Journal
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
Volume 43, Issue 6, Pages 2048-2056Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ci0340916
Keywords
-
Ask authors/readers for more resources
Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available