Name
Abstract | ||
|---|---|---|
Predicting target-ligand interactions is a critical task of chemogenomics and plays a key role in virtual drug discovery. Moreover, it is important to take insights into the molecular recognition mechanisms between chemical substructures of ligands and binding sites of targets. In this work, we suppose the interaction between a ligand and a target is the result of the comprehensive effect of multiple fields between the ligand and the binding site of the target, and propose a multi-field interaction model (MFIM) to predict the target-ligand interaction. The evaluation result on the same data set shows that MFIM outperforms other two representative methods. The derived fragment interaction network is robust to the parameter fluctuation and the connections in the network are sparse. Moreover, the edge weights of the network might reflect the fragment interaction intensity and most of the significant edges are chemical interpretable. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2016.03.079 | Neurocomputing |
Keywords | Field | DocType |
Protein representation,Ligand representation,Field interaction,Target–ligand interaction,Target–ligand network analysis | Drug discovery,Search engine,Binding site,Pattern recognition,Chemical substance,Molecular recognition,Interaction model,Interaction network,Artificial intelligence,Chemogenomics,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
206 | C | 0925-2312 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caihua Wang | 1 | 7 | 1.45 |
| Juan Liu | 2 | 1128 | 145.32 |
| Fei Luo | 3 | 40 | 5.71 |
| Qian-Nan Hu | 4 | 26 | 4.80 |