a>, <a class="reference external" href="https://doi.org/10.3390/molecules26206185">doi:10.3390/molecules26206185</a>.</p></li>
<li id="id14"><p>Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte, Angelica Mazzolari, Alessandro Pedretti, Giulio Vistoli, Thierry Langer, Filip Miljković, and Johannes Kirchmair. Active learning approach for guiding site-of-metabolism measurement and annotation. <em>Journal of Chemical Information and Modeling</em>, 0(0):null, 0. PMID: 38170877. URL: <a class="reference external" href="https://doi.org/10.1021/acs.jcim.3c01588">https://doi.org/10.1021/acs.jcim.3c01588</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1021/acs.jcim.3c01588">arXiv:https://doi.org/10.1021/acs.jcim.3c01588</a>, <a class="reference external" href="https://doi.org/10.1021/acs.jcim.3c01588">doi:10.1021/acs.jcim.3c01588</a>.</p></li>
<li id="id30"><p>Doris A. Schuetz, Thomas Seidel, Arthur Garon, Riccardo Martini, Markus Körbel, Gerhard F. Ecker, and Thierry Langer. Grail: grids of pharmacophore interaction fields. <em>Journal of Chemical Theory and Computation</em>, 14(9):4958–4970, 2018. PMID: 30075621. URL: <a class="reference external" href="https://doi.org/10.1021/acs.jctc.8b00495">https://doi.org/10.1021/acs.jctc.8b00495</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1021/acs.jctc.8b00495">arXiv:https://doi.org/10.1021/acs.jctc.8b00495</a>, <a class="reference external" href="https://doi.org/10.1021/acs.jctc.8b00495">doi:10.1021/acs.jctc.8b00495</a>.</p></li>
<li id="id31"><p>Marcus Wieder, Arthur Garon, Ugo Perricone, Stefan Boresch, Thomas Seidel, Anna Maria Almerico, and Thierry Langer. Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. <em>Journal of Chemical Information and Modeling</em>, 57(2):365–385, 2017. PMID: 28072524. URL: <a class="reference external" href="https://doi.org/10.1021/acs.jcim.6b00674">https://doi.org/10.1021/acs.jcim.6b00674</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1021/acs.jcim.6b00674">arXiv:https://doi.org/10.1021/acs.jcim.6b00674</a>, <a class="reference external" href="https://doi.org/10.1021/acs.jcim.6b00674">doi:10.1021/acs.jcim.6b00674</a>.</p></li>
<li id="id28"><p>Stefan Michael Kohlbacher, Matthias Schmid, Thomas Seidel, and Thierry Langer. Applications of the novel quantitative pharmacophore activity relationship method qphar in virtual screening and lead-optimisation. <em>Pharmaceuticals</em>, 2022. URL: <a class="reference external" href="https://www.mdpi.com/1424-8247/15/9/1122">https://www.mdpi.com/1424-8247/15/9/1122</a>, <a class="reference external" href="https://doi.org/10.3390/ph15091122">doi:10.3390/ph15091122</a>.</p></li>
<li id="id27"><p>Jörg Heider, Jonas Kilian, Aleksandra Garifulina, Steffen Hering, Thierry Langer, and Thomas Seidel. Apo2ph4: a versatile workflow for the generation of receptor-based pharmacophore models for virtual screening. <em>Journal of Chemical Information and Modeling</em>, 63(1):101–110, 2023. PMID: 36526584. URL: <a class="reference external" href="https://doi.org/10.1021/acs.jcim.2c00814">https://doi.org/10.1021/acs.jcim.2c00814</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1021/acs.jcim.2c00814">arXiv:https://doi.org/10.1021/acs.jcim.2c00814</a>, <a class="reference external" href="https://doi.org/10.1021/acs.jcim.2c00814">doi:10.1021/acs.jcim.2c00814</a>.</p></li>
<li id="id29"><p>Stefan M. Kohlbacher, Thierry Langer, and Thomas Seidel. Qphar: quantitative pharmacophore activity relationship: method and validation. <em>Journal of Cheminformatics</em>, 13(1):57, Aug 2021. URL: <a class="reference external" href="https://doi.org/10.1186/s13321-021-00537-9">https://doi.org/10.1186/s13321-021-00537-9</a>, <a class="reference external" href="https://doi.org/10.1186/s13321-021-00537-9">doi:10.1186/s13321-021-00537-9</a>.</p></li>
</ul>
</div>
</section>
<section id="how-to-cite">
<span id="index-6"></span><h2>How to cite<a class="headerlink" href="#how-to-cite" title="Permalink to this heading"></a></h2>
<ul class="simple">
<li><p><em>Source code:</em> Thomas Seidel, <em>Chemical Data Processing Toolkit source code repository</em>, <a class="reference external" href="https://github.com/molinfo-vienna/CDPKit">https://github.com/molinfo-vienna/CDPKit</a></p></li>
<li><p><em>Documentation:</em> Thomas Seidel, Oliver Wieder, <em>Chemical Data Processing Toolkit documentation pages</em>, <a class="reference external" href="https://cdpkit.org">https://cdpkit.org</a></p></li>
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<section id="people">
<span id="index-7"></span><h2>People<a class="headerlink" href="#people" title="Permalink to this heading"></a></h2>
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<li><p><a class="reference external" href="https://cheminfo.univie.ac.at/people/senior-scientists/thomas-seidel">Thomas Seidel</a> (project founder, main developer)</p></li>
<li><p><a class="reference external" href="https://cheminfo.univie.ac.at/people/post-doctoral-researchers/oliver-wieder">Oliver Wieder</a> (documentation)</p></li>
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