chytorch-synplan 1.68

Library for modeling molecules and reactions in torch way

Chytorch [kʌɪtɔːrtʃ]

Library for modeling molecules and reactions in torch way.

Installation

Use pip install chytorch to install release version.

Or pip install . in source code directory to install DEV version.

Pretrained models

Chytorch main package doesn't include models zoo. Each model has its own named package and can be installed separately. Installed models can be imported as from chytorch.zoo.<model_name> import Model.

Usage

chytorch.nn.MoleculeEncoder - core graphormer layer for molecules encoding. API is combination of torch.nn.TransformerEncoderLayer with torch.nn.TransformerEncoder.

Batch preparation:

chytorch.utils.data.MoleculeDataset - Map-like on-the-fly dataset generators for molecules. Supported chython.MoleculeContainer objects, and PaCh structures.

chytorch.utils.data.collate_molecules - collate function for torch.utils.data.DataLoader.

Note: torch DataLoader automatically do proper collation since 1.13 release.

Example:

from chytorch.utils.data import MoleculeDataset, SMILESDataset
from torch.utils.data import DataLoader

data = ['CCO', 'CC=O']
ds = MoleculeDataset(SMILESDataset(data, cache={}))
dl = DataLoader(ds, batch_size=10)

Forward call:

Molecules coded as tensors of:

• atoms numbers shifted by 2 (e.g. hydrogen = 3). 0 - reserved for padding, 1 - reserved for CLS token, 2 - extra reservation.

• neighbors count, including implicit hydrogens shifted by 2 (e.g. CO = CH3OH = [6, 4]). 0 - reserved for padding, 1 - extra reservation, 2 - no-neighbors, 3 - one neighbor.

• topological distances' matrix shifted by 2 with upper limit. 0 - reserved for padding, 1 - reserved for not-connected graph components coding, 2 - self-loop, 3 - connected atoms.

  from chytorch.nn import MoleculeEncoder

  encoder = MoleculeEncoder() for b in dl: encoder(b)

Combine molecules and labels:

chytorch.utils.data.chained_collate - helper for combining different data parts. Useful for tricky input.

from torch import stack
from torch.utils.data import DataLoader, TensorDataset
from chytorch.utils.data import chained_collate, collate_molecules, MoleculeDataset

dl = DataLoader(TensorDataset(MoleculeDataset(molecules_list), properties_tensor),
    collate_fn=chained_collate(collate_molecules, stack))

Voting NN with single hidden layer:

chytorch.nn.VotingClassifier, chytorch.nn.BinaryVotingClassifier and chytorch.nn.VotingRegressor - speed optimized multiple heads for ensemble predictions.

Helper Modules:

chytorch.nn.Slicer - do tensor slicing. Useful for transformer's CLS token extraction in torch.nn.Sequence.

Data Wrappers:

In chytorch.utils.data module stored different data wrappers for simplifying ML workflows. All wrappers have torch.utils.data.Dataset interface.

• SizedList - list wrapper with size() method. Useful with torch.utils.data.TensorDataset.
• SMILESDataset - on-the-fly smiles to chython.MoleculeContainer or chython.ReactionContainer parser.
• LMDBMapper - LMDB KV storage to dataset mapper.
• TensorUnpack, StructUnpack, PickleUnpack - bytes to tensor/object unpackers

Publications

1 Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task