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---
title: Tokenization
keywords: fastai
sidebar: home_sidebar
summary: "Tokenization and SMILES utils"
description: "Tokenization and SMILES utils"
nb_path: "nbs/01_tokenization.ipynb"
---
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<h2 id="Reaction-SMILES-tokenizer">Reaction SMILES tokenizer<a class="anchor-link" href="#Reaction-SMILES-tokenizer"> </a></h2><p>We use the same tokenizer as the one used for <a href="http://rxnmapper.ai">http://rxnmapper.ai</a>.</p>
<p>It is an instance of <a href="https://github.com/huggingface/transformers">Hugginface Transformers</a> BertTokenizer.</p>
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<h4 id="get_default_tokenizer" class="doc_header"><code>get_default_tokenizer</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L23" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_default_tokenizer</code>()</p>
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<h2 id="SmilesTokenizer" class="doc_header"><code>class</code> <code>SmilesTokenizer</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L33" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>SmilesTokenizer</code>(<strong><code>vocab_file</code></strong>:<code>str</code>, <strong><code>unk_token</code></strong>:<code>str</code>=<em><code>'[UNK]'</code></em>, <strong><code>sep_token</code></strong>:<code>str</code>=<em><code>'[SEP]'</code></em>, <strong><code>pad_token</code></strong>:<code>str</code>=<em><code>'[PAD]'</code></em>, <strong><code>cls_token</code></strong>:<code>str</code>=<em><code>'[CLS]'</code></em>, <strong><code>mask_token</code></strong>:<code>str</code>=<em><code>'[MASK]'</code></em>, <strong><code>do_lower_case</code></strong>=<em><code>False</code></em>, <strong>**<code>kwargs</code></strong>) :: <code>BertTokenizer</code></p>
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<p>Constructs a SmilesBertTokenizer.
Adapted from <a href="https://github.com/huggingface/transformers">https://github.com/huggingface/transformers</a>
and <a href="https://github.com/rxn4chemistry/rxnfp">https://github.com/rxn4chemistry/rxnfp</a>.</p>
<p>Args:
vocabulary_file: path to a token per line vocabulary file.</p>
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<h2 id="RegexTokenizer" class="doc_header"><code>class</code> <code>RegexTokenizer</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L94" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>RegexTokenizer</code>(<strong><code>regex_pattern</code></strong>:<code>str</code>=<em>`'(\%$[0-9]{3}$|$$[^$$]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\||$|$|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\</em>|\$|\%[0-9]{2}|[0-9])'`*)</p>
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<p>Run regex tokenization</p>
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<h2 id="Smiles-Utils">Smiles Utils<a class="anchor-link" href="#Smiles-Utils"> </a></h2>
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<h2 id="NotCanonicalizableSmilesException" class="doc_header"><code>class</code> <code>NotCanonicalizableSmilesException</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L118" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>NotCanonicalizableSmilesException</code>() :: <code>ValueError</code></p>
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<p>Inappropriate argument value (of correct type).</p>
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<h4 id="canonicalize_smi" class="doc_header"><code>canonicalize_smi</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L122" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>canonicalize_smi</code>(<strong><code>smi</code></strong>, <strong><code>remove_atom_mapping</code></strong>=<em><code>False</code></em>)</p>
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<p>Canonicalize SMILES</p>
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<h4 id="process_reaction" class="doc_header"><code>process_reaction</code><a href="https://github.com/rxn4chemistry/rxnfp/tree/master/rxnfp/tokenization.py#L136" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>process_reaction</code>(<strong><code>rxn</code></strong>)</p>
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<p>Process and canonicalize reaction SMILES</p>
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<h1 id="Examples">Examples<a class="anchor-link" href="#Examples"> </a></h1><p><a href="/rxnfp/tokenization.html#process_reaction"><code>process_reaction</code></a> removes atom-mapping, canonicalizes the molecules and combines reactant and reactions</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">atom_mapped_rxn</span> <span class="o">=</span> <span class="s1">'F[c:5]1[n:6][cH:7][cH:8][cH:9][c:10]1[F:11].[CH3:1][CH:2]([CH3:3])[SH:4]>CN(C)C=O.O=C([O-])[O-].[K+].[K+]>[CH3:1][CH:2]([CH3:3])[S:4][c:5]1[n:6][cH:7][cH:8][cH:9][c:10]1[F:11]'</span>
<span class="n">canonical_rxn</span> <span class="o">=</span> <span class="s2">"CC(C)S.CN(C)C=O.Fc1cccnc1F.O=C([O-])[O-].[K+].[K+]>>CC(C)Sc1ncccc1F"</span>
<span class="n">tokenized_rxn</span> <span class="o">=</span> <span class="s1">'C C ( C ) S . C N ( C ) C = O . F c 1 c c c n c 1 F . O = C ( [O-] ) [O-] . [K+] . [K+] >> C C ( C ) S c 1 n c c c c 1 F'</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">AllChem</span><span class="o">.</span><span class="n">ReactionFromSmarts</span><span class="p">(</span><span class="n">atom_mapped_rxn</span><span class="p">,</span> <span class="n">useSmiles</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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
"
>
</div>
</div>
</div>
</div>
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">canonical_rxn</span> <span class="o">==</span> <span class="n">process_reaction</span><span class="p">(</span><span class="n">atom_mapped_rxn</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">AllChem</span><span class="o">.</span><span class="n">ReactionFromSmarts</span><span class="p">(</span><span class="n">canonical_rxn</span><span class="p">,</span> <span class="n">useSmiles</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea output_execute_result">
<img 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
"
>
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{% endraw %}
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<p><a href="/rxnfp/tokenization.html#SmilesTokenizer"><code>SmilesTokenizer</code></a> splits the reaction SMILES into tokens.</p>
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{% raw %}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">get_default_tokenizer</span><span class="p">()</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">SmilesTokenizer</span><span class="p">)</span>
<span class="n">basic_tokenizer</span> <span class="o">=</span> <span class="n">RegexTokenizer</span><span class="p">()</span>
</pre></div>
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{% endraw %}
{% raw %}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">tokenized_rxn</span> <span class="o">==</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">basic_tokenizer</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">canonical_rxn</span><span class="p">))</span>
</pre></div>
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{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">vocab_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">591</span>
</pre></div>
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{% endraw %}
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