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TFRegNCI

Introduction

TFRegNCI is a tool for predicting Non-covalent interaction(NCI). This program package includes NCI prediction models, 2D (TFRegNCI) and 3D (TFRegNCI_3D), and a visualization module, Grad RAM, for feature visual analyses. The corresponding codes for utility are as follows.

Programs and Files:

1.TFRegNCI: A 2D multimodal correction model.

  • TFRegNCI.py: main running file.
  • TFRegNCI_model.py: 2D TFRegNCI model.
  • util: Transformer utiliy package.

2.TFRegNCI-3D: A 3D Interpretable correction multimodal model.

  • TFRegNCI_3D_grad_ram.py: main running file.
  • TFRegNCI_3D_model.py: 3D TFRegNCI model.
  • grad_ram: Grad RAM implementation package.
  • util: Transformer utiliy package.

3.Model parameters: The parameter file for TFRegNCI and TFRegNCI-3D.

  • TFRegNCI_para.pkl: TFRegNCI parameters
  • TFRegNCI-3D_para.pkl: TFRegNCI-3D parameters

Environment

  • Python: 3.7
  • Platform: pytorch 1.10

Citation

If you think the method useful in your research, please consider citing:

@article{wang2023tfregnci,
 title={TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion},
 author={Wang, Donghan and Li, Wenze and Dong, Xu and Li, Hongzhi and Hu, LiHong},
 journal={Journal of Chemical Information and Modeling},
 year={2023},
 publisher={ACS Publications}
}