Small Molecule Autoencoders: Architecture Engineering to Optimize Latent Space Utility and Sustainability

Embedding you own SMILES or SELFIES with pre-trained models

We have added checkpoints of the the three best performing models ready for use:

• model-checkpoints/S2S_gru_a0_e-0_ls128_h128_l1_fullMOSES_b0-0_670487_50.ckpt: The Autoencoder with the best full reconstruction rate for SMILES.
• model-checkpoints/S2S_gru_a1_e-1_ls128_h128_l3_sub1-5x-can2enum_b0-0_26225_200.ckpt: The SMILES autoencoder trained with canonical SMILES as input and enumerated SMILES as targets. This model stood out due to its good latent space organisation.
• model-checkpoints/S2S_gru_a0_e-0_ls128_h128_l1_fullMOSES-selfies_b0-0_116739_50.ckpt: The Autoencoder with the best full reconstruction rate for SELFIES.

NOTE: All checkpoints of the models investigated in our paper including source code and scripts for testing are depsited on Zenodo.

evaluate reconstruction performance on the test set

For convenience reasons, test molecules and their reconstruction attempts for all models have been generated with /scripts/predict-all.py and are deposited in the 'test-recs' folder. These files can be used to compute the Full Reconstruction rates with /scripts/compute-full-reconstruction.py and the Mean Similarity, Levenshtein Distance and SeqMatcher Similarity with /scripts/compute-metrics.py.

evaluate the latent space

To evaluate the latent space of an existing checkpoint, run:

scripts/evaluate-latent-space.py -ckpt='S2S_gru_a1_e-0_ls128_h128_l3_sub1_b0-0_26225_200.ckpt'

PCA and histomgram will be returned as a PDF.

Note, that the checkpoints follow a naming convention that provides all necessary parameters to build the model that fits the checkpoint. For more information, see checkpoint naming convention below.

train new model

To train a new model, run:

scripts/train.py -n='S2S_gru_a0_e-0_ls8_h8_l1_sub1-selfies_b0-0' -bs=1024 -e=1 -g=4

With -n being the model name according to the naming convention (see below), -bs being the batch size, e the number of training epochs and -g the number of GPUs to use.

checkpoint naming convention

Explained on the example S2S_gru_a0_e-0_ls8_h8_l1_sub1-selfies_b0-0:

• S2S: string to string, do not change this part
• gru: alternatively lstm, indicated which RNN type to use
• a0: whether or not to use attention. a0 = no attention, a1 = with attention
• e-0: whether or not the input or output is enumerated (e-0 = no, e-1 = yes)
• ls8: the latent size (here 8)
• h8: the hidden size (here 8)
• l1: number of layers (here 1)
• sub1-selfies: the dataset. One of sub1-selfies (50k subset selfies), sub1(50k subset smiles), fullMOSES-selfies(full set selfies), fullMOSES (full set smiles)
• b0-0: Do not change.

Provided checkpoints further include the seed and the number of epochs.