Update inference_app.py

inference_app.py

@@ -10,6 +10,7 @@ from scipy.optimize import differential_evolution, NonlinearConstraint
 from biotite.structure.io.pdb import PDBFile
 from rdkit import Chem
 from rdkit.Chem import AllChem
+from rdkit.Geometry import Point3D
 
 
 def generate_input_conformer(
@@ -61,50 +62,48 @@ def set_protein_to_new_coord(input_pdb_file, new_coord, output_file):
     file.write(output_file)
 
 
-def optimize_coordinate(points, bound_buffer=15, dmin=6.05):
+# def optimize_coordinate(points, bound_buffer=15, dmin=6.05):
 
-    bounds = list(
-        zip(
-            np.average(points, axis=0) - [bound_buffer]*3,
-            np.average(points, axis=0) + [bound_buffer]*3
-            )
-        )
+#     bounds = list(
+#         zip(
+#             np.average(points, axis=0) - [bound_buffer]*3,
+#             np.average(points, axis=0) + [bound_buffer]*3
+#             )
+#         )
 
-    # Define the constraint function (ensure dmin distance)
-    con = NonlinearConstraint(lambda x: np.min(np.linalg.norm(points - x, axis=1)), dmin, 8)
+#     # Define the constraint function (ensure dmin distance)
+#     con = NonlinearConstraint(lambda x: np.min(np.linalg.norm(points - x, axis=1)), dmin, 8)
 
-    # Define the objective function (minimize pairwise distance)
-    def objective(x):
-        return np.sum(np.linalg.norm(points - x, axis=1))
+#     # Define the objective function (minimize pairwise distance)
+#     def objective(x):
+#         return np.sum(np.linalg.norm(points - x, axis=1))
 
-    # Perform differential evolution to find the optimal coordinate
-    result = differential_evolution(objective, bounds, constraints=con)
+#     # Perform differential evolution to find the optimal coordinate
+#     result = differential_evolution(objective, bounds, constraints=con)
 
-    return result.x, result.fun
+#     return result.x, result.fun
 
 
 def predict(input_sequence, input_ligand, input_msa, input_protein):
     start_time = time.time()
     
     # Do inference here
-    mol = generate_input_conformer(input_ligand, minimize_maxIters=500)
-    
+    mol = generate_input_conformer(input_ligand)
+    conf = mol.GetConformer()
+    # set ligand
+    for i in range(mol.GetNumAtoms()):
+        conf.SetAtomPosition(i, Point3D(0,0,0))    
     molwriter = Chem.SDWriter("test_docking_pose.sdf")
     molwriter.write(mol)
 
-    mol_coords = mol.GetConformer().GetPositions()    
-    # new_coord = [0, 0, 0]
-    # new_coord = np.mean(mol_coords, axis=0) + [3.5, 3.5, 3.5]
-    new_coord, min_dist_sum = optimize_coordinate(mol_coords)
-    # get mindist to protein
-    min_dist = np.min(np.linalg.norm(mol_coords - new_coord, axis=1))
-
+    # set protein
+    new_coord = [6.05, 0, 0]
     output_file = "test_out.pdb"
     set_protein_to_new_coord(input_protein, new_coord, output_file)
     
     # return an output pdb file with the protein and ligand with resname LIG or UNK. 
     # also return any metrics you want to log, metrics will not be used for evaluation but might be useful for users
-    metrics = {"min_dist": min_dist, "min_dist_sum": min_dist_sum}
+    metrics = {}
     
     end_time = time.time()
     run_time = end_time - start_time