Name
Abstract | ||
|---|---|---|
Prediction the interactions between proteins (targets) and small molecules (ligands) is a critical task for the drug discovery in silico. In this work, we consider the target binding site instead of the whole target and propose a pairwise input neural network (PINN) for constructing the site-ligand interaction prediction model. Different with the ordinary artificial neural network (ANN) with one vector as input, the proposed PINN can accept a pair of vectors as the input, corresponding to a binding site and a ligand respectively. The 5-CV evaluation results show that PINN outperforms other representative target-ligand interaction prediction methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BIBM.2014.6999129 | BIBM |
Keywords | Field | DocType |
drug discovery in silico,ordinary artificial neural network,pairwise input neural network,small molecules,vector pair,protein,target-ligand interaction prediction methods,proteins,whole target,molecular biophysics,pinn,site-ligand interaction prediction model,target binding site,ann,medical computing,neural nets,drugs,neural networks,kernel,predictive models,bioinformatics,vectors,chemicals | Kernel (linear algebra),Pairwise comparison,Drug discovery,Binding site,Computer science,Ligand,Small molecule,Artificial intelligence,Bioinformatics,Artificial neural network,Machine learning,In silico | Conference |
ISSN | Citations | PageRank |
2156-1125 | 2 | 0.35 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caihua Wang | 1 | 7 | 1.45 |
| Juan Liu | 2 | 1128 | 145.32 |
| Fei Luo | 3 | 2 | 0.35 |
| Yafang Tan | 4 | 2 | 0.35 |
| Zixin Deng | 5 | 2 | 0.35 |
| Qian-Nan Hu | 6 | 2 | 0.35 |