deploy: d193a9f

Author: liugangcode

_sources/index.rst.txt

@@ -6,7 +6,7 @@
 torch-molecule documentation
 ============================
 
-Welcome to the Torch-Molecule documentation. Torch-Molecule is an actively developed package designed to facilitate molecular discovery through deep learning. It offers a user-friendly interface similar to `sklearn`, and includes model checkpoints for efficient deployment and benchmarking across various molecular tasks. The package currently focuses on three main components:
+Welcome to the torch-molecule documentation. torch-molecule is an actively developed package designed to facilitate molecular discovery through deep learning. It offers a user-friendly interface similar to `sklearn`, and includes model checkpoints for efficient deployment and benchmarking across various molecular tasks. The package currently focuses on three main components:
 
 - Predictors: Molecular property prediction models that make predictions (regression or classification) based on molecular graphs.
 - Generators: Molecular graph generators that generate new molecules.

index.html

@@ -260,7 +260,7 @@
         <article role="main" id="furo-main-content">
           <section id="torch-molecule-documentation">
 <h1>torch-molecule documentation<a class="headerlink" href="#torch-molecule-documentation" title="Link to this heading">¶</a></h1>
-<p>Welcome to the Torch-Molecule documentation. Torch-Molecule is an actively developed package designed to facilitate molecular discovery through deep learning. It offers a user-friendly interface similar to <cite>sklearn</cite>, and includes model checkpoints for efficient deployment and benchmarking across various molecular tasks. The package currently focuses on three main components:</p>
+<p>Welcome to the torch-molecule documentation. torch-molecule is an actively developed package designed to facilitate molecular discovery through deep learning. It offers a user-friendly interface similar to <cite>sklearn</cite>, and includes model checkpoints for efficient deployment and benchmarking across various molecular tasks. The package currently focuses on three main components:</p>
 <ul class="simple">
 <li><p>Predictors: Molecular property prediction models that make predictions (regression or classification) based on molecular graphs.</p></li>
 <li><p>Generators: Molecular graph generators that generate new molecules.</p></li>