Spaces:
Running
Running
File size: 9,023 Bytes
bca3a49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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 sys
from env_consts import TEST_INPUT_DIR, TEST_OUTPUT_DIR, CKPT_PATH
import json
import logging
import numpy as np
import os
import pickle
from dockformer.data.data_modules import OpenFoldSingleDataset
logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(level=logging.INFO)
import torch
torch_versions = torch.__version__.split(".")
torch_major_version = int(torch_versions[0])
torch_minor_version = int(torch_versions[1])
if (
torch_major_version > 1 or
(torch_major_version == 1 and torch_minor_version >= 12)
):
# Gives a large speedup on Ampere-class GPUs
torch.set_float32_matmul_precision("high")
torch.set_grad_enabled(False)
from dockformer.config import model_config
from dockformer.utils.script_utils import (load_models_from_command_line, run_model, save_output_structure,
get_latest_checkpoint)
from dockformer.utils.tensor_utils import tensor_tree_map
def list_files_with_extensions(dir, extensions):
return [f for f in os.listdir(dir) if f.endswith(extensions)]
def override_config(base_config, overriding_config):
for k, v in overriding_config.items():
if isinstance(v, dict):
base_config[k] = override_config(base_config[k], v)
else:
base_config[k] = v
return base_config
def run_on_folder(input_dir: str, output_dir: str, run_config_path: str, skip_relaxation=True,
long_sequence_inference=False, skip_exists=False):
config_preset = "initial_training"
save_outputs = False
device_name = "cuda" if torch.cuda.is_available() else "cpu"
run_config = json.load(open(run_config_path))
ckpt_path = CKPT_PATH
if ckpt_path is None:
ckpt_path = get_latest_checkpoint(os.path.join(run_config["train_output_dir"], "checkpoint"))
print("Using checkpoint: ", ckpt_path)
config = model_config(config_preset, long_sequence_inference=long_sequence_inference)
config = override_config(config, run_config.get("override_conf", {}))
model_generator = load_models_from_command_line(
config,
model_device=device_name,
model_checkpoint_path=ckpt_path,
output_dir=output_dir)
print("Model loaded")
model, output_directory = next(model_generator)
dataset = OpenFoldSingleDataset(data_dir=input_dir, config=config.data, mode="predict")
for i, processed_feature_dict in enumerate(dataset):
tag = dataset.get_metadata_for_idx(i)["input_name"]
print("Processing", tag)
output_name = f"{tag}_predicted"
protein_output_path = os.path.join(output_directory, f'{output_name}_protein.pdb')
if os.path.exists(protein_output_path) and skip_exists:
print("skipping exists", output_name)
continue
# turn into a batch of size 1
processed_feature_dict = {key: value.unsqueeze(0).to(device_name)
for key, value in processed_feature_dict.items()}
out = run_model(model, processed_feature_dict, tag, output_dir)
# Toss out the recycling dimensions --- we don't need them anymore
processed_feature_dict = tensor_tree_map(
lambda x: np.array(x[..., -1].cpu()),
processed_feature_dict
)
out = tensor_tree_map(lambda x: np.array(x.cpu()), out)
affinity_output_path = os.path.join(output_directory, f'{output_name}_affinity.json')
# affinity = torch.sum(torch.softmax(torch.tensor(out["affinity_2d_logits"]), -1) * torch.linspace(0, 15, 32),
# dim=-1).item()
affinity_2d = torch.sum(torch.softmax(torch.tensor(out["affinity_2d_logits"]), -1) * torch.linspace(0, 15, 32),
dim=-1).item()
affinity_1d = torch.sum(torch.softmax(torch.tensor(out["affinity_1d_logits"]), -1) * torch.linspace(0, 15, 32),
dim=-1).item()
affinity_cls = torch.sum(torch.softmax(torch.tensor(out["affinity_cls_logits"]), -1) * torch.linspace(0, 15, 32),
dim=-1).item()
affinity_2d_max = torch.linspace(0, 15, 32)[torch.argmax(torch.tensor(out["affinity_2d_logits"]))].item()
affinity_1d_max = torch.linspace(0, 15, 32)[torch.argmax(torch.tensor(out["affinity_1d_logits"]))].item()
affinity_cls_max = torch.linspace(0, 15, 32)[torch.argmax(torch.tensor(out["affinity_cls_logits"]))].item()
print("Affinity: ", affinity_2d, affinity_cls, affinity_1d)
with open(affinity_output_path, "w") as f:
json.dump({"affinity_2d": affinity_2d, "affinity_1d": affinity_1d, "affinity_cls": affinity_cls,
"affinity_2d_max": affinity_2d_max, "affinity_1d_max": affinity_1d_max,
"affinity_cls_max": affinity_cls_max}, f)
# binding_site = torch.sigmoid(torch.tensor(out["binding_site_logits"])) * 100
# binding_site = binding_site[:processed_feature_dict["aatype"].shape[1]].flatten()
# predicted_contacts = torch.sigmoid(torch.tensor(out["inter_contact_logits"])) * 100
# binding_site = torch.max(predicted_contacts, dim=2).values.flatten()
ligand_output_path = os.path.join(output_directory, f"{output_name}_ligand_{{i}}.sdf")
protein_mask = processed_feature_dict["protein_mask"][0].astype(bool)
ligand_mask = processed_feature_dict["ligand_mask"][0].astype(bool)
save_output_structure(
aatype=processed_feature_dict["aatype"][0][protein_mask],
residue_index=processed_feature_dict["in_chain_residue_index"][0],
chain_index=processed_feature_dict["chain_index"][0],
plddt=out["plddt"][0][protein_mask],
final_atom_protein_positions=out["final_atom_positions"][0][protein_mask],
final_atom_mask=out["final_atom_mask"][0][protein_mask],
ligand_atype=processed_feature_dict["ligand_atype"][0].astype(int),
ligand_chiralities=processed_feature_dict["ligand_chirality"][0].astype(int),
ligand_charges= processed_feature_dict["ligand_charge"][0].astype(int),
ligand_bonds=processed_feature_dict["ligand_bonds"][0].astype(int),
ligand_idx=processed_feature_dict["ligand_idx"][0].astype(int),
ligand_bonds_idx=processed_feature_dict["ligand_bonds_idx"][0].astype(int),
final_ligand_atom_positions=out["final_atom_positions"][0][ligand_mask][:, 1, :], # only ca index
protein_output_path=protein_output_path,
ligand_output_path=ligand_output_path,
)
logger.info(f"Output written to {protein_output_path}...")
if not skip_relaxation:
# Relax the prediction.
logger.info(f"Running relaxation on {protein_output_path}...")
from dockformer.utils.relax import relax_complex
try:
relax_complex(protein_output_path,
ligand_output_path,
os.path.join(output_directory, f'{output_name}_protein_relaxed.pdb'),
os.path.join(output_directory, f'{output_name}_ligand_relaxed.sdf'))
except Exception as e:
logger.error(f"Failed to relax {protein_output_path} due to {e}...")
if save_outputs:
output_dict_path = os.path.join(
output_directory, f'{output_name}_output_dict.pkl'
)
with open(output_dict_path, "wb") as fp:
pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(f"Model output written to {output_dict_path}...")
if __name__ == "__main__":
config_path = sys.argv[1] if len(sys.argv) > 1 else os.path.join(os.path.dirname(__file__), "run_config.json")
input_dir, output_dir = TEST_INPUT_DIR, TEST_OUTPUT_DIR
options = {"skip_relaxation": True, "long_sequence_inference": False}
if len(sys.argv) > 3:
input_dir = sys.argv[2]
output_dir = sys.argv[3]
if "--relax" in sys.argv:
options["skip_relaxation"] = False
if "--long" in sys.argv:
options["long_sequence_inference"] = True
if "--allow-skip" in sys.argv:
options["skip_exists"] = True
run_on_folder(input_dir, output_dir, config_path, **options)
|