bugfix, 2.0.3

.gitignore

@@ -5,7 +5,6 @@ run.sh
 result/
 examples/library/
 nogit/
-test.sh
 
 
 # Byte-compiled / optimized / DLL files

feature_extraction.py

@@ -14,7 +14,7 @@ def __init__(self):
             help="custom path of protein pdb file (.pdb)",
             required=True,
         )
-        self.add_argument("--out", type=str, help="save path of features (torch object)", required=True)
+        self.add_argument("-o", "--out", type=str, help="save path of features (torch object)", required=True)
         self.add_argument(
             "--ref_ligand",
             type=str,

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pharmaconet"
-version = "2.0.2"
+version = "2.0.3"
 description = "PharmacoNet: Open-Source Software for Protein-based Pharmacophore Modeling and Virtual Screening"
 license = { text = "MIT" }
 authors = [{ name = "Seonghwan Seo", email = "shwan0106@kaist.ac.kr" }]

src/pmnet/__init__.py

@@ -1,6 +1,6 @@
 from .pharmacophore_model import PharmacophoreModel
 
-__version__ = "2.0.2"
+__version__ = "2.0.3"
 __citation_information__ = (
     "Seo, S., & Kim, W. Y. (2023, December). "
     "PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling. "

src/pmnet/module.py

@@ -141,7 +141,7 @@ def get_center(
             extension = os.path.splitext(ref_ligand_path)[1]
             assert extension in [".sdf", ".pdb", ".mol2"]
             ref_ligand = next(pybel.readfile(extension[1:], str(ref_ligand_path)))
-            x, y, z = np.mean([atom.coords for atom in ref_ligand.atoms], axis=0, dtype=np.float32).to_list()
+            x, y, z = np.mean([atom.coords for atom in ref_ligand.atoms], axis=0, dtype=np.float32).tolist()
         return float(x), float(y), float(z)
 
     @torch.no_grad()

src/pmnet/pharmacophore_model.py

@@ -121,7 +121,8 @@ def create(
             center = (x, y, z)
         graph = DensityMapGraph(center, resolution, size)
         for node in hotspot_infos:
-            graph.add_node(node["type"], node["position"], node["score"], node["map"])
+            x, y, z = tuple(node["hotspot_position"].tolist())
+            graph.add_node(node["nci_type"], (x, y, z), float(node["hotspot_score"]), node["point_map"])
         graph.setup()
 
         model = cls()

test/maintain.sh

@@ -0,0 +1,8 @@
+/bin/rm -rf result/6oim
+python modeling.py --cuda --pdb 6oim
+python modeling.py --cuda --pdb 6oim --ref_ligand ./result/6oim/6oim_B_MG.pdb
+python modeling.py --cuda --protein ./result/6oim/6oim.pdb --prefix 6oim
+python modeling.py --cuda --protein ./result/6oim/6oim.pdb --ref_ligand ./result/6oim/6oim_B_MG.pdb --prefix 6oim
+python modeling.py --cuda --protein ./result/6oim/6oim.pdb --center 1.872 -8.260 -1.361 --prefix 6oim
+python screening.py -p ./result/6oim/6oim_D_MOV_model.pm -d ./examples/library/ --cpus 4 -o tmp.csv
+python feature_extraction.py --cuda -p ./result/6oim/6oim.pdb --center 1.872 -8.260 -1.361 -o tmp.pt