| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T225 |
0-4 |
Sentence |
denotes |
2.8. |
| T226 |
6-29 |
Sentence |
denotes |
Swiss Target Prediction |
| T227 |
30-182 |
Sentence |
denotes |
Computational approaches are key players in narrowing down the dataset of potential drug targets and suggesting alternative targets for known molecules. |
| T228 |
183-361 |
Sentence |
denotes |
Molecular insight of the bioactive molecules and their mode of actions are important for understanding the observed phenotypes, prediction and optimization of existing compounds. |
| T229 |
362-599 |
Sentence |
denotes |
Swiss Target Prediction (http://www.swisstargetprediction.ch.) is an online web-based interface which helps in finding bioactive molecules having similar configuration with related or similar biochemical targets (Campillos et al., 2008). |
| T230 |
600-709 |
Sentence |
denotes |
The primary goal of the tool is to identify biochemical targets of molecules which are known to be bioactive. |
| T231 |
710-1123 |
Sentence |
denotes |
In the present study, Swiss Target Prediction online server was used for predicting the percentage proportion activity of each selected WS phytoconstituent with known intracellular targets like kinases, nuclear receptors, transcription factors, phosphodiesterases, oxidoreductases, cytochrome P450, voltage gated-ion channels, hydrolases, phosphatases, G-protein coupled receptors and primary active transporters. |
