DECIMER V1.0 is now available, Please check our new repository DECIMER-Image_Transformer !!
The repository contains the network and the related scripts for encoder-decoder based Chemical Image Recognition
├── Network/ # Main model and evaluator scripts
+ ├ ─ Trainer_Image2Smiles.py # Main training script - further could be modified for training
+ ├ ─ I2S_Data.py # Data reader module for training
+ ├ ─ I2S_Model.py # Autoencoder network
+ ├ ─ Evaluate.py # To Load trained model and evaluate an image (Predicts SMILES)
+ └ ─ I2S_evalData.py # To load the tokenizer and the images for evaluation
+
├── Utils/ # Utilities used to generate the text data
+ ├ ─ Deepsmiles_Encoder.py # Used for encoding SMILES to DeepSMILES
+ ├ ─ Deepsmiles_Decoder.py # Used for decoding DeepSMILES to SMILES
+ ├ ─ Smilesto_selfies.py # Used for encoding SMILES to SELFIES
+ ├ ─ Smilesto_selfies.py # Used for encoding SELFIES to SMILES
+ └ ─ Tanimoto_Calculator_Rdkit.py # Calculates Tanimoto similarity on Original VS Predicted SMILES
+
├── LICENSE
├── Python_Requirements # Python requirements needed to run the scripts without error
└── README.md
- This can be done using pip, check the Tensorflow website for the installation guide. DECIMER can run on both CPU and GPU platforms. Installing Tensorflow-GPU should be done according to this guide.
- matplotlib
- sklearn
- pillow
- deepsmiles
- Directories can be easily specified inside the scripts.
- The path to the SMILES data is specified in I2S_Data.py
- The path to the image data is specified in Trainer_Image2Smiles.py
- The path to checkpoints will be generated in the same folder where your Trainer script is located, If you would like to use a different path it can be modified in Trainer_Image2Smiles.py.
├── Image2SMILES/
+ ├ ─ checkpoints/
+ ├ ─ Trainer_Image2Smiles.py
+ ├ ─ I2S_Data.py
+ ├ ─ I2S_Model.py
+ ├ ─ Evaluate.py
+ └ ─ I2S_evalData.py
+
├── Data/
+ ├ ─ Train_Images/
+ └ ─ DeepSMILES.txt
+
└── Predictions/
└ ─ Utils/
-
Generating image data:
- You can generate your images using SDF or SMILES. The DECIMER Java repository contains the scripts used to generate images that were used for training in our case. You simply have to clone the repository, get the CDK libraries, and use them as referenced libraries to compile the scripts you want to use.
e.g: javac -cp cdk-2.3.jar:. SmilesDepictor.java # Compiling the script on your local directory. java -cp cdk-2.3.jar:. SmilesDepictor # Run the compiled script.
- The generated images should be placed under /Image2SMILES/Data/Train_Images/
-
Generating Text Data:
- You should use the corresponding SDF or SMILES file to generate the text data. Here, the text data is DeepSMILES strings. The DeepSMILES can be generated using [Deepsmiles_Encoder.py] under Utils. Split the DeepSMILES strings appropriately after generating them.
- Place the DeepSMILES data under /Image2SMILES/Data/
- After specifying the "paths" to the data correctly. you can train the Image2SMILES network on a GPU enabled machine(CPU platform can be much slower for a big number of Images).
$ python3 Image2SMILES.py &> log.txt &- After the training is finished, you can use your images to test the model trained using the Evaluate.py. to generate a completely new set of test data, you can use the same steps as above mentioned to generate training data.
Note: Training the model yourself is straightforward, but for reference please check DECIMER V1.0 repository
- To use the trained model provided in the repository please follow these steps;
- Model also available here: Trained Model and should be placed under Trained_Models directory
- Clone the repository
git clone https://github.com/Kohulan/DECIMER-Image-to-SMILES.git - Change directory to Network folder
cd DECIMER-Image-to-SMILES/Network - Copy a sample image to the Network folder, check the path to the model inside Predictor.py and run
python3 Predictor.py --input sample.png
- Clone the repository
- This project is licensed under the MIT License - see the LICENSE file for details
- Use this BibTeX to cite
@article{Rajan2020,
abstract = {The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100 million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose.},
author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph},
doi = {10.1186/s13321-020-00469-w},
issn = {1758-2946},
journal = {Journal of Cheminformatics},
month = {dec},
number = {1},
pages = {65},
title = {{DECIMER: towards deep learning for chemical image recognition}},
url = {https://doi.org/10.1186/s13321-020-00469-w https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00469-w},
volume = {12},
year = {2020}
}
