EMMPTNet is an explainable multi-view, multi-scale deep learning network based on physicochemical and topological properties designed to predict the binding affinity between RNA and small molecules. The model combines the chemical and structural properties of RNA and small molecules to improve prediction accuracy and interpretability.

• Code: This section houses EMMPTNet's modeling framework and data construction methods.
• dataset: This section holds the final results of EMMPTNet's data construction.
• weights: This section holds the model weights trained by EMMPTNet.

In this study, we employed the following experimental setup.

• Cross validation: ‘python code/train.py‘

If you have any issues or questions about this paper or need assistance with reproducing the results, please contact me.

Zeyu Wu

School of Artificial Intelligence and Computer Science, Jiangnan University,

Email: wuzeyu@stu.jiangnan.edu.cn