diff diffdock.xml @ 0:bfba870d3537 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/main/tools/diffdock/ commit b0db941a49bdfaa799b10cd68f9b7d8509f608d2

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/diffdock.xml	Thu Jul 03 13:43:38 2025 +0000
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+<tool id="diffdock" name="diffdock" version="@TOOL_VERSION@+@VERSION_SUFFIX@" profile="24.2" license="MIT">
+    <description>Predict ligand binding poses using DiffDock's diffusion-based docking method</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="requirements" />
+    <expand macro="creators" />
+    <command detect_errors="aggressive"><![CDATA[
+        ln -s /home/appuser/DiffDock/* . &&
+        ln -s '$protein_path' protein_path.pdb &&
+        ln -s '$ligand_description' ligand_description.sdf &&
+        micromamba run -n diffdock python -m inference
+        --protein_path protein_path.pdb
+        --ligand_description ligand_description.sdf
+        --out_dir results
+    ]]></command>
+    <inputs>
+        <param argument="--protein_path" type="data" format="pdb" label="Protein structure" help="3D structure of the protein receptor in PDB format. This is the target for ligand docking."/>
+        <param argument="--ligand_description" type="data" format="sdf" label="Ligand molecule" help="Structure of the ligand(s) to be docked in SDF format. Multiple molecules can be included."/>
+        <param argument="--samples_per_complex" type="integer" label="Samples per complex" value="10" help="Number of binding pose samples to generate for each protein-ligand complex."/>
+        <param argument="--inference_steps" type="integer" label="Total inference steps" value="20" help="Total number of denoising steps to run in the diffusion model."/>
+        <param argument="--actual_steps" type="integer" label="Actual denoising steps" value="19" help="Number of denoising steps that are actually performed. Must be less than or equal to inference steps."/>
+    </inputs>
+    <outputs>
+        <collection name="output_collection" type="list" label="${tool.name} on ${on_string}">
+            <discover_datasets pattern="__name_and_ext__" directory="results" recurse="true" format="sdf"/>
+        </collection>
+    </outputs>
+    <tests>
+    <test expect_num_outputs="1">
+        <param name="protein_path" value="1a0q_protein_processed.pdb"/>
+        <param name="ligand_description" value="1a0q_ligand.sdf"/>
+        <param name="samples_per_complex" value="10"/>
+        <param name="inference_steps" value="20"/>
+        <param name="actual_steps" value="19"/>
+        <output_collection name="output_collection" type="list">
+            <element name="rank1">
+                <assert_contents>
+                    <has_n_lines n="52"/>
+                    <has_line line="1a0q_ligand"/>
+                    <has_line line=" 23 23  0  0  0  0  0  0  0  0999 V2000"/>
+                </assert_contents>
+            </element>
+        </output_collection>
+    </test>
+    </tests>
+    <help><![CDATA[
+DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
+------------------------------------------------------------------
+
+DiffDock is a novel deep learning-based docking method using diffusion models to predict ligand binding poses.
+
+For more information, visit the `DiffDock GitHub repository <https://github.com/gcorso/DiffDock>`_
+
+**License**
+
+* `MIT <https://raw.githubusercontent.com/gcorso/DiffDock/refs/heads/main/LICENSE>`_
+
+    ]]></help>
+    <expand macro="citations" />
+</tool>
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