RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Accepted to IJCAI 2021
Prepare dependencies
We use Pytorch for ML framework, RDKit for cheminformatics tool, and DGL for easy implementation of graph neural networks. We highly recommend to use Anaconda for managing package dependencies.
conda create -n RetCL python=3.7 conda activate RetCL conda install pytorch=1.5.0 cudatoolkit=10.1 -c pytorch conda install rdkit=2019.09 -c rdkit conda install dgl=0.4.3 dgllife=0.2.1 -c dglteam
Preprocessing data
python preprocess.py --dataset uspto_50k --datadir data/uspto_50k/ python preprocess.py --dataset uspto_50k --datadir data/uspto_50k_modified/ python preprocess.py --dataset uspto_candidates --datadir data/uspto_candidates/
You can download the USPTO-full dataset from the GLN repository.
Evaluation
We provide pretrained models in checkpoints/.
You can obtain the same results reported in Table 1 using the following scripts:
python evaluate.py --num-layers 5 --use-sum --best 50 --beam 50 --ckpt checkpoints/uspto50k_unknown.pth python evaluate.py --num-layers 5 --use-sum --best 50 --beam 50 --ckpt checkpoints/uspto50k_given.pth --use-label
To obtain the results in Table 2, replace --best 50 --beam 50 with --best 200 --beam 200.
For the generalization experiment, run the following script:
python evaluate.py --num-layers 5 --use-sum --best 50 --beam 50 --ckpt checkpoints/uspto50k_modified_unknown.pth --datadir data/uspto_50k_modified/ --classwise
Training
You can train our RetCL framework using train.py. For example, one can learn with 4 nearest neighbors and sum pooling using the following script:
python train.py --use-sum --num-neighbors 4 --logdir logs/uspto_50k_unknown_N4