Using Deep Learning for Compound Selectivity Prediction

Author(s): Ruisheng Zhang, Juan Li, Jingjing Lu, Rongjing Hu, Yongna Yuan, Zhili Zhao.

Journal Name: Current Computer-Aided Drug Design

Volume 12 , Issue 1 , 2016

Submit Manuscript
Submit Proposal

Abstract:

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

Keywords: Deep belief networks, compound selectivity, neural network, multitask learning.

Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 12
ISSUE: 1
Year: 2016
Page: [5 - 14]
Pages: 10
DOI: 10.2174/1573409912666160219113250

Article Metrics

PDF: 58
HTML: 4
EPUB: 1
PRC: 1