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 and chytorch.nn.ReactionEncoder - core graphormer layers for molecules and reactions. API is combination of torch.nn.TransformerEncoderLayer with torch.nn.TransformerEncoder.

Batch preparation:

chytorch.utils.data.MoleculeDataset and chytorch.utils.data.ReactionDataset - Map-like on-the-fly dataset generators for molecules and reactions. Supported chython.MoleculeContainer and chython.ReactionContainer objects, and bytes-packed forms.

chytorch.utils.data.collate_molecules and chytorch.utils.data.collate_reactions - collate functions for torch.utils.data.DataLoader.

Example:

from chython import SMILESRead
from chytorch.utils.data import collate_molecules, MoleculeDataset
from torch.utils.data import DataLoader

data = []
for r in SMILESRead('data.smi'):
    r.canonicalize()  # fix aromaticity and functional groups
    data.append(r)

ds = MoleculeDataset(data)
dl = DataLoader(ds, collate_fn=collate_molecules, 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 - for MLM task.
• neighbors count, including implicit hydrogens shifted by 2 (e.g. CO = CH3OH = [6, 4]). 0 - reserved for padding, 1 - for MLM task, 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.

Reactions coded in similar way. Molecules atoms and neighbors matrices just stacked. Distance matrices stacked on diagonal. Reactions include additional tensor with reaction role codes for each token. 0 - padding, 1 - reaction CLS, 2 - reactants, 3 - products.

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.

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, torch.stack))

Scheduler:

chytorch.optim.lr_scheduler.WarmUpCosine - Linear warmup followed with cosine-function for 0-pi range rescaled to lr_rate - decrease_coef * lr_rate interval.

Voting NN with single hidden layer:

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

Caching:

chytorch.utils.cache.SequencedFileCache, chytorch.utils.cache.SequencedDBCache, chytorch.utils.cache.SequencedDtypeCompressedCache, chytorch.utils.cache.CycleDataLoader - helpers for caching slow dataset generators output.