File size: 1,947 Bytes
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Any, Dict, List
from pxdbench.tools.base import BasePredictor
class MPNNPredictor(BasePredictor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
dir_name = os.path.dirname(__file__)
self.script_path = os.path.join(dir_name, "main_mpnn.py")
def design_monomer(
self, pdb_dir: str, pdb_names: List[str], num_samples: int
) -> List[Dict]:
input_data = {
"pdb_dir": pdb_dir,
"pdb_names": pdb_names,
"num_samples": num_samples,
"mpnn_cfg": self.cfg.to_dict(),
"design_type": "monomer",
}
output = self.run(input_data)
return output
def design_binder(
self,
pdb_dir: str,
pdb_names: List[str],
num_samples: int,
binder_chains: List[str],
cond_chains: List[str],
) -> List[Dict]:
input_data = {
"pdb_dir": pdb_dir,
"pdb_names": pdb_names,
"num_samples": num_samples,
"binder_chains": binder_chains,
"cond_chains": cond_chains,
"mpnn_cfg": self.cfg.to_dict(),
"design_type": "binder",
}
output = self.run(input_data)
return output
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