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Template Relevance Network for Retrosynthesis

A faithful (to the best of my ability) re-implementation (because the authors did not release the source code) of the expansion network in Segler's seminal Nature paper: "Planning chemical syntheses with deep neural networks and symbolic AI", which they first published here in "Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction". In essence, NeuralSym is a feedforward network using Highway-ELU blocks, taking as input a product fingerprint (ECFP4), and producing as output an array of logits, one value for each template extracted from the training dataset. A softmax classification can be done across these logits to determine the most promising reaction template, which can be passed through RDChiral to generate the predicted precursors for this product.

Data files

This model has been re-trained on USPTO-50K dataset, which has ~50,000 atom-mapped reactions from US Patent data. I have done some pre-processing on the original data from Schneider et al. to get slightly cleaner train/valid/test datasets, which have 39713/4989/5005 reaction SMILES respectively. These are in the 3 .pickle files in data/. From these 3 files, just do

    python prepare_data.py

to extract templates from training data, get the labels & so on. I have significantly optimized the code & it automatically parallelizes, so it shouldn't take long to prepare all the data on a standard 8/16-core machine, maybe 15 mins total (slowest step is the variance thresholding of 1 mil-dim fingerprints to 32681-dim fingerprints)

Training

I have provided a sample train.sh file with sensible hyperparameters that achieved ~40% top-1 valid/test in terms of template-matching accuracy. For reactant matching accuracy (this is what we ultimately care about), I can get ~45.5% top-1 and 87.4% top-50 accuracy, calculated using infer_all.py. Just do

    bash -i train.sh

On 1x RTX2080 one epoch takes 8 seconds, and the whole training finishes in <5 minutes. The training arguments can be found in train.py & should be self-explanatory. I plan to do a quick bayesian optimization using Optuna, but don't expect any fancy improvements (<1% probably).

A fully trained model that scored 40.28% top-1 & 75.1% top-50 on the test set is uploaded on google drive here and you need to save it into the checkpoint/ folder. The logs from training that model are also included in logs_sample/. Note that it is quite sizeable, ~374M, so I don't recommend checkpointing models until you're sure that you have what you need.

I have also incorporated some neat visualization code in the validation/testing step to print some examples of reactions & the model's predictions, e.g.:

curr product:                       COC(=O)c1cccc2[nH]c(NCC3CCNCC3)nc12
pred template:                      [C:2]-[N&H1&D2&+0:1]-[C:3]>>C-C(-C)(-C)-O-C(=O)-[N&H0&D3&+0:1](-[C:2])-[C:3]
true template:                      [C:2]-[N&H1&D2&+0:1]-[C:3]>>C-C(-C)(-C)-O-C(=O)-[N&H0&D3&+0:1](-[C:2])-[C:3]
pred precursor (score = +0.9984):   ['COC(=O)c1cccc2[nH]c(NCC3CCN(C(=O)OC(C)(C)C)CC3)nc12']
true precursor (score = +0.9984):   COC(=O)c1cccc2[nH]c(NCC3CCN(C(=O)OC(C)(C)C)CC3)nc12

curr product:                       COc1ccc(I)c(OCC2CO2)c1
pred template:                      [#8:3]-[C:2]-[C&H2&D2&+0:1]-[O&H0&D2&+0:4]-[c:5]>>Cl-[C&H2&D2&+0:1]-[C:2]-[#8:3].[O&H1&D1&+0:4]-[c:5]
true template:                      [#8:3]-[C:2]-[C&H2&D2&+0:1]-[O&H0&D2&+0:4]-[c:5]>>Cl-[C&H2&D2&+0:1]-[C:2]-[#8:3].[O&H1&D1&+0:4]-[c:5]
pred precursor (score = +0.9820):   ['COc1ccc(I)c(O)c1.ClCC1CO1']
true precursor (score = +0.9820):   COc1ccc(I)c(O)c1.ClCC1CO1

Inference

For my own purposes, I am first working on generating up to top-200 precursors across the entire train/valid/test datasets. This is in infer_all.py.

But I have also done a simple API in infer_one.py that accepts a list of product SMILES and outputs a dictionary of top-K precursors & corresponding probabilities assigned by the model. This is just a sample to demonstrate the functionality - feel free to adapt as you wish!

Example:

{'COC(=O)c1cccc2[nH]c(NCC3CCNCC3)nc12': 
    [
        (['COC(=O)c1cccc2[nH]c(NCC3CCN(C(=O)OC(C)(C)C)CC3)nc12'], 0.9992377758026123), 
        (['COC(=O)c1cccc2[nH]c(NCC3CCN(C(=O)OCc4ccccc4)CC3)nc12'], 0.0002514408261049539), 
        (['COC(=O)c1cccc2[nH]c(NCC3CCN(C(=O)C(F)(F)F)CC3)nc12'], 0.00024452427169308066), 
        (['COC(=O)c1cccc2[nH]c(NCC3CCN(Cc4ccccc4)CC3)nc12'], 0.00012763732229359448), 
        (['COC(=O)c1cccc2[nH]c(NCc3ccncc3)nc12'], 4.4018081098329276e-05)
    ]}

Next in the pipeline: build a sample webapp that allows user to draw molecules & feed it into the infer_one API, followed by visualization of the proposed reactants & their probabilities.

Requirements & Setup instructions

RDKit, RDChiral & PyTorch are the main packages. Tested on Python 3.6

    TORCH_VER=1.6.0
    CUDA_VER=10.1
    CUDA_CODE=cu101

    # ensure conda is initialized first
    conda create -n neuralsym python=3.6 tqdm pathlib typing scipy pandas joblib -y
    conda activate neuralsym

    conda install -y pytorch=${TORCH_VER} torchvision cudatoolkit=${CUDA_VER} torchtext -c pytorch
    conda install -y rdkit -c rdkit

    pip install -e "git://github.com/connorcoley/rdchiral.git#egg=rdchiral"

Citation

Please cite Segler et al.'s original paper

@article{segler2017neural,
  title={Neural-symbolic machine learning for retrosynthesis and reaction prediction},
  author={Segler, Marwin HS and Waller, Mark P},
  journal={Chemistry--A European Journal},
  volume={23},
  number={25},
  pages={5966--5971},
  year={2017},
  publisher={Wiley Online Library}
}