Bioactivity classification of anti aids compounds using neural network and support vector machine: a comparison
High Throughput Screening has been used in drug discovery to screen large numbers of potential compounds against a biological target by making it possible to screen tens of thousands to hundreds of thousands of compounds at the early stage of drug design. However, it is impractical to test eve...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7414/1/24p%20RAHAYU%20A.%20HAMID.pdf |
| Summary: | High Throughput Screening has been used in drug discovery to screen large numbers
of potential compounds against a biological target by making it possible to screen
tens of thousands to hundreds of thousands of compounds at the early stage of drug
design. However, it is impractical to test every available compound against every
biological target. Classification is an approach in classifYing the compounds into
active and inactive based on already known actives. In this study, Neural Network
and Support Vector Machines (SVM) are used to classify AIDS data represented as
2D descriptors. Selection of compounds used is based on the most diverse
compounds. The classification models will be tested using different ratios of the data
set to identify whether the size of data would affect the rate of classification. Besides
th~t, the study also analyses the effects of dimensional reduction towards the results
of the two teclmiques. Final results indicate that SVM produces better classification
results for both the original data and the reduced dimension data. |
|---|