Name
Title | ||
|---|---|---|
Neuro-fuzzy Prediction of Biological Activity and Rule Extraction for HIV-1 Protease Inhibitors |
Abstract | ||
|---|---|---|
A fuzzy neural network (FNN) and multiple linear regression (MLR) were used to predict biological activities of 26 newly designed HIV-1 protease potential inhibitory compounds. Molecular descriptors of 151 known inhibitors were used to train and test the FNN and to develop MLR models. The predictive ability of these two models was investigated and compared. We found the predictive ability of the FNN to be generally superior to that of MLR. The fuzzy IF/THEN rules were extracted from the trained network. These rules map chemical structure descriptors to predicted inhibitory values. The obtained rules can be used to analyze the influence of descriptors. Our results indicate that FNN and fuzzy IF/THEN rules are powerful modeling tools for QSAR studies. |
Year | DOI | Keywords |
|---|---|---|
2005 | 10.1109/CIBCB.2005.1594906 | neural networks,multiple linear regression,predictive models,databases,chemistry,neuro fuzzy,biological activity,chemical structure,molecular descriptor,fuzzy neural network |
Field | DocType | ISBN |
Molecular descriptor,Quantitative structure–activity relationship,Neuro-fuzzy,Computer science,Fuzzy logic,HIV-1 protease,Artificial intelligence,Artificial neural network,Machine learning,Linear regression | Conference | 0-7803-9387-2 |
Citations | PageRank | References |
7 | 0.55 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Razvan Andonie | 1 | 117 | 17.71 |
| Levente Fabry-asztalos | 2 | 20 | 3.21 |
| Catharine Collar | 3 | 11 | 1.34 |
| Sarah Abdul-wahid | 4 | 21 | 2.56 |
| Nicholas Salim | 5 | 11 | 1.00 |
| Fabry-Asztalos, L. | 6 | 7 | 0.55 |
| Abdul-Wahid, S. | 7 | 7 | 0.55 |