From 4d81a065c0e8d52e5e281c84a660a2b54c2cbc99 Mon Sep 17 00:00:00 2001 From: jdurrant Date: Wed, 6 Jan 2021 20:25:21 -0500 Subject: [PATCH] Added license text to files. --- LICENSE.md | 24 ---------- config/__init__.py | 13 ++++++ config/moad_partitions.py | 15 ++++++ leadopt/__init__.py | 13 ++++++ leadopt/data_util.py | 83 +++++++++++++++++++-------------- leadopt/grid_util.py | 27 ++++++++--- leadopt/infer.py | 14 ++++++ leadopt/metrics.py | 20 ++++++-- leadopt/model_conf.py | 58 ++++++++++++++--------- leadopt/models/__init__.py | 13 ++++++ leadopt/models/backport.py | 14 ++++++ leadopt/models/voxel.py | 18 ++++++- leadopt/util.py | 74 +++++++++++++++++------------ scripts/make_fingerprints.py | 21 +++++++-- scripts/merge_moad.py | 31 ++++++++---- scripts/moad_training_splits.py | 26 ++++++++--- scripts/moad_util.py | 27 ++++++++--- scripts/process_moad.py | 55 ++++++++++++++-------- scripts/split_moad.py | 32 +++++++++---- train.py | 15 ++++++ 20 files changed, 419 insertions(+), 174 deletions(-) diff --git a/LICENSE.md b/LICENSE.md index 1751c98..5e03b92 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -172,27 +172,3 @@ any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. _END OF TERMS AND CONDITIONS_ - -### APPENDIX: How to apply the Apache License to your work - -To apply the Apache License to your work, attach the following boilerplate -notice, with the fields enclosed by brackets `[]` replaced with your own -identifying information. (Don't include the brackets!) The text should be -enclosed in the appropriate comment syntax for the file format. We also -recommend that a file or class name and description of purpose be included on -the same "printed page" as the copyright notice for easier identification -within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - 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. diff --git a/config/__init__.py b/config/__init__.py index e69de29..1c2961a 100644 --- a/config/__init__.py +++ b/config/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021 Jacob Durrant + +# 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. diff --git a/config/moad_partitions.py b/config/moad_partitions.py index 0a32d17..59e20bc 100644 --- a/config/moad_partitions.py +++ b/config/moad_partitions.py @@ -1,3 +1,18 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + TRAIN = 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diff --git a/leadopt/__init__.py b/leadopt/__init__.py index e69de29..1c2961a 100644 --- a/leadopt/__init__.py +++ b/leadopt/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021 Jacob Durrant + +# 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. diff --git a/leadopt/data_util.py b/leadopt/data_util.py index 7981bb6..d291c41 100644 --- a/leadopt/data_util.py +++ b/leadopt/data_util.py @@ -1,3 +1,18 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + """ Contains utility code for reading packed data files. """ @@ -10,15 +25,15 @@ import tqdm # Atom typing -# +# # Atom typing is the process of figuring out which layer each atom should be # written to. For ease of testing, the packed data file contains a lot of # potentially useful atomic information which can be distilled during the # data loading process. -# +# # Atom typing is implemented by map functions of the type: # (atom descriptor) -> (layer index) -# +# # If the layer index is -1, the atom is ignored. @@ -139,13 +154,13 @@ def _fn(*args): class FragmentDataset(Dataset): """Utility class to work with the packed fragments.h5 format.""" - + def __init__(self, fragment_file, rec_typer=REC_TYPER['simple'], lig_typer=LIG_TYPER['simple'], filter_rec=None, filter_smi=None, - fdist_min=None, fdist_max=None, fmass_min=None, fmass_max=None, + fdist_min=None, fdist_max=None, fmass_min=None, fmass_max=None, verbose=False, lazy_loading=True): """Initializes the fragment dataset. - + Args: fragment_file: path to fragments.h5 rec_typer: AtomTyper for receptor @@ -174,11 +189,11 @@ def __init__(self, fragment_file, rec_typer=REC_TYPER['simple'], def _load_rec(self, fragment_file, rec_typer): """Loads receptor information.""" f = h5py.File(fragment_file, 'r') - + rec_coords = f['rec_coords'][()] rec_types = f['rec_types'][()] rec_lookup = f['rec_lookup'][()] - + r = range(len(rec_types)) if self.verbose: r = tqdm.tqdm(r, desc='Remap receptor atoms') @@ -194,7 +209,7 @@ def _load_rec(self, fragment_file, rec_typer): rec_mapping = {} for i in range(len(rec_lookup)): rec_mapping[rec_lookup[i][0].decode('ascii')] = i - + rec = { 'rec_coords': rec_coords, 'rec_types': rec_types, @@ -203,11 +218,11 @@ def _load_rec(self, fragment_file, rec_typer): 'rec_mapping': rec_mapping, 'rec_loaded': rec_loaded } - + f.close() - + return rec - + def _load_fragments(self, fragment_file, lig_typer): """Loads fragment information.""" f = h5py.File(fragment_file, 'r') @@ -227,12 +242,12 @@ def _load_fragments(self, fragment_file, lig_typer): # unpack frag data into separate structures frag_coords = frag_data[:,:3].astype(np.float32) frag_types = frag_data[:,3].astype(np.uint8) - + frag_remapped = np.zeros(len(frag_types), dtype=np.uint16) if not self._lazy_loading: for i in range(len(frag_types)): frag_remapped[i] = lig_typer.apply(frag_types[i]) - + frag_loaded = np.zeros(len(frag_lookup)).astype(np.bool) # find and save connection point @@ -244,7 +259,7 @@ def _load_fragments(self, fragment_file, lig_typer): for i in r: _,f_start,f_end,_,_ = frag_lookup[i] fdat = frag_data[f_start:f_end] - + found = False for j in range(len(fdat)): if fdat[j][3] == 0: @@ -253,7 +268,7 @@ def _load_fragments(self, fragment_file, lig_typer): break assert found, "missing fragment connection point at %d" % i - + frag = { 'frag_coords': frag_coords, # d_idx -> (x,y,z) 'frag_types': frag_types, # d_idx -> (type) @@ -267,7 +282,7 @@ def _load_fragments(self, fragment_file, lig_typer): 'frag_lig_idx': frag_lig_idx, 'frag_loaded': frag_loaded } - + f.close() return frag @@ -275,14 +290,14 @@ def _load_fragments(self, fragment_file, lig_typer): def _get_valid_examples(self, filter_rec, filter_smi, fdist_min, fdist_max, fmass_min, fmass_max, verbose): """Returns an array of valid fragment indexes. - + "Valid" in this context means the fragment belongs to a receptor in filter_rec and the fragment abides by the optional mass/distance constraints. """ # keep track of valid examples valid_mask = np.ones(self.frag['frag_lookup'].shape[0]).astype(np.bool) - + num_frags = self.frag['frag_lookup'].shape[0] # filter by receptor id @@ -298,7 +313,7 @@ def _get_valid_examples(self, filter_rec, filter_smi, fdist_min, fdist_max, fmas if rec in filter_rec: valid_rec[i] = 1 valid_mask *= valid_rec - + # filter by ligand smiles string if filter_smi is not None: valid_lig = np.zeros(num_frags, dtype=np.bool) @@ -318,10 +333,10 @@ def _get_valid_examples(self, filter_rec, filter_smi, fdist_min, fdist_max, fmas # filter by fragment distance if fdist_min is not None: valid_mask[self.frag['frag_dist'] < fdist_min] = 0 - + if fdist_max is not None: valid_mask[self.frag['frag_dist'] > fdist_max] = 0 - + # filter by fragment mass if fmass_min is not None: valid_mask[self.frag['frag_mass'] < fmass_min] = 0 @@ -337,10 +352,10 @@ def _get_valid_examples(self, filter_rec, filter_smi, fdist_min, fdist_max, fmas def __len__(self): """Returns the number of valid fragment examples.""" return self.valid_idx.shape[0] - + def __getitem__(self, idx): """Returns the Nth example. - + Returns a dict with: f_coords: fragment coordinates (Fx3) f_types: fragment layers (Fx1) @@ -354,23 +369,23 @@ def __getitem__(self, idx): # convert to fragment index frag_idx = self.valid_idx[idx] return self.get_raw(frag_idx) - + def get_raw(self, frag_idx): # lookup fragment rec_id, f_start, f_end, p_start, p_end = self.frag['frag_lookup'][frag_idx] smiles = self.frag['frag_smiles'][frag_idx].decode('ascii') conn = self.frag['frag_conn'][frag_idx] - + # lookup receptor rec_idx = self.rec['rec_mapping'][rec_id.decode('ascii')] _, r_start, r_end = self.rec['rec_lookup'][rec_idx] - + # fetch data # f_coords = self.frag['frag_coords'][f_start:f_end] # f_types = self.frag['frag_types'][f_start:f_end] p_coords = self.frag['frag_coords'][p_start:p_end] r_coords = self.rec['rec_coords'][r_start:r_end] - + if self._lazy_loading and self.frag['frag_loaded'][frag_idx] == 0: frag_types = self.frag['frag_types'] frag_remapped = self.frag['frag_remapped'] @@ -468,29 +483,29 @@ def __init__(self, fingerprint_file): def _load_fingerprints(self, fingerprint_file): """Loads fingerprint information.""" f = h5py.File(fingerprint_file, 'r') - + fingerprint_data = f['fingerprints'][()] fingerprint_smiles = f['smiles'][()] - + # create smiles->idx mapping fingerprint_mapping = {} for i in range(len(fingerprint_smiles)): sm = fingerprint_smiles[i].decode('ascii') fingerprint_mapping[sm] = i - + fingerprints = { 'fingerprint_data': fingerprint_data, 'fingerprint_mapping': fingerprint_mapping, 'fingerprint_smiles': fingerprint_smiles, } - + f.close() - + return fingerprints def for_smiles(self, smiles): """Return a Tensor of fingerprints for a list of smiles. - + Args: smiles: size N list of smiles strings (as str not bytes) """ diff --git a/leadopt/grid_util.py b/leadopt/grid_util.py index a683b5e..b74a5d5 100644 --- a/leadopt/grid_util.py +++ b/leadopt/grid_util.py @@ -1,3 +1,18 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + """ Contains code for gpu-accelerated grid generation. """ @@ -120,15 +135,15 @@ def gpu_gridify(grid, atom_num, atom_coords, atom_mask, layer_offset, # invisible atoms if mask == 0: continue - + # point radius squared r = point_radius r2 = point_radius * point_radius - + # quick cube bounds check if abs(fx-tx) > r2 or abs(fy-ty) > r2 or abs(fz-tz) > r2: continue - + # value to add to this gridpoint val = 0 @@ -147,7 +162,7 @@ def gpu_gridify(grid, atom_num, atom_coords, atom_mask, layer_offset, d2 = (fx-tx)**2 + (fy-ty)**2 + (fz-tz)**2 if d2 > r2: continue - + val = 1 elif point_type == 2: # POINT_TYPE.CUBE # solid cube fill @@ -290,7 +305,7 @@ def get_batch(data, batch_size=16, batch_set=None, width=48, res=0.5, rot = fixed_rot if rot is None: rot = rand_rot() - + if ignore_receptor: mol_gridify( cuda_grid, @@ -420,5 +435,5 @@ def get_raw_batch(r_coords, r_types, p_coords, p_types, rec_typer, lig_typer, point_type=point_type, acc_type=acc_type ) - + return torch_grid diff --git a/leadopt/infer.py b/leadopt/infer.py index a8ac924..876acf2 100644 --- a/leadopt/infer.py +++ b/leadopt/infer.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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 diff --git a/leadopt/metrics.py b/leadopt/metrics.py index 5acbeae..debdec0 100644 --- a/leadopt/metrics.py +++ b/leadopt/metrics.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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 torch import torch.nn as nn @@ -36,7 +50,7 @@ def broadcast_fn(fn, yp, yt): def average_position(fingerprints, fn, norm=True): """Returns the average ranking of the correct fragment relative to all possible fragments. - + Args: fingerprints: NxF tensor of fingerprint data fn: distance function to compare fingerprints @@ -54,7 +68,7 @@ def _average_position(yp, yt): # number of fragment that are closer or equal count = torch.sum((dist <= p_dist[i]).to(torch.float)) c[i] = count - + score = torch.mean(c) return score @@ -148,7 +162,7 @@ def _top_k_acc(yp, yt): for j in range(len(k)): c[i,j] = int(count < k[j]) - + score = torch.mean(c, 0) m = {'%sacc_%d' % (pre, h): v.item() for h,v in zip(k,score)} diff --git a/leadopt/model_conf.py b/leadopt/model_conf.py index 1ce35b1..cc2a4c4 100644 --- a/leadopt/model_conf.py +++ b/leadopt/model_conf.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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 import json @@ -59,7 +73,7 @@ def _direct_loss(fingerprints, fn): class RunLog(object): def __init__(self, args, models, wandb_project=None): """Initialize a run logger. - + Args: args: command line training arguments models: {name: model} mapping @@ -71,7 +85,7 @@ def __init__(self, args, models, wandb_project=None): project=wandb_project, config=args ) - + for m in models: wandb.watch(models[m]) @@ -131,7 +145,7 @@ def setup_base_parser(parser): @staticmethod def setup_parser(sub): """Adds arguments to a subparser. - + Args: sub: an argparse subparser """ @@ -147,7 +161,7 @@ def load(cls, path, device='cuda'): Call LeadoptModel.load to infer model type. Or call subclass.load to load a specific model type. - + Args: path: full path to saved model """ @@ -181,7 +195,7 @@ def __init__(self, args, device='cuda', with_log=True): def save(self, path): """Save model configuration to a path. - + Args: path: path to an existing directory to save models """ @@ -303,9 +317,9 @@ def load_data(self): # # partitions.TRAIN if not self._args['no_partitions'] else None), # filter_rec=set(get_bios(moad_partitions.TRAIN)), # filter_smi=set(moad_partitions.TRAIN_SMI), - # fdist_min=self._args['fdist_min'], - # fdist_max=self._args['fdist_max'], - # fmass_min=self._args['fmass_min'], + # fdist_min=self._args['fdist_min'], + # fdist_max=self._args['fdist_max'], + # fmass_min=self._args['fmass_min'], # fmass_max=self._args['fmass_max'], # verbose=True # ) @@ -318,9 +332,9 @@ def load_data(self): # # partitions.VAL if not self._args['no_partitions'] else None), # filter_rec=set(get_bios(moad_partitions.VAL)), # filter_smi=set(moad_partitions.VAL_SMI), - # fdist_min=self._args['fdist_min'], - # fdist_max=self._args['fdist_max'], - # fmass_min=self._args['fmass_min'], + # fdist_min=self._args['fdist_min'], + # fdist_max=self._args['fdist_max'], + # fmass_min=self._args['fmass_min'], # fmass_max=self._args['fmass_max'], # verbose=True # ) @@ -336,9 +350,9 @@ def load_data(self): dat, filter_rec=set(get_bios(moad_partitions.TRAIN)), filter_smi=set(moad_partitions.TRAIN_SMI), - fdist_min=self._args['fdist_min'], - fdist_max=self._args['fdist_max'], - fmass_min=self._args['fmass_min'], + fdist_min=self._args['fdist_min'], + fdist_max=self._args['fdist_max'], + fmass_min=self._args['fmass_min'], fmass_max=self._args['fmass_max'], ) @@ -346,9 +360,9 @@ def load_data(self): dat, filter_rec=set(get_bios(moad_partitions.VAL)), filter_smi=set(moad_partitions.VAL_SMI), - fdist_min=self._args['fdist_min'], - fdist_max=self._args['fdist_max'], - fmass_min=self._args['fmass_min'], + fdist_min=self._args['fdist_min'], + fdist_max=self._args['fdist_max'], + fmass_min=self._args['fmass_min'], fmass_max=self._args['fmass_max'], ) @@ -391,7 +405,7 @@ def train(self, save_path=None, custom_steps=None, checkpoint_callback=None, dat self._models['voxel'].parameters(), lr=self._args['learning_rate']) steps_per_epoch = len(train_dat) // self._args['batch_size'] steps_per_epoch = custom_steps if custom_steps is not None else steps_per_epoch - + # configure metrics dist_fn = DIST_FN[self._args['dist_fn']] @@ -505,9 +519,9 @@ def run_test(self, save_path, use_val=False): # filter_rec=partitions.TEST, filter_rec=set(get_bios(moad_partitions.VAL if use_val else moad_partitions.TEST)), filter_smi=set(moad_partitions.VAL_SMI if use_val else moad_partitions.TEST_SMI), - fdist_min=self._args['fdist_min'], - fdist_max=self._args['fdist_max'], - fmass_min=self._args['fmass_min'], + fdist_min=self._args['fdist_min'], + fdist_max=self._args['fdist_max'], + fmass_min=self._args['fmass_min'], fmass_max=self._args['fmass_max'], verbose=True ) @@ -523,7 +537,7 @@ def run_test(self, save_path, use_val=False): smiles = [test_dat[i]['smiles'] for i in range(len(test_dat))] correct_fp = fingerprints.for_smiles(smiles).numpy() - + # (example_idx, sample_idx) queries = [] for i in range(len(test_dat)): diff --git a/leadopt/models/__init__.py b/leadopt/models/__init__.py index e69de29..1c2961a 100644 --- a/leadopt/models/__init__.py +++ b/leadopt/models/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021 Jacob Durrant + +# 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. diff --git a/leadopt/models/backport.py b/leadopt/models/backport.py index 0d8079d..6a537b6 100644 --- a/leadopt/models/backport.py +++ b/leadopt/models/backport.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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. + from torch.nn import Module from typing import Tuple, Union diff --git a/leadopt/models/voxel.py b/leadopt/models/voxel.py index e5ed6b6..24aa3ae 100644 --- a/leadopt/models/voxel.py +++ b/leadopt/models/voxel.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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 torch import torch.nn as nn @@ -56,12 +70,12 @@ def __init__(self, in_channels, output_size, blocks=[32,64], fc=[2048], pad=True self.pred = nn.Sequential(*pred) self.norm = nn.Sigmoid() - + def forward(self, x): for b in self.blocks: x = b(x) x = self.reduce(x) x = self.pred(x) x = self.norm(x) - + return x diff --git a/leadopt/util.py b/leadopt/util.py index 676a28c..9c6fecc 100644 --- a/leadopt/util.py +++ b/leadopt/util.py @@ -1,3 +1,18 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + ''' rdkit/openbabel utility scripts ''' @@ -28,11 +43,11 @@ def combine_all(frags): """Combines a list of rdkit mols.""" if len(frags) == 0: return None - + c = frags[0] for f in frags[1:]: c = Chem.CombineMols(c,f) - + return c @@ -41,7 +56,7 @@ def generate_fragments(mol, max_heavy_atoms=0, only_single_bonds=True): Args: mol: The molecule to fragment. - max_heavy_atoms: The maximum number of heavy atoms to include + max_heavy_atoms: The maximum number of heavy atoms to include in generated fragments. nly_single_bonds: If set to true, this method will only return fragments generated by breaking single bonds. @@ -54,11 +69,11 @@ def generate_fragments(mol, max_heavy_atoms=0, only_single_bonds=True): # if we have multiple ligands already, split into pieces and then iterate ligands = Chem.GetMolFrags(mol, asMols=True, sanitizeFrags=False) - + for i in range(len(ligands)): lig = ligands[i] other = list(ligands[:i] + ligands[i+1:]) - + # iterate over bonds for i in range(lig.GetNumBonds()): # (optional) filter base on single bonds @@ -86,10 +101,10 @@ def generate_fragments(mol, max_heavy_atoms=0, only_single_bonds=True): # (optional) filter based on number of heavy atoms in the fragment if max_heavy_atoms > 0 and fragments[1].GetNumHeavyAtoms() > max_heavy_atoms: continue - + # if we have other ligands present, merge them with the parent parent = fragments[0] - + if len(other) > 0: parent = combine_all([parent] + other) @@ -101,25 +116,25 @@ def generate_fragments(mol, max_heavy_atoms=0, only_single_bonds=True): def load_ligand(sdf): """Loads a ligand from an sdf file and fragments it. - + Args: sdf: Path to sdf file containing a ligand. """ lig = next(Chem.SDMolSupplier(sdf, sanitize=False)) frags = generate_fragments(lig) - + return lig, frags def load_ligands_pdb(pdb): """Load multiple ligands from a pdb file. - + Args: pdb: Path to pdb file containing a ligand. """ lig_mult = Chem.MolFromPDBFile(pdb) ligands = Chem.GetMolFrags(lig_mult, asMols=True, sanitizeFrags=True) - + return ligands @@ -127,25 +142,25 @@ def remove_water(m): """Removes water molecules from an rdkit mol.""" parts = Chem.GetMolFrags(m, asMols=True, sanitizeFrags=False) valid = [k for k in parts if not Chem.MolToSmiles(k, allHsExplicit=True) == '[OH2]'] - + assert len(valid) > 0, 'error: molecule contains only water' - + merged = valid[0] for part in valid[1:]: merged = Chem.CombineMols(merged, part) - + return merged def load_receptor(rec_path): """Loads a receptor from a pdb file and retrieves atomic information. - + Args: rec_path: Path to a pdb file. """ rec = Chem.MolFromPDBFile(rec_path, sanitize=False) rec = remove_water(rec) - + return rec @@ -167,10 +182,10 @@ def conv_charge(x): for v in valid: coords += [k.coords for k in v.atoms] types += [( - k.atomicnum, - int(k.OBAtom.IsAromatic()), - int(k.OBAtom.IsHbondDonor()), - int(k.OBAtom.IsHbondAcceptor()), + k.atomicnum, + int(k.OBAtom.IsAromatic()), + int(k.OBAtom.IsHbondDonor()), + int(k.OBAtom.IsHbondAcceptor()), conv_charge(k.OBAtom.GetPartialCharge()) ) for k in v.atoms] @@ -181,7 +196,7 @@ def get_connection_point(frag): '''return the coordinates of the dummy atom as a numpy array [x,y,z]''' dummy_idx = get_types(frag).index(0) coords = get_coords(frag)[dummy_idx] - + return coords @@ -189,10 +204,10 @@ def frag_dist_to_receptor(rec, frag): '''compute the minimum distance between the fragment connection point any receptor atom''' rec_coords = rec.GetConformer().GetPositions() conn = get_connection_point(frag) - + dist = np.sum((rec_coords - conn) ** 2, axis=1) min_dist = np.sqrt(np.min(dist)) - + return min_dist @@ -200,10 +215,10 @@ def frag_dist_to_receptor_raw(coords, frag): '''compute the minimum distance between the fragment connection point any receptor atom''' rec_coords = np.array(coords) conn = get_connection_point(frag) - + dist = np.sum((rec_coords - conn) ** 2, axis=1) min_dist = np.sqrt(np.min(dist)) - + return min_dist @@ -243,11 +258,11 @@ def mol_to_points(mol, atom_types=[6,7,8,9,15,16,17,35,53]): types = get_types(mol) layers = np.array([(atom_types.index(k) if k in atom_types else -1) for k in types]) - + # filter by existing layer coords = coords[layers != -1] layers = layers[layers != -1].reshape(-1,1) - + return coords, layers @@ -277,8 +292,7 @@ def merge_smiles(sma, smb): e.AddBond(na,nb,Chem.rdchem.BondType.SINGLE) r = e.GetMol() - + sm = Chem.MolToSmiles(Chem.RemoveHs(r, sanitize=False), isomericSmiles=False) - - return sm + return sm diff --git a/scripts/make_fingerprints.py b/scripts/make_fingerprints.py index 0a5ae4e..116ab04 100644 --- a/scripts/make_fingerprints.py +++ b/scripts/make_fingerprints.py @@ -1,5 +1,20 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + ''' -utility script to generate fingerprints for smiles strings +Utility script to generate fingerprints for smiles strings ''' import argparse @@ -15,10 +30,10 @@ def fold_to(bv, size=2048): '''fold a SparseBitVec to a certain length''' fp = np.zeros(size) - + for b in list(bv.GetOnBits()): fp[b % size] = 1 - + return fp diff --git a/scripts/merge_moad.py b/scripts/merge_moad.py index 539251f..fe89800 100644 --- a/scripts/merge_moad.py +++ b/scripts/merge_moad.py @@ -1,3 +1,16 @@ +# Copyright 2021 Jacob Durrant + +# 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 argparse @@ -9,43 +22,43 @@ def unpack(path): f = h5py.File(path, 'r') - + dat = {} for k in f.keys(): dat[k] = f[k][()] - + f.close() - + return dat def append(dat, other): frag_coord_off = dat['frag_data'].shape[0] frag_lig_smi_off = dat['frag_lig_smi'].shape[0] rec_coord_off = dat['rec_coords'].shape[0] - + # Update fragment coords. other['frag_lookup']['f1'] += frag_coord_off other['frag_lookup']['f2'] += frag_coord_off other['frag_lookup']['f3'] += frag_coord_off other['frag_lookup']['f4'] += frag_coord_off - + # Update receptor coords. other['rec_lookup']['f1'] += rec_coord_off other['rec_lookup']['f2'] += rec_coord_off - + # Update ligand index. other['frag_lig_idx'] += frag_lig_smi_off - + # Concatenate everything. for k in dat: dat[k] = np.concatenate((dat[k], other[k]), axis=0) def cat_all(paths): dat = unpack(paths[0]) - + for i in range(1, len(paths)): append(dat, unpack(paths[i])) - + return dat def main(): diff --git a/scripts/moad_training_splits.py b/scripts/moad_training_splits.py index 9643419..1fc365e 100644 --- a/scripts/moad_training_splits.py +++ b/scripts/moad_training_splits.py @@ -1,3 +1,17 @@ +# Copyright 2021 Jacob Durrant + +# 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 argparse import os @@ -24,23 +38,23 @@ def do_split(families, pa=0.6): fa.append(f) else: fb.append(f) - + return (fa, fb) def split_smi(smi): l = list(smi) sz = len(l) - + np.random.shuffle(l) - + return (set(l[:sz//2]), set(l[sz//2:])) def split_smi3(smi): l = list(smi) sz = len(l) - + np.random.shuffle(l) - + v = sz//3 return (set(l[:v]), set(l[v:v*2]), set(l[v*2:])) @@ -117,7 +131,7 @@ def gen_split(csv, output, seed=7): out += 'TEST_SMI = ' + repr(test_s).replace(' ','') + '\n' open(output, 'w').write(out) - + def main(): parser = argparse.ArgumentParser() diff --git a/scripts/moad_util.py b/scripts/moad_util.py index e939d60..5cb68b2 100644 --- a/scripts/moad_util.py +++ b/scripts/moad_util.py @@ -1,25 +1,40 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + # MOAD csv parsing utility class Family(object): def __init__(self): self.targets = [] - + def __repr__(self): return 'F(%d)' % len(self.targets) - + class Protein(object): def __init__(self, pdb_id): # (chain, smi) self.pdb_id = pdb_id.upper() self.ligands = [] - + def __repr__(self): return '%s(%d)' % (self.pdb_id, len(self.ligands)) def parse_moad(csv): csv_dat = open(csv, 'r').read().strip().split('\n') csv_dat = [x.split(',') for x in csv_dat] - + families = [] curr_f = None @@ -47,10 +62,10 @@ def parse_moad(csv): if line[4] != 'valid': continue curr_t.ligands.append((line[3], line[9])) - + curr_f.targets.append(curr_t) families.append(curr_f) - + by_target = {} for f in families: for t in f.targets: diff --git a/scripts/process_moad.py b/scripts/process_moad.py index a2def99..3035cb1 100644 --- a/scripts/process_moad.py +++ b/scripts/process_moad.py @@ -1,3 +1,18 @@ +# Copyright 2021 Jacob Durrant + +# 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. + + ''' Utility script to convert the MOAD dataset into a packed format ''' @@ -24,7 +39,7 @@ # Data Format -# +# # Receptor data: # - rec_lookup: [id][start][end] # - rec_coords: [x][y][z] @@ -56,7 +71,7 @@ def basic_replace(sm): m = Chem.MolFromSmiles(sm, False) - + for a,b in REPLACE: m = Chem.ReplaceSubstructs( m, @@ -64,7 +79,7 @@ def basic_replace(sm): Chem.MolFromSmiles(b, sanitize=False), replaceAll=True )[0] - + return Chem.MolToSmiles(m) @@ -74,7 +89,7 @@ def neutralize_smiles(sm): m = t.canonicalize(m) sm = Chem.MolToSmiles(m) sm = basic_replace(sm) - + try: return molvs.standardize_smiles(sm) except: @@ -98,7 +113,7 @@ def load_example(base, rec_id, target): lig_off = 0 for lig in target.ligands: - + lig_path = os.path.join(base, '%s_%s.pdb' % (rec_id, lig[0].replace(' ','_'))) try: lig_mol = Chem.MolFromPDBFile(lig_path, True) @@ -119,22 +134,22 @@ def load_example(base, rec_id, target): for parent, frag in splits: frag_data = util.mol_array(frag) parent_data = util.mol_array(parent) - + frag_smi = Chem.MolToSmiles( - frag, - isomericSmiles=False, - kekuleSmiles=False, - canonical=True, + frag, + isomericSmiles=False, + kekuleSmiles=False, + canonical=True, allHsExplicit=False ) frag_smi = neutralize_smiles(frag_smi) - + frag.UpdatePropertyCache(strict=False) mass = ExactMolWt(frag) - + dist = util.frag_dist_to_receptor_raw(rec_coords, frag) - + fragments.append((frag_data, parent_data, frag_smi, mass, dist, lig_off)) lig_off += 1 @@ -184,14 +199,14 @@ def process(work, processed, moad_csv, out_path='moad.h5', num_cores=1): # Multiprocess. with multiprocessing.Pool(num_cores) as p: - with tqdm.tqdm(total=len(work)) as pbar: + with tqdm.tqdm(total=len(work)) as pbar: for w, res in p.imap_unordered(multi_load, work): pbar.update() if res == None: print('[!] Failed: %s' % w[1]) continue - + rcoords, rtypes, fragments, ex_lig_smiles = res if len(fragments) == 0: @@ -216,14 +231,14 @@ def process(work, processed, moad_csv, out_path='moad.h5', num_cores=1): frag_start = frag_i frag_end = frag_i + fdat.shape[0] frag_i += fdat.shape[0] - + parent_start = frag_i parent_end = frag_i + pdat.shape[0] frag_i += pdat.shape[0] - + frag_data.append(fdat) frag_data.append(pdat) - + frag_lookup.append((rec_id.encode('ascii'), frag_start, frag_end, parent_start, parent_end)) frag_lig_idx.append(lig_idx+lig_off) frag_smiles.append(frag_smi) @@ -232,7 +247,7 @@ def process(work, processed, moad_csv, out_path='moad.h5', num_cores=1): # Add ligand smiles. frag_lig_smi += ex_lig_smiles - + # Convert to numpy format. print('Convert numpy...', flush=True) n_rec_lookup = np.array(rec_lookup, dtype='