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Official Implementation for "Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery".

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seq2seq-fingerprint

This code implements sequence to sequence fingerprint.

Installation requirements

  1. We right now depend on the tensorflow==1.4.1.
  2. smile is required(for Ubuntu OS, pip install smile).
  3. ZINC is used for our experiments, which is a free database of commercially-available compounds for virtual screening. You can download ZINC datasets from http://zinc.docking.org/

References:

If our work is helpful for your research, please consider citing:

@article{xu2017seq2seqfingerprint,
  title={Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery},
  author={Zheng Xu, Sheng Wang, Feiyun Zhu, and Junzhou Huang},
  journal={BCB’17, Aug 2017, Boston, Massachusetts USA},
  year={2017}
}

Input and output files:

Path name Path Discription
smi_path /data/zinc/zinc.smi - input smile data for building vocab
vocab_path ~/expr/seq2seq-fp/pretrain/zinc.vocab - directory to save vocabulary
out_path ~/expr/seq2seq-fp/pretrain/zinc.tokens - directory to save tokens
tmp_path ~/expr/seq2seq-fp/pretrain/zinc.tmp - directory to save temporary data

Running workflow:

1. Prepare data

a) Build vocabulary

Use the build_vocab switch to turn on building vocabulary functionality.

python data.py --build_vocab 1 --smi_path /data/zinc/zinc.smi --vocab_path ~/expr/seq2seq-fp/pretrain/zinc.vocab --out_path ~/expr/seq2seq-fp/pretrain/zinc.tokens --tmp_path ~/expr/seq2seq-fp/pretrain/zinc.tmp

Example Output:

Creating temp file...
Building vocabulary...
Creating vocabulary /home/username/expr/test/pretrain/zinc.vocab from data /tmp/tmpcYVqV0
  processing line 100000
  processing line 200000
  processing line 300000
Translating vocabulary to tokens...
Tokenizing data in /tmp/tmpcYVqV0
  tokenizing line 100000
  tokenizing line 200000
  tokenizing line 300000

b) If vocabulary already exsits (for test data)

Translate the SMI file using existing vocabulary Switch off build_vocab option, or simply hide it from the command line. (note: zinc.smi is used for training, zinc_test.smi is used for evaluating)

python data.py --smi_path /data/zinc/zinc_test.smi --vocab_path ~/expr/seq2seq-fp/pretrain/zinc.vocab --out_path ~/expr/seq2seq-fp/pretrain/zinc_test.tokens --tmp_path ~/expr/seq2seq-fp/pretrain/zinc_test.tmp

Example Output:

Creating temp file...
Reading vocabulary...
Translating vocabulary to tokens...
Tokenizing data in /tmp/tmpmP8R_P

2. Train

a) Build model(model.json)

python train.py build ~/expr/test/gru-2-256/

model.json example

{"dropout_rate": 0.5, "learning_rate_decay_factor": 0.99, "buckets": [[30, 30], [60, 60], [90, 90]],"target_vocab_size": 41, "batch_size": 5, "source_vocab_size": 41, "num_layers": 2, "max_gradient_norm": 5.0, "learning_rate": 0.5, "size": 128}

b) Train model

python train.py train ~/expr/test/gru-2-256/ ~/expr/seq2seq-fp/pretrain/zinc.tokens ~/expr/seq2seq-fp/pretrain/zinc_test.tokens --batch_size 64

Example Output:

global step 145600 learning rate 0.1200 step-time 0.314016 perplexity 1.000712
  eval: bucket 0 perplexity 1.001985
  eval: bucket 1 perplexity 1.002438
  eval: bucket 2 perplexity 1.000976
  eval: bucket 3 perplexity 1.002733
global step 145800 learning rate 0.1200 step-time 0.265477 perplexity 1.001033
  eval: bucket 0 perplexity 1.003763
  eval: bucket 1 perplexity 1.001052
  eval: bucket 2 perplexity 1.000259
  eval: bucket 3 perplexity 1.001401

Train from scratch

python train.py train ~/expr/unsup-seq2seq/models/gru-2-128/ ~/expr/unsup-seq2seq/data/zinc.tokens ~/expr/unsup-seq2seq/data/zinc_test.tokens --batch_size 256
python train.py train ~/expr/unsup-seq2seq/models/gru-3-128/ ~/expr/unsup-seq2seq/data/zinc.tokens ~/expr/unsup-seq2seq/data/zinc_test.tokens --batch_size 256
python train.py train ~/expr/unsup-seq2seq/models/gru-2-256/ ~/expr/unsup-seq2seq/data/zinc.tokens ~/expr/unsup-seq2seq/data/zinc_test.tokens --batch_size 256 --summary_dir ~/expr/unsup-seq2seq/models/gru-2-256/summary/

Example output

global step 200 learning rate 0.5000 step-time 0.317007 perplexity 7.833510
  eval: bucket 0 perplexity 32.720735
  eval: bucket 1 perplexity 24.253715
  eval: bucket 2 perplexity 16.619440
  eval: bucket 3 perplexity 13.854121
global step 400 learning rate 0.5000 step-time 0.259872 perplexity 6.460571
  eval: bucket 0 perplexity 31.408722
  eval: bucket 1 perplexity 22.750650
  eval: bucket 2 perplexity 15.665839
  eval: bucket 3 perplexity 12.682373

3. Decode

(note: model.json and weights in the subdirectory of ~/expr/test/gru-2-256/ are necessary to run decode)

python decode.py sample ~/expr/test/gru-2-256/  ~/expr/seq2seq-fp/pretrain/zinc.vocab ~/expr/seq2seq-fp/pretrain/zinc_test.tmp --sample_size 500

Example output:

Loading seq2seq model definition from /home/zhengxu/expr/test/gru-4-256/model.json...
Loading model weights from checkpoint_dir: /home/zhengxu/expr/test/gru-4-256/weights/
: CC(OC1=CC=C2/C3=C(/CCCC3)C(=O)OC2=C1C)C(=O)N[C@@H](CC4=CC=CC=C4)C(O)=O
> CC(OC1=CC=C2/C3=C(/CCCC3)C(=O)OC2=C1C)C(=O)N[C@@H](CC4=CC=CC=C4)C(O)=O

: CSC1=CC=C(CN(C)CC(=O)NC2=C(F)C=CC=C2F)C=C1
> CSC1=CC=C(CN(C)CC(=O)NC2=C(F)C=CC=C2F)C=C1

: CC1\C=C(\C)CC2C1C(=O)N(CCC[N+](C)(C)C)C2=O
> CC1\C=C(\C)CC2C1C(=O)N(CCC[N+](C)(C)C)C2=O

: CCC(=O)C1=CC(F)=C(C=C1)N2CCN(CC2)C(=O)C3=CC=C(F)C=C3
> CCC(=O)C1=CC(F)=C(C=C1)N2CCN(CC2)C(=O)C3=CC=C(F)C=C3

: CC1=CC(=CC=C1)C(=O)NC2=CC(=CC=C2)C(=O)N3CCOCC3
> CC1=CC(=CC=C1)C(=O)NC2=CC(=CC=C2)C(=O)N3CCOCC3

All FP

Generate all fingerprint

python decode.py fp ~/expr/test/gru-2-256/ ~/expr/seq2seq-fp/pretrain/zinc.vocab ~/expr/seq2seq-fp/pretrain/zinc_test.tmp ~/expr/test_2.fp

Example Output:

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Exact match count: 9665/10000

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Official Implementation for "Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery".

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