Pairwise input neural network for target-ligand interaction prediction

By Caihua Wang, Juan Liu, Fei Luo, Yafang Tan, Z. Deng, Qian-Nan Hu. 2014

34 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Zhiyong Lu
2. Fei Wang
3. C. Gilvary
4. Olivier Elemento
5. Jiayu Zhou
6. Mengying Sun
7. Sendong Zhao
8. Brett K. Beaulieu-Jones

In Molecular Biology

1. Fei Wang
2. Casey S. Greene
3. Yifan Peng
4. Brett K. Beaulieu-Jones
5. G. Way
6. Amr M. Alexandari
7. Zhiyong Lu
8. Avanti Shrikumar

In Pharmacology

1. Jiayu Zhou
2. Fei Wang
3. Jinbo Xu
4. S. Joshua Swamidass

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[291] devised a pairwise input neural network with two hidden layers that takes two inputs, a drug and a target binding site, and predicts whether they interact.”
• Graph convolutional networks for computational drug development and discovery (2019, methodology): “Several commonly used deep architectures have been employed in drug-related works [4–7] and achieved substantial improvements compared to traditional machine learning methods.”