Chemical molecular structural formula can express chemical knowledge more directly and conveniently, so it plays an important role in academic communication. Hand-drawing chemical molecular structural formulas is a routine task for students and researchers in the field of chemistry. If hand-drawn chemical molecular structural formulas can be converted into machine-readable data, computers can process and analyze these chemical molecular structural formulas, so Greatly improve the efficiency of chemical research.
The main research methods of this study are as follows:
- In terms of Dataset, several chemical molecular structural formulas are drawn by hand. In addition, this article uses the modified source code RDKit to convert SMILES codes into chemical molecular structural formula images, using random bond lengths, random rotation and other operations , to simulate the hand-painted chemical molecular structure image, and then combine image enhancement, degradation and other methods to further process the image to obtain a large number of synthetic images. Also, img to img task was done using diffusion.
- In terms of model, use the PyTorch deep learning framework to build the model of the encoder and decoder architecture, and use variant networks such as convolutional neural networks as encoders to convert the features of hand-drawn chemical molecular structure images into fixed The encoded state of the shape is then fed into the decoder to obtain a machine-readable encoding.
The dependencies required for the hdr environment of this project
conda env create -f environment.yaml
conda activate hdr
The experiment done by pipline_stages is a comparative test of synthetic image method
The experiment done by size_test is a comparison test of the number of synthetic images
The experiment done by SD_HD is a synthetic image: real hand-painted image scale experiment
If you need to obtain the complete dataset, please contact the corresponding author by email:weiliu@hnucm.edu.cn