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Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

Code for "Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits" (https://arxiv.org/abs/2006.15426)

Code was run/tested for:

- python 3.6
- pytorch 1.3.1
- tensorflow 2.0
- rdkit 2020.03.2

Pytorch is used for building, training and evaluating models. CUDA support is recommended.

Tensorflow is used only for visualizing training process (tensorboard). CUDA support is not required.

Environment setup

We recommend running MEGAN in an isolated conda environment, which can be created with:

conda env create -f env.yml

Edit env.sh file so it suits your configuration, if necessary. Before running any scripts, run:

source env.sh

This activates the conda environment and sets a few environment values.

Download training/evaluation data

For USPTO-50k, the data needs to be first manually downloaded from: https://www.dropbox.com/sh/6ideflxcakrak10/AAAESdZq7Y0aNGWQmqCEMlcza/typed_schneider50k and unpacked to the data/uspto_50k folder (Thanks to the authors of https://github.com/Hanjun-Dai/GLN for providing the data).

The following scripts download the datasets and generate train/val/test split:

python bin/acquire.py uspto_50k  # assumes that raw data is in data/uspto_50k
python bin/acquire.py uspto_mit
python bin/acquire.py uspto_full

Preprocessing training data

The following scripts build graph representation of data needed to train MEGAN:

python bin/featurize.py uspto_50k megan_16_bfs_randat
python bin/featurize.py uspto_mit megan_for_8_dfs_cano
python bin/featurize.py uspto_full megan_32_bfs_randat

Datasets and featurizers are defined in src/config.py.

By default, featurization is multithreaded with number of jobs equal to the number of CPUs. It can be changed by:

N_JOBS=N python bin/featurize.py uspto_full megan_32_bfs_randat

where N is an integer >= 1

Training

python bin/train.py uspto_50k models/uspto_50k
python bin/train.py uspto_50k_rt models/uspto_50k_rt
python bin/train.py uspto_mit models/uspto_mit_mix
python bin/train.py uspto_mit_sep models/uspto_mit_sep

This trains models with the same configuration as we describe in the paper.

We use gin-config (https://github.com/google/gin-config) for managing training hyperparameters. Gin configuration files are in configs. Configuration values can also be passed as script parameters like:

python bin/train.py uspto_50k models/uspto_50k --learning_rate 0.5 --n_encoder_conv 8

Training takes from about 10 hours for USPTO-50k to about 60 hours for USPTO-FULL on a single Nvidia GeForce GTX 1070 GPU.

Evaluation

python bin/eval.py models/uspto_50k --beam-size 50 --show-every 100
python bin/eval.py models/uspto_50k_rt --beam-size 50 --show-every 100
python bin/eval.py models/uspto_mit_mix --beam-size 10 --show-every 1000
python bin/eval.py models/uspto_mit_sep --beam-size 10 --show-every 1000
python bin/eval.py models/uspto_full --beam-size 50 --show-every 1000

For evaluation script we use argh, so _ in parameter names are replaced with -. Evaluation can take long time, especially for large beam sizes (up to a couple of hours for USPTO-FULL with beam size 50).

Evaluation produces two files: eval_*.txt has calculated Top K values, pred_*.txt contains predicted SMILES and actions.

Packed data and models

We include packed pre-processed data, as well as weights of the model trained on USPTO-50k for two variants (reaction type unknown/reaction type given) as a GitHub Release with version number v1.1 in this repo. To use data and pretrained models, unpack the "megan_data.zip" archive in the root directory of the project.