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Molecule-RNN

Molecule-RNN is a recurrent neural network built with Pytorch to generate molecules for drug discovery.

Tokenization of SMILES

There are different ways to tokenize SMILES, 3 of them are implemented in this project:

  1. Character-level tokenization, which is a naive way to tokenize SMILES. In this scheme, every character is treated as a single token expect those two-charater elements such Al and Br.
  2. Regular expression-based tokenization. In this scheme, each pair of square bracket [*] is also treated as a single token.
  3. SELFIES tokenization. SELFIES stands for Self-Referencing Embedded Strings, it is a 100% robust molecular string representation. See details here.

Dataset

The chembl28 dataset is used. It is under ./dataset

Training

  1. Set the out_dir in train.yaml as the directory where you want to store output results.
  2. Set which_vocab and vocab_path in train.yaml to specify which tokenization scheme to use. The pre-computed vocabularies are at ./vocab.
  3. Twick other hyper-paramters in train.yaml if you like (the default setting is working).
  4. Run the training script.
python train.py

Sampling

The trained model will be saved in the out_dir directory. We can generate molecules by sampling the trained model according to the output distribution. If the -result_dir is not specified, the out_dir in train.yaml will be used.

python sample.py -result_dir your_output_dir

The default setting yields over 80% valid rate for character-level tokenization and regex-based tokenization, and it gives 99.9% valid rate for SELFIES tokenization. After the sampling, we can filter out the invalid SMILES:

python filter_sampled.py -result_dir your_output_dir

Here are examples of some sampled molecules:

TODOs

Currently beam search sampling is not supported given the lenghts of the sequences. Feel free to make a PR or write an issue if you have any idea to search for molecules with high probabilities. :)