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+FROM gnina/gnina:v1.1
+
+RUN useradd -m -u 1000 user
+WORKDIR /usr/src/app
+
+COPY --link --chown=1000 ./ /usr/src/app
+COPY . .
+
+
+RUN pip3 install rdkit==2023.9.6 pandas==2.1.4 posebusters==0.2.7
+RUN pip3 install gradio gradio_molecule3d
+
+RUN git clone https://github.com/rlabduke/reduce.git && cd reduce && make && make install
+
+USER user
+
+EXPOSE 7860
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+
+CMD ["python3", "inference_app.py"]
+

LICENSE

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+MIT License
+
+Copyright (c) 2024 inductive-bio
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

inference_app.py

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+# Runs the full strong baseline, including smina/vina docking,
+# gnina rescoring, and an input conformational ensemble.
+import argparse
+import os
+import shutil
+import subprocess
+
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import AllChem, PandasTools, rdMolTransforms
+
+import numpy as np
+from moleculekit.molecule import Molecule
+
+import time
+
+import gradio as gr
+
+from gradio_molecule3d import Molecule3D
+
+def protonate_receptor_and_ligand(protein,ligand):
+    protein_out = protein.replace(".pdb","_H.pdb")
+    with open(protein_out, "w") as f:
+        subprocess.run(
+            ["reduce", "-BUILD", protein],
+            stdout=f,
+            stderr=subprocess.DEVNULL,
+        )
+    ligand_out = ligand.replace(".pdb","_H.pdb")
+    subprocess.run(["obabel", ligand, "-O", ligand_out, "-p", "7.4"])
+
+
+def generate_conformers(ligand, num_confs=8):
+    mol = Chem.MolFromMolFile(
+         ligand.replace(".pdb","_H.pdb")
+    )
+    mol.RemoveAllConformers()
+    mol = Chem.AddHs(mol)
+    AllChem.EmbedMultipleConfs(mol, numConfs=num_confs, randomSeed=1)
+    AllChem.UFFOptimizeMoleculeConfs(mol)
+    with Chem.SDWriter(
+         ligand.replace(".pdb","_multiple_confs.pdb")
+    ) as writer:
+        for cid in range(mol.GetNumConformers()):
+            writer.write(mol, confId=cid)
+
+def get_bb(points):
+    """Return bounding box from a set of points (N,3)
+
+    Parameters
+    ----------
+    points : numpy.ndarray
+        Set of points (N,3)
+
+    Returns
+    -------
+    boundingBox : list
+        List of the form [xmin, xmax, ymin, ymax, zmin, zmax]
+
+    """
+    minx = np.min(points[:, 0])
+    maxx = np.max(points[:, 0])
+
+    miny = np.min(points[:, 1])
+    maxy = np.max(points[:, 1])
+
+    minz = np.min(points[:, 2])
+    maxz = np.max(points[:, 2])
+    bb = [[minx, miny, minz], [maxx, maxy, maxz]]
+    return bb
+
+def run_docking(protein, ligand):
+    
+    mol = Molecule(protein)
+    mol.center()
+    bb = get_bb(mol.coords)
+    size_x = bb[1][0] - bb[0][0]
+    size_y = bb[1][1] - bb[0][1]
+    size_z = bb[1][2] - bb[0][2]
+
+    subprocess.run(
+        [
+            "gnina",
+            "-r",
+            protein.replace(".pdb","_H.pdb"),
+            "-l",
+            ligand.replace(".sdf","_ligand_multiple_confs.sdf"),
+            "-o",
+            ligand.replace(".sdf","_multiple_confs_poses.sdf"),
+            "--center_x",  # bounding box matching PoseBusters methodology
+            str(0),
+            "--center_y",
+            str(0),
+            "--center_z",
+            str(0),
+            "--size_x",
+            str(size_x),
+            "--size_y",
+            str(size_y),
+            "--size_z",
+            str(size_z),
+            "--scoring",
+            "vina",
+            "--exhaustiveness",
+            "4",
+            "--num_modes",
+            "1",
+            "--seed",
+            "1",
+        ]
+    )
+    # sort the poses from the multiple conformation runs, so overall best is first
+    poses = PandasTools.LoadSDF(
+         ligand.replace(".sdf","_multiple_confs_poses.sdf")
+    )
+    poses["CNNscore"] = poses["CNNscore"].astype(float)
+    gnina_order = poses.sort_values("CNNscore", ascending=False).reset_index(drop=True)
+    PandasTools.WriteSDF(
+        gnina_order,
+         ligand.replace(".sdf","_multiple_confs_poses.sdf")
+        properties=list(poses.columns),
+    )
+    return poses["CNNscore"]
+
+
+def predict (input_sequence, input_ligand,input_msa, input_protein):
+    start_time = time.time()
+
+    protonate_receptor_and_ligand(input_protein, input_ligand)
+    generate_conformers(input_protein, input_ligand)
+    cnn_score = run_docking(input_protein, input_ligand)
+    metrics = {"cnn_score": cnn_score}
+    end_time = time.time()
+    run_time = end_time - start_time
+    return ["test_out.pdb", "test_docking_pose.sdf"], metrics, run_time
+
+with gr.Blocks() as app:
+
+    gr.Markdown("# Template for inference")
+
+    gr.Markdown("Title, description, and other information about the model")   
+    with gr.Row():
+        input_sequence = gr.Textbox(lines=3, label="Input Protein sequence (FASTA)")
+        input_ligand = gr.Textbox(lines=3, label="Input ligand SMILES")
+    with gr.Row():
+        input_msa = gr.File(label="Input Protein MSA (A3M)")
+        input_protein = gr.File(label="Input protein monomer")
+        
+    
+    # define any options here
+
+    # for automated inference the default options are used
+    # slider_option = gr.Slider(0,10, label="Slider Option")
+    # checkbox_option = gr.Checkbox(label="Checkbox Option")
+    # dropdown_option = gr.Dropdown(["Option 1", "Option 2", "Option 3"], label="Radio Option")
+
+    btn = gr.Button("Run Inference")
+
+    gr.Examples(
+        [
+            [
+                "SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL:SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL",
+                "COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O",
+                "test_out.pdb"
+            ],
+        ],
+        [input_sequence, input_ligand, input_protein],
+    )
+    reps =    [
+    {
+      "model": 0,
+      "style": "cartoon",
+      "color": "whiteCarbon",
+    },
+        {
+      "model": 1,
+      "style": "stick",
+      "color": "greenCarbon",
+    }
+        
+  ]
+    
+    out = Molecule3D(reps=reps)
+    metrics = gr.JSON(label="Metrics")
+    run_time = gr.Textbox(label="Runtime")
+
+    btn.click(predict, inputs=[input_sequence, input_ligand, input_msa, input_protein], outputs=[out,metrics, run_time])
+
+app.launch()