Semantic Scholar Open Access 2001 650 sitasi

Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis

R. Burbidge M. Trotter B. Buxton S. Holden

Abstrak

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.

Topik & Kata Kunci

Computer Science Medicine

Penulis (4)

R. Burbidge

M. Trotter

B. Buxton

S. Holden

Format Sitasi

APA MLA BibTeX

Burbidge, R., Trotter, M., Buxton, B., Holden, S. (2001). Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis. https://doi.org/10.1016/S0097-8485(01)00094-8

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.1016/S0097-8485(01)00094-8

Informasi Jurnal

Tahun Terbit: 2001
Bahasa: en
Total Sitasi: 650×
Sumber Database: Semantic Scholar
DOI: 10.1016/S0097-8485(01)00094-8
Akses: Open Access ✓