Update inference_app.py

inference_app.py

@@ -18,7 +18,7 @@ import gradio as gr
 
 from gradio_molecule3d import Molecule3D
 
-def protonate_receptor_and_ligand(protein,ligand):
+def protonate_receptor_and_ligand(protein):
     protein_out = protein.replace(".pdb","_H.pdb")
     with open(protein_out, "w") as f:
         subprocess.run(
@@ -26,20 +26,18 @@ def protonate_receptor_and_ligand(protein,ligand):
             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 = Chem.MolFromSmiles(
+         ligand
     )
     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")
+         "ligand.sdf"
     ) as writer:
         for cid in range(mol.GetNumConformers()):
             writer.write(mol, confId=cid)
@@ -84,9 +82,9 @@ def run_docking(protein, ligand):
             "-r",
             protein.replace(".pdb","_H.pdb"),
             "-l",
-            ligand.replace(".sdf","_ligand_multiple_confs.sdf"),
+            "ligand.sdf",
             "-o",
-            ligand.replace(".sdf","_multiple_confs_poses.sdf"),
+            "ligand_output.sdf",
             "--center_x",  # bounding box matching PoseBusters methodology
             str(0),
             "--center_y",
@@ -111,13 +109,13 @@ def run_docking(protein, ligand):
     )
     # sort the poses from the multiple conformation runs, so overall best is first
     poses = PandasTools.LoadSDF(
-         ligand.replace(".sdf","_multiple_confs_poses.sdf")
+         "ligand_output.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"),
+        "ligand_output.sdf",
         properties=list(poses.columns),
     )
     return poses["CNNscore"]
@@ -131,7 +129,7 @@ def predict (input_sequence, input_ligand,input_msa, input_protein):
     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
+    return ["input_protein.pdb", "ligand_output.sdf"], metrics, run_time
 
 with gr.Blocks() as app: